future of art essay

Art for our sake: artists cannot be replaced by machines – study

There has been an explosion of interest in ‘creative AI’, but does this mean that artists will be replaced by machines? No, definitely not, says Anne Ploin , Oxford Internet Institute researcher and one of the team behind today’s report on the potential impact of machine learning (ML) on creative work. 

The report, ‘ AI and the Arts: How Machine Learning is Changing Artistic Work ’ , was co-authored with OII researchers Professor Rebecca Eynon and Dr Isis Hjorth as well as Professor Michael A. Osborne from Oxford’s Department of Engineering .

Their study took place in 2019, a high point for AI in art. It was also a time of high interest around the role of AI (Artificial Intelligence) in the future of work, and particularly around the idea that automation could transform non-manual professions, with a previous study by Professor Michael A. Osborne and Dr Carl Benedict Frey predicting that some 30% of jobs could, technically, be replaced in an AI revolution by 2030.

Human agency in the creative process is never going away. Parts of the creative process can be automated in interesting ways using AI...but the creative decision-making which results in artworks cannot be replicated by current AI technology

Mx Ploin says it was clear from their research that machine learning was becoming a tool for artists – but will not replace artists. She maintains, ‘The main message is that human agency in the creative process is never going away. Parts of the creative process can be automated in interesting ways using AI (generating many versions of an image, for example), but the creative decision-making which results in artworks cannot be replicated by current AI technology.’

She adds, ‘Artistic creativity is about making choices [what material to use, what to draw/paint/create, what message to carry across to an audience] and develops in the context in which an artist works. Art can be a response to a political context, to an artist’s background, to the world we inhabit. This cannot be replicated using machine learning, which is just a data-driven tool. You cannot – for now – transfer life experience into data.’

She adds, ‘AI models can extrapolate in unexpected ways, draw attention to an entirely unrecognised factor in a certain style of painting [from having been trained on hundreds of artworks]. But machine learning models aren’t autonomous.

Artistic creativity is about making choices ...and develops in the context in which an artist works...the world we inhabit. This cannot be replicated using machine learning, which is just a data-driven tool

‘They aren’t going to create new artistic movements on their own – those are PR stories. The real changes that we’re seeing are around the new skills that artists develop to ‘hack’ technical tools, such as machine learning, to make art on their own terms, and around the importance of curation in an increasingly data-driven world.’

The research paper uses a case study of the use of current machine learning techniques in artistic work, and investigates the scope of AI-enhanced creativity and whether human/algorithm synergies may help unlock human creative potential. In doing so, the report breaks down the uncertainty surrounding the application of AI in the creative arts into three key questions.

• How does using generative algorithms alter the creative processes and embodied experiences of artists?
• How do artists sense and reflect upon the relationship between human and machine creative intelligence?
• What is the nature of human/algorithmic creative complementarity?

According to Mx Ploin, ‘We interviewed 14 experts who work in the creative arts, including media and fine artists whose work centred around generative ML techniques. We also talked to curators and researchers in this field. This allowed us to develop fuller understanding of the implications of AI – ranging from automation to complementarity – in a domain at the heart of human experience: creativity.’

They found a range of responses to the use of machine learning and AI. New activities required by using ML models involved both continuity with previous creative processes and rupture from past practices. There were major changes around the generative process, the evolving ways ML outputs were conceptualised, and artists’ embodied experiences of their practice.

And, says the researcher, there were similarities between the use of machine learning and previous periods in art history, such as the code-based and computer arts of the 1960s and 1970s. But the use of ML models was a “step change” from past tools, according to many artists.

While the machine learning models could help produce ‘surprising variations of existing images’, practitioners felt the artist remained irreplaceable...in making artworks

But, she maintains, while the machine learning models could help produce ‘surprising variations of existing images’, practitioners felt the artist remained irreplaceable in terms of giving images artistic context and intention – that is, in making artworks.

Ultimately, most agreed that despite the increased affordances of ML technologies, the relationship between artists and their media remained essentially unchanged, as artists ultimately work to address human – rather than technical – questions.

Don’t let it put you off going to art school. We need more artists

The report concludes that human/ML complementarity in the arts is a rich and ongoing process, with contemporary artists continuously exploring and expanding technological capabilities to make artworks . Although ML-based processes raise challenges around skills, a common language, resources, and inclusion, what is clear is that the future of ML arts will belong to those with both technical and artistic skills. There is more to come.

But, says Mx Ploin, ‘Don’t let it put you off going to art school. We need more artists.’

Further information

AI and the Arts: How Machine Learning is Changing Artistic Work . Ploin, A., Eynon, R., Hjorth I. & Osborne, M.A. (2022). Report from the Creative Algorithmic Intelligence Research Project. Oxford Internet Institute, University of Oxford, UK. Download the full report .

This report accounts for the findings of the 'Creative Algorithmic Intelligence: Capabilities and Complementarity' project, which ran between 2019 and 2021 as a collaboration between the University of Oxford's Department of Engineering and Oxford Internet Institute.

The report also showcases a range of artworks from contemporary artists who use AI as part of their practice and who participated in our study: Robbie Barrat , Nicolas Boillot , Sofia Crespo , Jake Elwes , Lauren Lee McCarthy , Sarah Meyohas , Anna Ridler , Helena Sarin , and David Young.

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What Is The Future of Art?

Art has always reflected the human experience, evolving alongside our culture, technology, and societal changes. As we stand on the brink of a new era, the future of art holds great promise and exciting possibilities. This article explores how innovation, technology, and shifting paradigms are shaping the art world and what we can expect in the years to come.

Technology as a Creative Medium

Artists can now use technology as an integral part of their creative expression. Digital art , virtual reality (VR ), and augmented reality (AR) are already pushing the boundaries of traditional art forms. Blending art and technology allows for interactive and participatory art, expanding the audience’s role in the creative process.

Artificial Intelligence (AI) and Machine Learning

AI and machine learning algorithms are becoming powerful tools for artists . They can generate art, assist in the creative process, and even predict artistic trends based on data analysis. AI-generated art challenges the traditional definition of authorship and opens up new avenues for collaboration between humans and machines.

Blockchain and Art Ownership

Blockchain technology is revolutionizing art ownership and provenance . It offers transparent and tamper-proof records of art transactions, reducing fraud and ensuring artists receive fair compensation for their work. This innovation may lead to a more equitable art market, empowering artists and collectors. This is a space that will make a huge leap in future of art.

Art Sustainability in Future of Art

The art world is becoming more environmentally conscious. Artists are exploring sustainable materials and methods to create their works. The art world is also addressing its carbon footprint by adopting eco-conscious practices, such as sustainable exhibition design and transportation methods.

Decentralized and Digital Art Galleries

The concept of art galleries is shifting from physical spaces to digital platforms. Decentralized galleries allow artists to showcase their work globally without the constraints of geography. NFT (Non-Fungible Token) art marketplaces have gained popularity, enabling artists to sell their digital creations directly to collectors.

Art and Social Change

Art has always played a role in reflecting societal issues and sparking change. In the future, artists will continue to address pressing global challenges, such as climate change, social justice, and mental health. Art has the power to engage audiences emotionally and intellectually, making it a catalyst for meaningful conversations and activism.

Inclusivity and Diversity

The art world is becoming increasingly diverse and inclusive. Artists from underrepresented backgrounds are gaining recognition, challenging historical imbalances in the art canon. Museums and galleries are working to diversify their collections and leadership, ensuring that all voices are heard and celebrated.

Art Education in the Digital Age

Art education is evolving to adapt to the digital age. Online courses, virtual workshops, and digital resources are democratizing art education, making it accessible to a global audience. The future artist may receive training through immersive VR experiences, collaborating with mentors and peers worldwide.

Art and Neuroaesthetics

The study of neuroaesthetics is shedding light on how the brain perceives and responds to art. Understanding the neural mechanisms behind art appreciation may inform new artistic approaches and enhance the viewer’s experience.

The Role of Museums and Cultural Institutions

Museums and cultural institutions are reimagining their societal roles and defining many initiatives that contributes to future of art. They are becoming hubs for community engagement, education, and innovation. Virtual exhibitions and digital archives allow access to art and culture on a global scale, transcending physical boundaries.

Art and Wellbeing

Art has a therapeutic impact on mental health and wellbeing. In the future, we can expect to see more integration of art into healthcare settings, from hospitals to mental health facilities. Art therapy and creative interventions will continue to play a crucial role in healing and self-expression.

Conclusion: Future of Art

The future of art is a dynamic landscape where innovation, technology, and evolving societal values converge. Artists push boundaries, challenge conventions, and use their creativity to address pressing global issues. As we embark on this exciting journey into the future of art, one thing is certain: art will continue to be a profound and transformative force in our lives, shaping our understanding of the world and ourselves.

The article contains sponsored affiliate links from Amazon to valuable resources.

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What’s The Future Of Art?

Art, by nature, is a subjective field and frankly, it’s hard to say what does and does not constitute art today. Some people will tell you that a simple TV commercial is art while others would argue that “real” art is something much deeper. My point is, it’s difficult enough to define what it is today, much less speculate what it will be like in the future .

But, you know, the future is kind of our business. So, to catch a glimpse of what artists have in store for us in years to come, we asked a group of industry experts…

What’s The Future Look Like For The Industry?

Prepare to wonder…

Susan Morrow Potje, Owner of the Celebration of Fine Art

Mark Simpson, Founder of  Evrywhere

Nicholas Laborde, CEO of Raconteur Games

Paolo Villacarlos, Art Director at Fanrock

Artists have designed with the knowledge that different viewers in a space will have a different spatial relationship with the media, e.g. distance and line of sight. Artists of the future shall craft work that adapts the artistic experience to the different sociocultural contexts that each viewer brings to the table. Anticipation of the breadth of histories creates a new layer where artists of the future can construct parallel realities to be consumed simultaneously.

There are multiple sides to every story. Content creators could potentially spin them all.”

Jacob Fisher, Lead Artist & Founder at studioSPACE

Justin Kujawa, Founder & Creative Director of Matic

Looking further into the future, society is on track to spend more time in the virtual world than the real one. Herewithin we have the opportunity to reshape the environment we want to live in.”

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About the author.

Nick Hastreiter

I write about the future of business. I approach this by interviewing founders, CEO's, and other game changers to share their vision for the future of their industry.

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In August 2020, “What Do We Know about the Future of Art History? Part 1” appeared as a special essay in caa.reviews . It explored the history of CAA’s roster of PhD dissertations, beginning with its establishment in 1963 and then delving into the changing circumstances that continue to animate its presentation. The article made the case that this list of art history dissertations constitutes more than just a procedural accounting of projects completed. When analyzed as a data set, the dissertations illuminated unexamined patterns that have occurred within the field in the United States and Canada over the past decades. A series of visualizations distilled some of these findings in a concrete format, but with the author’s full awareness of the limits of this data set—and really any data set—in capturing and conveying the complexity of the stories that we tell about our own relationships to our changing field of study.

That article generated a considerable amount of interest, suggesting that art historians across the discipline are eager to learn more about the shifting shape of the field and are not averse to pursuing the language of descriptive statistics as one possible approach. It also inspired this follow-up essay and another forthcoming one, both commissioned as installments to appear in a series in caa.reviews . All three use the CAA dissertation roster as a salient point of departure and are committed to data-inflected practices of analysis. All are similarly poised at a temporal pivot, with the claim that we must look back to move forward and that understanding our past will help us to sketch the possibilities for the future of our field at a time of great uncertainty.

The present essay, part 2, expands the data set back in time from its previous starting point of 2002 to the year 1980, relying upon the considerable efforts of data gathering undertaken by collaborators at the Visual Resources Centre at Penn State University ( PSU ). 1 Their meticulous work has provided a longer and richer view of these dissertations that offers clarity about the expansion of art history as a field of study. As stewards of this data set, the PSU team is deeply committed to making this resource publicly available, updated, and as accurate as possible, with the understanding that this primary source presents many opportunities for continuing research and further inquiry. 2 Additionally, Pepe Karmel has extended the data drawn from the CAA rosters to explore job placements over the past decade. His findings will appear as the third and final essay in this series. Whereas part 1 of this series was written under truly isolated conditions during the tight lockdown that was enacted in the early months of the COVID -19 pandemic in New York, this coauthored sequel has emerged from a lively collaboration, involving art history faculty, librarians, visual resource curators, digital humanities specialists, and graduate and undergraduate students, across several institutions.

The PSU Data Set

Before launching into a discussion of the findings, we must delve into the data itself, under the premise that art historians ought to grapple with questions about data discovery, structure, and tractability as fundamental concerns in the face of new analytical methods that hinge on computation. In 2019–20, a team of graduate assistants and staff of the Visual Resources Centre at Penn State transcribed the data from the CAA roster, including all completed dissertations, from 1980 to 2018. 3 The nature of the roster, which was published first in print and then digitally, required that they manually gather data from The Art Bulletin from 1980 to 2002, using PDF files accessed from JSTOR . Entries dating from 2002 to 2018 were extracted from the caa.reviews platform , also through manual transcription, working entry by entry. By the end of summer 2020, the completed data set, henceforth known as the PSU data set, yielded 6,345 unique entries. 4

As acknowledged in part 1 , the CAA dissertation roster does not constitute a complete record of all of the dissertations that have been written in the field, due to the fact that it relies on self-reported data from institutions in the United States and Canada. In some years, participating institutions did not submit a roster and, in certain cases, they omitted some of their own eligible dissertations. Furthermore, a manual data collection method is always subject to human error despite the measures taken to ensure consistency and accuracy, which we will elaborate in the paragraphs and endnotes below. No method can be truly comprehensive, but the authors of the PSU data set affirm that they collected the data in good faith and followed best practices.

The first article featured a data set that was harvested computationally from caa.reviews and only included entries from 2002 to 2018. 5 By contrast, the PSU data set is much larger, hand-curated, and enhanced. Graduate assistants registered each dissertation as a single entry, according to the standard subfields of the published rosters, with up to three of its associated subject areas. 6 In the PSU data set, the information generally appears without modification, with the major exception that graduate assistants added the associated dissertation abstract to each entry. This endeavor hinged on extensive further research, using the ProQuest database, the official repository for theses and dissertations for the Library of Congress, or drawing from institutional electronic theses and dissertations ( ETD ) databases. 7 The added abstracts provide greater substance about the content of the dissertations and thus present a vast catalog of art historical research. With their standard format and associated metadata, these abstracts have the capacity to reveal the emergence of certain research trends, to highlight institutional preoccupations, or to focus on specific scholarly circles. This is a considerable added value of the PSU data set. As an example, the PSU team is currently engaged in a study on the rise of “visual culture” as an area of inquiry within the history of art that can be queried through the abstracts over the past forty years. A team at the Getty Research Institute is using the abstracts to explore approaches to iconography over the same span of time. 8 The results of their research, attained by applying techniques of text analysis to the abstracts, will appear in future publications.

This research process revealed certain discrepancies between the entries that appeared in the CAA dissertation roster, ProQuest, and the ETD . As a rule, graduate assistants deferred to the more up-to-date ProQuest or ETD record to override or correct any conflicting information published in the corresponding listing in The Art Bulletin or caa.reviews . 9 In particular, many institutions or authors announced completion to CAA in a given year, but the dissertation ended up being filed in another year. By extension, some of the aggregated findings that appear in the following visualizations differ from those in the August 2020 essay , for which the data was not amended aside from basic cleaning and restructuring. 10 As a result, the findings of the current essay and the following visualizations significantly enhance and improve upon the results in part 1.

The Growth of the Field

With its wide coverage of 6,345 dissertations, the PSU data set vividly reveals the pace and scope of art history’s rise as a field over the past forty years. 11 From 1980 through the mid-1990s, around 60 to 100 dissertations were completed each year. A period of marked and steep growth begins in the late 1990s, bringing the tally to around 200 to 250 dissertations completed annually. Since then, this range has been generally sustained, with 276 as the highest point, attained in the year 2013. Looking forward, this trend line is sure to experience more dips and ebbs, especially considering the fact that some graduate programs have elected to suspend admissions for the academic year 2021–22 as a response to the COVID -19 pandemic. These decisions will likely manifest as a visible drop in the number of dissertations completed over the following years. For that reason, we assert the year 2013 as a peak for the number of dissertations completed, and one that is unlikely to be attained again in the near future. Figure 1 (below) shows the overall reporting of dissertations by year.

Another pathway through the CAA dissertation roster concerns diversity in the field of art history. However, the PSU data set cannot answer this question on its own terms as the roster does not provide any personal information about the dissertation authors, apart from first and last name. For that reason, we inferred the gender of the dissertation authors using methods of prediction that data scientists commonly rely upon for large data sets in which gender is not self-reported. This study draws on the gender package written by Lincoln Mullen, and employs the data from the US Social Security Administration baby names records to predict gender based on first name. 12 Needless to say, these methods have some serious limitations and, for that reason, are widely acknowledged as a “blunt tool to study a complex subject.” 13 Fundamentally, they hinge on binary conceptions of gender, while assuming sex assigned at birth to be a stable category. More specifically, the package draws on a data set of 91,230 US names, with attention to the frequency of their use over time. 14 As such, we can attain a high level of confidence in the results for names that have been used commonly in the United States over the past century. Yet, clearly, many art historians do not fall into that category, particularly those of international origin and those with uncommon first names. If we were to remedy these shortcomings, we would have to enhance the current data set with much more nuanced information reported directly from the authors themselves, which would entail a major effort toward new data collection.

Working within these acknowledged constraints, Figure 2 indicates, perhaps to no surprise, that women have completed a large percentage of art history PhDs in the United States and Canada, even in the early years of the 1980s. 15 While the number of male art historians completing dissertations has nearly doubled over the past twenty years, the cadre of newly minted female art history PhDs has tripled in size since 1980. Readers will also note the rise of “undetermined” results, which occurred when the instrument that was used failed to deliver a prediction because the first name was not represented in the database. It is worthwhile to peruse the names that triggered these statistical limitations; for example, Hyewon, Ching, Masumi, Sabine, Zeynep, Fan, and Alla, all names that are uncommon in the Anglo-American context, appeared more than once in the “undetermined” category. 16 Rather than manually ascribing gender for the entries where the gender seemed apparent, they were all left as “undetermined,” with the understanding that this category holds interpretational value on its own terms. In 1987, for instance, the “undetermined” names constituted 2 percent of the overall dissertations. By contrast, they composed 16 percent of dissertations completed in 2014, representing the highest proportion during this time period. So, the rise in “undetermined” names points, at least in part, to the increased diversity of the field, with larger numbers of scholars whose names do not have an Anglo-American origin, some of whom may have been international students studying in the United States and Canada.

Fig. 2 Completed Dissertations by Year and Gender, 1980–2018, Based on the CAA Dissertation Roster and the PSU Data Set (created by Nancy Um; click for larger version )

Cameron Blevins and Lincoln Mullen’s analysis of history dissertations serves as a useful point of comparison for the art history data, although their study extends much further back, to the year 1950, and is based on ProQuest records. 17 Starting from the year 1980, many more history dissertations were completed each year than art history dissertations. Yet, both areas witnessed steep growth in the 1990s. According to Blevins and Mullen’s study, history dissertations peaked in 1996, compared to art history’s later 2013 peak. While the majority of dissertations in history were completed by men in 1980, the ratio of dissertations written by male and female historians is roughly equal today. As Blevins and Mullen described, writing in 2015, “Female historians have achieved something approaching parity with male historians in terms of how many women and men complete dissertations each year.” 18 This comparison highlights significant differences between the composition of the fields of art history and history. A percent stacked chart (Figure 3) demonstrates this difference even more clearly, by presenting the proportion of art history dissertations completed by year broken down by gender. It shows that, in art history, women have completed at least 50 percent of dissertations each year since 1980, and in most years, women completed between 60 and 70 percent of them. While the proportion fluctuates incrementally within this scope, there are no definitive rises or falls in either direction. Again, a comparison with the same breakdown for history illustrates that, by contrast, female historians’ share of completed dissertations rose steadily over this period to reach nearly 50 percent in the past decade. 19 In art history, the scales are tipped markedly toward women and have been for the past forty years.

Fig. 3 Proportion of Completed Dissertations by Year and Gender, 1980–2018, Based on the CAA Dissertation Roster and the PSU Data Set (created by Nancy Um; click for larger version )

One wonders how this gender breakdown might be reflected among the ranks of art history faculty and other professions across North America, a topic which would require further study. 20 As a promising foray in that direction, Evonne Levy and a team of researchers at the University of Toronto carried out the data-driven project Canada: A History of Art History , which focused on faculty appointments. Levy’s team gathered information from annual university “Calendars,” catalogs that colleges and universities issue to provide information about their academic programs and faculty members. The team also conducted intensive research on individual programs, including interviews with faculty and staff. Their findings indicate that in 1940, 100 percent of the full-time tenure-track or tenured art history faculty in Canada were men. That situation shifted with each passing decade. By 2017, only 36.5 percent were male and the majority, 63.5 percent, were female (Figure 4). The most recent proportion of female to male art history faculty in Canada, with its roughly 2:1 ratio, thus aligns with the composition of new art history PhD holders, as indicated in Figure 3. In order to pursue the same question about the art history faculty in the United States, we would have to engage in a serious undertaking to gather data, both quantitative and qualitative, akin to Levy’s endeavor, but at a much larger scale. The project would also have to address questions about institution type and size, relative ranks of the faculty members, and the ever important issues of race, ethnicity, and contingent labor.

Institutions and Advisers

Part 1 of this series explored doctoral institutions and advisers in order to highlight the major sites that have fostered and mentored new generations of art historians over recent years. The expanded PSU data set confirms those earlier observations in quite stark terms. Figure 5 singles out the forty-four universities at which fifty or more dissertations were completed, which constitutes half of the institutions that were included in the dissertation roster from 1980 to the present. Once again, the Institute of Fine Arts, New York University ( IFA / NYU ), appears as the most generative site for doctoral work in the United States, presenting a record of 8.6 percent of the overall dissertations completed, equalling 549 dissertations in total. 21 It is followed by Columbia, City University of New York ( CUNY ), Harvard, and Yale. These five high-volume programs, all located in the northeast, clearly play an outsize role in training doctoral students for the field, with over three hundred dissertations each. Comparatively, the majority of PhD-granting institutions have a record of less than one hundred dissertations completed in the same time period.

Based on the expanded PSU data set, 1,281 distinct faculty members advised the 6,345 dissertations listed during this time period. 22 Figure 6 singles out only those advisers who supervised twenty or more dissertations, a group that includes forty-four people, labeled with the most recent institutional affiliation for the adviser that appears in the PSU data set. As was suggested in the August 2020 article , the late Linda Nochlin remains the most generative adviser of the group. The PSU data set includes sixty-four dissertations that she supervised from 1984 to 2017, when she passed away. By contrast, ProQuest lists sixty-seven students who completed dissertations under Nochlin’s supervision. Yet, neither list can be taken to be definitive, as both include and exclude certain entries. So, the actual number of students that Nochlin supervised exceeds the PSU data set’s total and likely also ProQuest’s total. Even so, both of these figures, underreported as they may be, are considerably higher than those of the other advisers with large cadres of doctoral students on the list, which are all below forty in total. This comparison is further evidence of the quite extraordinary impact of this legendary scholar on the field.

Fig. 6 Primary Advisers of 20 or More Completed Dissertations, 1980–2018, Labeled with the Most Recent Affiliation Listed, Based on the CAA Dissertation Roster and the PSU Data Set (created by Nancy Um; click for larger version )

When viewed in the format of a list and also the dehumanized bar charts featured above, the relationships between people, namely advisers and their advisees, and their connections to institutions tend to be flattened. Alternate formats of visualization, such as the social network diagram, map ties within and across groups. As an example, Figure 7 delves more deeply into some of the relationships suggested in Figure 6, by singling out those sixty-four students who completed dissertations under Nochlin’s mentorship as they appear in the PSU data set. This diagram presents a view of the adviser-advisee relationship as a pinwheel, linking together various students through their common doctoral adviser, indicated in gray. It also highlights the fact that Nochlin advised doctoral students from three different universities, IFA / NYU , CUNY , and Yale, each differentiated by color, over this time period. Therefore, it underlines quite clearly how an important and generative faculty adviser who taught at more than one institution connected a significant cadre of students across three universities over half a century. It is notable, of course, that these three institutions, IFA / NYU , CUNY , and Yale, are all among the high-volume doctoral institutions identified in Figure 5.

Fig. 7 Linda Nochlin and Her Students, Based on the CAA Dissertation Roster, 1980–2018, and the PSU Data Set (created by Nancy Um with visNetwork; click for interactive version, where you can scroll to zoom in or out/click and drag to rearrange the nodes )

This set of relationships is extended in Figure 8, with the inclusion of the doctoral students of two of Nochlin’s students, drawn from the PSU data set. This results in two additional pinwheels, which radiate from Nochlin’s own, in addition to the appearance of a fourth institution, Binghamton University, highlighted in gold. In the cases of these two added advisers, Patricia Mainardi and Tom McDonough, there are also discrepancies between the PSU data set and ProQuest lists, so their student cohorts are only partially represented. 23 Even so, this graphic format suggests the reach of Nochlin’s training, which clearly extended far beyond the three main institutions of her affiliation. More radiating pinwheels could be added to amplify these generational registers with other institutions and programs. Figure 8 also attributes to Nochlin a high “betweenness centrality,” an index that refers to the number of times in which a node, or a person in this case, serves as a bridge between others.

Fig. 8 Linda Nochlin, Her Students, and Some of Her Students’ Students, Based on the CAA Dissertation Roster, 1980–2018, and the PSU Data Set (created by Nancy Um with visNetwork; click for interactive version, where you can scroll to zoom in or out/click and drag to rearrange the nodes )

The interpersonal ties that appear in Figures 7 and 8 constitute the fundamental fabric of art historical discourse as carried out through exchanges that operate on a human level. Yet these ties, which are both intellectual and social, appear in print in uneven ways, often relegated to the interstices of academic writing, such as the acknowledgments in a book’s opening pages or tucked away in our more personalized footnotes. There is only one scholarly format that enshrines these relationships, the Festschrift, which is unique in that it declares implicit social underpinnings as the basis for its table of contents. But clearly there is great interest in preserving these connections and seeking to understand their enduring impact within the academy. For instance, the Dictionary of Art Historians sketches the biographies of major figures in the field, providing their bibliographies and tracing connections to other art historians, both collaborators and students, although it focuses only on Western art. On another level, the crowd-sourced effort The Academic Family Tree maps adviser-advisee relationships across disciplines, thereby capturing the multiplicative character of these cross-generational relationships in a publicly accessible record. 24

Rosters, Repositories, and the Limits of Institutional Memory

While it has been long overlooked, the CAA dissertation roster is a valuable primary source that sheds light on aspects of the history of art history as a discipline. With its chronological expansion and the enhancement of the added abstracts, the new PSU data set will be of continuing value to the field. The PSU team is dedicated to updating this resource as new rosters become available and is exploring mechanisms to address some of the data set’s errors and absences, many of which are based on original reporting problems. The team is also currently in discussion with CAA about ways to ensure the roster’s long-term maintenance in a machine-readable format, along with public access to it. This particular project of data curation and preservation should be acknowledged as a service to the discipline and, in fact, an open alternative to commercial indexing services such as ProQuest, which controls access to vast amounts of data about scholarly production. Indeed, the fundamental premise is that we, as scholars, should make information about our own work publicly available in order to circumvent the need to rely upon those commercial entities that ultimately seek to monetize access to the same body of information. 25 So, there is more at stake here than a mere set of quantitative exercises.

As the researchers at PSU delved into these dissertation entries, several questions emerged that required investigation outside of the limits of the roster. Namely, they sought to track down dissertation advisers who had long left their positions in a given department or had retired or passed away. They also endeavored to locate dissertations that were reported but seemingly unfiled or those authors whose names had changed and were thus difficult to track. So, the work of compiling the data set was not at all mechanical, and in fact it inspired deep reflection about how to capture the past of our programs and, by extension, how we may keep a durable record of the people who have animated them. Levy’s Canadian study also responded to this particular concern. Her team endeavored to record departmental histories by featuring a Department Fact Sheet and a Faculty Info Sheet for almost every program in the country. 26 These valuable fact sheets, composed in close consultation with each department, trace the development of degree programs over time, mark historical milestones, and even record past departmental debates. The faculty info sheets serve as important records, containing lists of all tenured and tenure-track faculty that appeared in the annual university calendars, thus likewise tracking historical changes.

On the other hand, for most institutions in the United States, departmental websites serve as the main repositories for program identities. Their contents are rarely standardized and many are not kept up to date. 27 Moreover, they generally do not preserve historical data. For instance, some departments include the profiles of emeritus faculty members or memorial pages for those who have passed away, but rarely do they include the names of those who departed the program for another institution or the many contingent faculty members who take up a large share of teaching and advising on short-term contracts. As one very useful but quite unique example, the University of Chicago features a robust history of its art history department since 1890 on its website, including a list of former faculty members dating back to the department’s founding. For some, but not all, short biographical sketches and lists of publications were compiled by past students. The site provides a listing of dissertations completed in the department, but only since 2005. 28 By contrast, the list of placements of its past doctoral students extends back to the 1990s. There are, of course, various ways to capture these institutional histories and legacies, although there are no codified standards for how to do so across the discipline or across universities. Fragments of a department’s history are more frequently excavated from the dusty files (or now hard drives) of individual faculty and staff, or simply committed to the memories of its longest-standing members. This essay and its earlier counterpart serve as prompts to inspire a discussion about how (and if) we wish to write these histories and which formats might be best suited to their long-term preservation, as art history barrels forward to the next stage in its development as a discipline.

Finally, we offer these provocations and findings with a certain amount of urgency. Institutions of higher education sit at a crossroads, as we have lived through a full year of COVID -19 and now anticipate the ways the pandemic will continue to affect our lives. We are all sensitive to the momentous changes that are occuring due to increasingly constrained resources and larger shifts within our own institutions. They include the possibility of program closures, the loss of faculty and staff, the continuing casualization of the academic workforce, and diminished support for our students, in addition to a belated reckoning with structural racism, social injustice, and increasing inequality. Humanities departments are vulnerable to such precarities and realignments, even while their scholars are ideally poised to provide much-needed understanding about this changing social landscape and its implications. Institutions will make weighty decisions based on the analysis of quantitative data, such as enrollments, budgets, outcomes, and placements. Art historians need to think deeply about how we can tell the story of our field, communicating through numbers as well as through words and mobilizing the data about our past to envision a promising future for the discipline and its next generations.

Nancy Um Binghamton University

Emily Hagen Penn State University

1 Elizabeth Mansfield, head of art history at Penn State University, spearheaded this project to compile and analyze data from the CAA roster, beginning in late 2019. The authors wish to thank Mansfield for her encouragement and generous support. They also thank Aaron Hyman, caa.reviews Editorial Board member, who provided the spark for this continuing project along with valuable feedback; Julie Nelson Davis, caa.reviews editor-in-chief, for her continuing enthusiasm for studies drawn from the CAA dissertation roster; and Evonne Levy, who kindly consulted about the data from Canada, in addition to Tom McDonough and Pepe Karmel, who both read the final draft and offered thoughtful comments.

2 Catherine D. Adams and Carolyn J. Lucarelli, Art History Dissertations and Abstracts from North American Institutions (University Park, PA: Penn State Libraries Open Publishing, 2021/ongoing), https://doi.org/10.26209/ahd .

3 The Penn State team included Catherine Adams, assistant curator of visual resources; Jennifer Glissman, MA student; Emily Hagen, PhD candidate; Claire Heidenreich, PhD candidate; Carolyn Lucarelli, curator of visual resources; and Kyle Marini, MA student. Adams and Lucarelli were essential collaborators on this project. Additionally, Heather Froehlich, literary informatics librarian, provided valuable guidance, feedback, and training throughout. While this essay was being finalized, caa.reviews published the dissertations that were completed in 2019 and 2020. These recent additions have not yet been incorporated into the PSU data set, and thus were not included in this article, although the PSU data set is currently being updated.

4 The authors of this essay assert that it is the responsibility of scholars to be explicit about how they obtained their data and the choices that they made in processing, cleaning, and analyzing it, following Catherine D’Ignazio and Lauren F. Klein’s directive that all aspects of data-oriented labor and decision-making should be documented and accessible. But, it is also acknowledged that the readers of this essay may be less engaged with these procedures. For this reason, the details of data processing and cleaning are generally relegated to the lengthy endnotes that follow. See D’Ignazio and Klein, Data Feminism (Cambridge, MA: MIT Press, 2020), 173–201.

5 Data set and scripts are available on GitHub: https://github.com/nancyum/caa .

6 The standard fields include: year, author last name, author first name, title, adviser(s), institution, and the CAA subject area categories , currently broken down along the lines of chronology, geographic area, and subject, genre, media, and artistic practice. In the roster, the dissertations are organized by year and further subdivided according to subject area. Within these subject areas, dissertations appear alphabetically by author. Dissertations with more than one subject area categorization appear in the online roster multiple times. The PSU team culled the duplicates from the data set, such that a single dissertation entry may have associated with it more than one subject area category, numbered from one to three, which reflects the order in which they appeared on the roster and is not indicative of any preference or priority. As described in the August 2020 essay, the subject area categories changed considerably according to the expanding interests and shifting approaches to art history across the roster’s sixty-year span (Nancy Um, “What Do We Know about the Future of Art History? Part 1,” caa.reviews , August 18, 2020, https://doi.org/10.3202/caa.reviews.2020.74 ). Each of the four assistants maintained a separate spreadsheet devoted to dissertations completed within a ten-year span, from the 1980s through the 2010s, which were then combined.

7 Institutional repositories for electronic theses and dissertations (ETDs) are maintained by individual institutions and are not searchable via a single database. To find abstracts not recorded by ProQuest, the Penn State team identified entries with missing abstracts, sorted by institution, located the institutional ETD , and manually queried these databases for each dissertation. The source of the abstract text, if not found in ProQuest or not found at all, was recorded in a separate notes field. Of the current 6,345 entries, 256 remain without abstracts, some due to institutional embargo. See Alexandra Gold, “The Great Embargo Debate,” Inside Higher Ed , September 20, 2018, https://www.insidehighered.com/blogs/gradhacker/great-embargo-debate .

8 Colleagues at the Getty Research Institute, Emily Pugh and Tracy Stuber, have also kindly consulted on this project’s next steps and future potential.

9 Modifications were made most commonly in the year and title fields, and sometimes in the author fields, due to inconsistent naming conventions, specifically in regard to the use of initials, middle names, and diacritics. Dissertations that had different titles in the ProQuest/ ETD databases were noted in a separate field.

10 Upon completion of the data entry, the PSU team cleaned the data using OpenRefine. Obvious spelling errors, leading and trailing spaces, corrupted diacritics, and special characters were corrected. Care was taken to identify duplicates and transcription errors, corrected against information recorded by ProQuest. Institution and adviser fields, which were often reported inconsistently, were reconciled to achieve consistency with the conventions of part 1 of this series . Adviser names, including those with hyphenated first names, were standardized in the format “Initial.Initial. Last Name.” Institution names appear in their shortest, most common form; for example, Washington University in St. Louis was reconciled to Washington University, and University of Maryland was reconciled to Maryland. Reconciliation of the institution field reduced the number of unique institutions from 211 to 88 in the PSU data set, which indicates the high level of variation in institutional naming conventions that appeared in the CAA dissertation roster over this period. The data was restructured for analysis, which was carried out using various packages with the R programming language.

11 The PSU data set includes entries that predate 1980, when the roster first began to appear in The Art Bulletin . Those entries, which were inconsistently recorded, were not included in the aggregated results that appear in the following figures, as they would skew the appearance of change over time. For information about the scope and changing shape of the CAA dissertation roster and its move from Art Journal to The Art Bulletin in 1980, see Um, “What Do We Know about the Future of Art History?, Part 1.”

12 Lincoln Mullen, “Gender: Predict Gender from Names Using Historical Data,” R package version 0.5.2, https://github.com/lmullen/gender .

13 Cameron Blevins and Lincoln Mullen, “Jane, John . . . Leslie? A Historical Method for Algorithmic Gender Prediction,” Digital Humanities Quarterly 9, no. 3 (2015), http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html .

14 Birth dates were determined using thirty-four as the average age of dissertation completion.

15 The authors offer their thanks to Esol Cho, a PhD candidate in political science at Binghamton University, who wrote the original scripts and helped to troubleshoot the analysis using the gender package. Cho also ran the same data using <a href=“https://genderize.io/>genderize.io” , another package for predicting gender in large data sets. Genderize.io generated fewer “undetermined” entries per year, but it resulted in a comparable breakdown in the proportion of male and female authors.

16 Names that include a hyphen, such as Anne-Marie, were split so that only the first part was included, in order to achieve a lower rate of “undetermined” findings. The “undetermined” group also includes all of those who use an initial as a first name.

17 “Dissertations about History by Gender, 1950–2012,” graph from Blevins and Mullen, “Jane, John . . . Leslie?” It should be noted that the ProQuest database of doctoral dissertations can be searched and mined on the basis of subject fields, but not by the discipline of record of the author. So, as with the CAA dissertation roster, one cannot gain a full picture of dissertations completed in the field through ProQuest, even though that database has been used to check, rectify, and enhance the entries that appear in the PSU data set.

18 Blevins and Mullen, “Jane, John . . . Leslie?”

19 “Proportion of Dissertations about History by Gender, 1950–2012,” graph from Blevins and Mullen, “Jane, John . . . Leslie?”

20 Pepe Karmel’s forthcoming study, to be published as part 3 of this essay series, will look at the career diversity of those who hold doctoral degrees in art history, including positions in museums, libraries, archives, commercial galleries, and other areas.

21 The preliminary results from the August 2020 essay can be viewed as comparison, including the whole group and only high-volume institutions ; see Um, “What Do We Know about the Future of Art History?, Part 1.”

22 Some dissertations on the roster named more than one adviser; up to three were recorded in the PSU data set for each entry. In each case, the first name listed was taken as the primary adviser.

23 In both of these cases, as well, the filtered lists from the PSU data set and from ProQuest differ, with certain exclusions and errors in each. Neither source provides a definitive list of Mainardi’s or McDonough’s doctoral advisees.

24 The Academic Family Tree project relies on user-generated content and is divided by discipline. See “About the Academic Family Tree,” https://academictree.org/about.php .

25 ProQuest is actively leveraging its database for research purposes. See “ProQuest’s TDM Studio™ Service Transforms Text and Data Mining with Efficiency, Flexibility and Power,” ProQuest News Release, January 24, 2020, https://about.proquest.com/about/news/2020/ProQuests- TDM -Studio-Service-Transforms-Text-and-Data-Mining.html .

26 The linked example Department Fact Sheet and Faculty Info Sheet are for the University of British Columbia. To see additional sheets, consult the program page for each institution at https://arthistoryincanada.ca/faculty/ .

27 CAA’s Graduate Programs in Art History and Graduate Programs in the Visual Arts directories , formerly published annually, may serve as another resource about departmental histories, although as of 2017 they are no longer being updated.

28 An examination of the list of dissertations on the University of Chicago website indicates some of the problems of reporting and record keeping that have been mentioned amply above in regard to the CAA dissertation roster. Although its entries matched up with the PSU data set for certain years, in others there were as many as six entries that were unreported to CAA , and thus did not appear in the PSU data set. Some dates of completion differed from those that were sent to CAA . In 2011 no dissertations were listed in the PSU data set, although the website includes five, one of which appeared in the PSU data set but under a different year.

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Art in an age of artificial intelligence

Artificial intelligence (AI) will affect almost every aspect of our lives and replace many of our jobs. On one view, machines are well suited to take over automated tasks and humans would remain important to creative endeavors. In this essay, I examine this view critically and consider the possibility that AI will play a significant role in a quintessential creative activity, the appreciation and production of visual art. This possibility is likely even though attributes typically important to viewers–the agency of the artist, the uniqueness of the art and its purpose might not be relevant to AI art. Additionally, despite the fact that art at its most powerful communicates abstract ideas and nuanced emotions, I argue that AI need not understand ideas or experience emotions to produce meaningful and evocative art. AI is and will increasingly be a powerful tool for artists. The continuing development of aesthetically sensitive machines will challenge our notions of beauty, creativity, and the nature of art.

Introduction

Artificial intelligence (AI) will permeate our lives. It will profoundly affect healthcare, education, transportation, commerce, politics, finance, security, and warfare ( Ford, 2021 ; Lee and Qiufan, 2021 ). It will also replace many human jobs. On one view, AI is particularly suited to take over routine tasks. If this view is correct, then humans involvement will remain relevant, if not essential, for creative endeavors. In this essay, I examine the potential role of AI in one particularly creative human activity—the appreciation and production of art. AI might not seem well suited for such aesthetic engagement; however, it would be premature to relegate AI to a minor role. In what follows, I survey what it means for humans to appreciate and produce art, what AI seems capable of, and how the two might converge.

Agency and purpose in art

If an average person in the US were asked to name an artistic genius they might mention Michelangelo or Picasso. Having accepted that they are geniuses, the merit of their work is given the benefit of the doubt. A person might be confused by a cubist painting, but might be willing to keep their initial confusion at bay by assuming that Picasso knew what he was doing Art historical narratives value individual agency ( Fineberg, 1995 ). By agency, I mean the choices a person makes, their intentionality, motivations, and the quality of their work. Even though some abstract art might look like it could be made by children, viewers distinguish the two by making inferences about the artists’ intentionality ( Hawley-Dolan and Winner, 2011 ).

Given the importance we give to the individual artist, it is not surprising that most people react negatively to forgeries ( Newman and Bloom, 2012 ). This reaction, even when the object is perceptually indistinguishable from an original, underscores the importance of the original creator in conferring authenticity to art. Authenticity does not refer to the mechanical skills of a painter. Rather it refers to the original conception of the work in the mind of the artist. We value the artist’s imagination and their choices in how to express their ideas. We might appreciate the skill involved in producing a forgery, but ultimately devalue such works as a refined exercise in paint-by-numbers.

Children care about authenticity. They value an original object and are less fond of an identical object if they think it was made by a replicator ( Hood and Bloom, 2008 ). Such observations suggest that the value of an original unique object made by a person rather than a machine is embedded in our developmental psychology. This sensibility persists among adults. Objects are typically imbued with something of the essence of its creator. People experience a connection between the creator and receiver transmitted through the object, which lends authenticity to the object ( Newman et al., 2014 ; Newman, 2019 ).

The value of art made by a person rather than a machine also seems etched in our brains. People care about the effort, skill, and intention that underly actions ( Kruger et al., 2004 ; Snapper et al., 2015 ); features that are more apparent in a human artist than they would be with a machine. In one study, people responded more favorably to identical abstract images if they thought the images were hanging in a gallery than if they were generated by a computer ( Kirk et al., 2009 ). This response was accompanied by greater neural activity in reward areas of the brain, suggesting that the participants experienced more pleasure if they thought the image came from a gallery than if it was produced by a machine. We do not know if such responses that were reported in 2009, will be true in 2029 or 2059. Even now, biases against AI art are mitigated if people anthropomorphize the machine ( Chamberlain et al., 2018 ). As AI art develops, we might be increasingly fascinated by the fact that people can create devices that themselves can create novel images.

Before the European Renaissance, agency was probably not important for how people thought about art ( Shiner, 2001 ). The very notion of art probably did not resemble how we think of artworks when we walk into a museum or a gallery. Even if the agency of an artist did not much matter, purpose did. Religious art conveyed spiritual messages. Indigenous cultures used art in rituals. Forms of a gaunt Christ on the crucifix, sensual carvings at Khajuraho temples, and Kongo sculptures of human forms impaled with nails, served communal purposes. Dissanayake (2008) emphasized the deep roots of ritual in the evolution of art. Purpose in art does not have to be linked to agency. We admire cave paintings at Lascaux or Alta Mira but do not give much thought to specific artists who made them. We continue to speculate about the purpose of these images.

Art is sometimes framed as “art for art’s sake,” as if it has no purpose. According to Benjamin (1936/2018) this doctrine, l’art pour l’art , was a reaction to art’s secularization. The attenuation of communal ritualistic functions along with the ease of art’s reproduction brought on a crisis. “Pure” art denied any social function and reveled in its purity.

Some of functions of art shifted from a communal purpose to individual intent. The Sistine Chapel, while promoting a Christian narrative, was also a product of Michelangelo’s mind. Modern and contemporary art bewilder many because the message of the art is often opaque. One needs to be educated about the point of a urinal on a pedestal or a picture of soup cans to have a glimmer as to why anybody considers these objects as important works of art. In these examples, intent of the artist is foregrounded while communal purpose recedes and for most viewers is hard to decipher. Even though 20th Century art often represented social movements, we emphasize the individual as the author of their message. Guernica, and its antiwar message, is attributed to an individual, even when embedded in a social context. We might ask, what was Basquiat saying about identity? How did Kahlo convey pain and death? How did depression affect Rothko’s art?

Would AI art have a purpose? As I will recount later, AI at the very least could be a powerful tool for an artist, perhaps analogous to the way a sophisticated camera is a tool for a fine art photographer. In that case, a human artist still dictates the purpose of the art. For a person using AI art generating programs, their own cultural context, their education, and personal histories influence their choices and modifications the initial “drafts” of images produced by the generator. If AI develops sentience, then questions about the purpose of AI art and its cultural context, if such work is even produced, will come to the fore and challenge our engagement with such art.

Reproduction and access

I mentioned the importance of authenticity in how a child reacts to reproductions and our distaste for forgeries. These observations point to a special status for original artwork. For Benjamin (1936/2018) the original had a unique presence in time and place. He regarded this presence as the artwork’s “aura.” The aura of art depreciates with reproduction.

Reproduction has been an issue in art for a long time. Wood cuts and lithographs (of course the printing press for literature) meant that art could be reproduced and many copies distributed. These copies made art more accessible. Photography and film, vastly increased reproductions of and access to art images.

Even before reproductions, paintings as portable objects within a frame, increased access to art. These objects could be moved to different locations, unlike frescoes or mosaics which had to be experienced in situ (setting aside the removal of artifacts from sites of origin to imperial collections). Paintings that could be transported in a frame already diminished their aura by being untethered to a specific location of origin.

Concerns about reproduction take on a different force in the digital realm. These concerns extend those raised by photographic reproduction. Analog photography retains the ghost of an original- in the form of a negative. Fine art photography often limits prints to a specific number to impart a semblance of originality and introduce scarcity to the physical artifact of a print. Digital photography has no negative. A RAW file might be close. Copies of the digital file, short of being corrupted, are indistinguishable from an original file, calling into question any uniqueness contained in that original. Perhaps non-fungible tokens could be used to establish an original unique identifier for such digital files.

If technology pushes art toward new horizons and commercial opportunities push advances in technology, then it is hard to ignore the likelihood that virtual reality (VR) and augmented reality (AR) will have an impact on our engagement with art. The ease of mass production and commercial imperatives to make more, also renders the notion of the aura of an individual object or specific location in VR nonsensical. AI art, by virtue of being digital, will lack uniqueness and not have the same aura as a specific object tied to a specific time and place. However, the images will be novel. Novelty, as I describe later, is an important feature of creativity.

Artificial intelligence in our lives

As I mentioned at the outset of this essay, machine learning and AI will have a profound effect on almost every aspect of what we do and how we live. Intelligence in current forms of AI is not like human cognition. AI as implemented in deep learning algorithms are not taught rules to guide the processing of their inputs. Their learning takes different forms. They can be supervised, reinforced, or unsupervised. For supervised learning, they are fed massive amounts of labeled data as input and then given feedback about how well their outputs match the desired label. In this way networks are trained to maximize an “objective function,” which typically targets the correct answer. For example, a network might be trained to recognize “dog” and learn to identify dogs despite the fact that dogs vary widely in color, size, and bodily configurations. After being trained on many examples of images that have been labeled a priori as dog, the network identifies images of dogs it has never encountered before. The distinctions between supervised, reinforcement learning, and unsupervised learning are not important to the argument here. Reinforcement learning relies on many trial-and-error iterations and learns to succeed from the errors it makes, especially in the context of games. Unsupervised learning learns by identifying patterns in data and making predictions based on past patterns in that are not labeled.

Artificial intelligence improves with more data. With massive information increasingly available from web searches, commercial purchases, internet posts, texts, official records, all resting on enormous cloud computing platforms, the power of AI is growing and will continue to do so for the foreseeable future. The limits to AI are availability of data and of computational power.

Artificial intelligence does some tasks better than humans. It processes massive amounts of information, generates many simulations, and identifies patterns that would be impossible for humans to appreciate. For example, in biology, AI recently solved the complex problem of three-dimensional protein folding from a two-dimensional code ( Callaway, 2022 ). The output of deep learning algorithms can seem magical ( Rich, 2022 ). Given that they are produced by complex multidimensional equations, their results resist easy explanation.

Current forms of AI have limits. They do not possess common sense. They are not adept at analytical reasoning, extracting abstract concepts, understanding metaphors, experiencing emotions, or making inferences ( Marcus and Davis, 2019 ). Given these limits, how could AI appreciate or produce art? If art communicates abstract and symbolic ideas or expresses nuanced emotions, then an intelligence that cannot abstract ideas or feel emotions would seem ill-equipped to appreciate or produce art. If we care about agency, short of developing sentience, AI has no agency. If we care about purpose, the purpose of an AI system is determined by its objective function. This objective, as of now, is put in place by human designers and the person making use of AI as a tool. If we care about uniqueness, the easy reproducibility of digital outputs depreciates any “aura” to which AI art might aspire.

Despite these reasons to be skeptical, it might be premature to dismiss a significant role of AI in art.

Art appreciation and production

What happens when people appreciate art? Art, when most powerful, can transform a viewer, evoke deep emotions, and promote new understanding of the world and of themselves. Historically, scientists working in empirical aesthetics have asked participants in their studies whether they like a work of art, find it interesting, or beautiful ( Chatterjee and Cardilo, 2021 ). The vast repository of images, on platforms like Instagram, Facebook, Flicker, and Pinterest, have images labeled with people’s preferences. These rich stores of data, growing every day, mean that AI programs can be trained to identify underlying patterns in images that people like.

Crowd-sourcing beauty or preference risks produce boring images. In the 1990s, Komar and Melamid (1999) conducted a pre-digital satirical project in crowd-sourcing art preferences. They hired polling companies to find out what paintings people in 11 countries wanted the most. For Americans, they found that 44% of Americans preferred blue; 49% preferred outdoor scenes featuring lakes, rivers, or oceans; more than 60% liked large paintings; 51% preferred wild, rather than domestic, animals; and 56% said they wanted historical figures featured in the painting. Based on this information, the painting most Americans want showed an idyllic landscape featuring a lake, two frolicking deer, a group of three clothed strollers, and George Washington standing upright in the foreground. For many critics, The Most Wanted Paintings were banal. They were the kind of anodyne images you might find in a motel. Is the Komar and Melamid experiment a cautionary tale for AI?

Artificial intelligence would not be polling people the way that Komar and Melamid did. With a large database of images, including paintings from various collections, the training phase would encompass an aggregate of many more images than collecting the opinions of a few hundred people. AI need not be confined to producing banal images reduced to a low common denominator. Labels for images in databases might end up being far richer than the simple “likes” on Instagram and other social media platforms. Imagine a nuanced taxonomy of words that describe different kinds of art and their potential impacts on viewers. At a small scale, such projects are underway ( Menninghaus et al., 2019 ; Christensen et al., 2022 ; Fekete et al., 2022 ). These research programs go beyond asking people if they like an image, or find it beautiful or interesting. In one such project, we queried a philosopher, a psychologist, a theologian, and art historian and a neuroscientist for verbal labels that could describe a work of art and labels that would indicate potential impacts on how they thought or felt. Descriptions of art could include terms like “colorful” or “dynamic” or refer to the content of art such as portraits or landscapes or to specific art historical movements like Baroque or post-impressionist. Terms describing the impact of art certainly include basic terms such as “like” and “interest,” but also terms like “provoke,” or “challenge,” or “elevate,” or “disgust.” The motivation behind such projects is that powerful art evokes nuanced emotions beyond just liking or disliking the work. Art can be difficult and challenging, and such art might make some viewers feel anxious and others feel more curious. Researchers in empirical aesthetics are increasing focused on identifying a catalog of cognitive and emotional impacts of art. Over the next few years, a large database of art images labeled with a wide range of descriptors and impacts could serve as a training set for an art appreciating AI. Since such networks are adept at extracting patterns in vast amounts of data, one could imagine a trained network describing a novel image it is shown as “playing children in a sunny beach that evokes joy and is reminiscent of childhood summers.” The point is that AI need not know what it is looking at or experience emotions. All it needs to be able to do is label a novel image with descriptions and impacts- a more complex version of labeling an image as a brown dog even if it has never seen that particular dog before.

Can AI, in its current form, be creative? One view is that AI is and will continue to be good at automated but not creative tasks. As AI disrupts work and replaces jobs that involve routine procedures, the hope is that creative jobs will be spared. This hope is probably not warranted.

Sequence transduction or transformer models are making strides in processing natural language. Self-GPT-3 (generative pre-trained transformers) as of now building on 45 terabytes of data can produce text based on the likelihood of words co-occurring in sequence. The words produced by transformer models can seem indistinguishable from sentences produced by humans. GPT-3 transformers can produce poetry, philosophical musings, and even self-critical essays ( Thunström, 2022 ).

The ability to use text to display images is the first step in producing artistic images. DALL-E 2, Imagen, Midjourney, and DreamStudio are gaining popularity as art generators that make images when fed words ( Kim, 2022 ). To give readers, who might not be familiar with the range of AI art images, a sense of these pictures I offer some examples.

The first set of images were made using Midjourney. I started with the prompt “a still life with fruit, flowers, a vase, dead game, a candle, and a skull in a Renaissance style” ( Figure 1 ). The program generates four options, from which I picked the one that came closest to how I imagined the image. I then generated another four variations from the one I picked and chose the one I liked best. The upscaled version of the figure is included.

Midjourney image generated to the prompt “a still life with fruit, flowers, a vase, dead game, a candle, and a skull in a Renaissance style”.

To show variations of the kind of images produced, I used the same procedures and prompts, except changing the style to Expressionist, Pop-art, and Minimalist ( Figures 2 – 4 ).

Midjourney image generated to the prompt “a still life with fruit, flowers, a vase, dead game, a candle, and a skull in an Expressionist style”.

Midjourney image generated to the prompt “a still life with fruit, flowers, a vase, dead game, a candle, and a skull in a Minimalist style”.

Midjourney image generated to the prompt “a still life with fruit, flowers, a vase, dead game, a candle, and a skull in a Pop-art style”.

“To show how one might build up an image I used Open AI’s program Dall-E, to generate an image to the prompt, “a Surreal Impressionist Landscape.” Then using the same program, I used the prompt, “a Surreal Impressionist Landscape that evokes the feeling of awe.” To demonstrate how different programs can produce different images to the same prompt,” a Surreal Impressionist Landscape that evokes the feeling of awe” I include images produced by Dream Studio and by Midjourney.

Regardless of the merits of each individual image, they only took a few minutes to make. Such images and many other produced easily could serve as drafts for an artist to consider the different ways they might wish to depict their ideas or give form to their intuitions ( Figures 5 – 8 ). The idea that artists use technology to guide their art is not new. For example, Hockney (2001) described ways that Renaissance masters used technology of their time to create their work.

Dall-E generated image to the prompt “a Surreal Impressionist Landscape”.

Midjourney generated image to the prompt “a Surreal Impressionist Landscape that evokes the feeling of awe”.

Dall-E generated image to the prompt “a Surreal Impressionist Landscape that evokes the feeling of awe”.

Dream Studio generated image to the prompt “a Surreal Impressionist Landscape that evokes the feeling of awe”.

Unlike the imperative for an autonomous vehicle to avoid mistakes when it needs to recognize a child playing in the street, art makes no such demands. Rather, art is often intentionally ambiguous. Ambiguity can fuel an artworks’ power, forcing viewers to ponder what it might mean. What then will be the role of the human artist? Most theories of creative processing include divergent and convergent thinking ( Cortes et al., 2019 ). Divergent thinking includes coming up with many possibilities. This phase can also be thought of as the generative or imaginative phase. A commonly used laboratory test is the Alternative Uses Test ( Cortes et al., 2019 ). This test asks people to offer as many uses of a common object, like a brick, that they can imagine. The more uses, that a person can conjure up, especially when they are unusual, is taken as a measure of divergent thinking and creative potential. When confronting a problem that needs a creative solution, generating many possibilities doesn’t mean that they are the right or the best one. An evaluative phase is needed to narrow the possibilities, to converge on a solution, and to identify a useful path forward. In producing a work of art, artists presumably shift back and forth between divergent and convergent processes as they keep working toward their final work.

An artist could use text-to-image platforms as a tool ( Kim, 2022 ). They could type in their intent and then evaluate the possible images generated, as I show in the figures. They might tweak their text several times. The examples of images included here using similar verbal prompts show how the text can be translated into images differently. Artists could choose which of the images generated they like and modify them. The divergent and generative parts of creative output could be powerfully enhanced by using AI, while the artist would evaluate these outputs. AI would be a powerful addition to their creative tool-kit.

Some art historians might object that art cannot be adequately appreciated outside its historical and cultural context. For example, Picasso and Matisse are better understood in relation to Cezanne. The American abstract expressionists are better understood as expressing an individualistic spirit while still addressed universal experiences; a movement to counter Soviet social realism and its collective ethos. We can begin to see how this important objection might be dealt with using AI. “Creative adversarial networks” can produce novel artworks by learning about historic art styles and then intentionally deviating from them ( Elgammal et al., 2017 ). These adversarial networks would use other artistic styles as a contextual springboard from which to generate images.

Artificial intelligence and human artists might be partners ( Mazzone and Elgammal, 2019 ), rather than one serving as a tool for the other. For example, in 2015 Mike Tyka created large-scale artworks using Iterative DeepDream and co-founded the Artists and Machine Intelligence program at Google. Using DeepDream and GANs he produced a series “Portraits of Imaginary People,” which was shown at ARS Electronica in Linz, Christie’s in New York and at the New Museum in Karuizawa (Japan) ( Interalia Magazine, 2018 ). The painter Pindar van Arman teaches robots to paint and believes they augment his own creativity. Other artists are increasingly using VR as an enriched and immersive experience ( Romano, 2022 ).

Kinsella (2018) Christie’s in New York sold an artwork called Portrait of Edmond de Belamy for $432,500. The portrait of an aristocratic man with blurry features was created by a GAN from a collective called Obvious. It was created using the WikiArt dataset that includes fifteen thousand portraits from the fourteenth to the twentieth century. Defining art has always been difficult. Art does not easily follow traditional defining criteria of having sufficient and necessary features to be regarded as a member of a specific category, and may not be a natural kind ( Chatterjee, 2014 ). One prominent account of art is an institutional view of art ( Dickie, 1969 ). If our social institutions agree that an object is art, then it is. Being auctioned and sold by Christie’s certainly qualifies as an institution claiming that AI art is in fact art.

In 2017, Turkish artist Refik Anadol, collaborating with Mike Tyka, created an installation using GANs called “Archive Dreaming.” This installation is an immersive experience with viewers standing in a cylindrical room. He used Istanbul’s SALT Galeta online library with 1.7 million images, all digitized into two terabytes of data. The holdings in this library relate to Turkey from the 19th Century to the present and include photographs, images, maps, and letters. Viewers stand in a cylindrical room and can gaze at changing displays on the walls. They can choose which documents to view, or the passively watch the display in an idle state. In the idle state, the archive “dreams.” Generators produce new images that resemble the original ones, but never actually existed—an alternate fictional historical archive of Turkey imagined by the machine ( Pearson, 2022 ).

Concerns, further future, and sentient artificial intelligence

Technology can be misused. One downside of deep learning is that biases embedded in training data sets can be reified. Systematic biases in the judicial system, in hiring practices, in procuring loans are written into AI “predictions” while giving the illusion of objectivity. The images produced by Dall-E so far perpetuate race and gender stereotypes ( Taylor, 2022 ). People probably do not vary much if asked to identify a dog, but they certainly do in identifying great art. Male European masters might continue to be lauded over women or under-represented minority artists and others of whom we have not yet heard.

On the other hand, current gatekeepers of art, whether at high-end galleries, museums, and biennales, are already biased in who and what art they promote. Over time, art through AI might become more democratized. Museums and galleries across the world are digitizing their collections. The art market in the 21st Century extends beyond Europe and the United States. Important shows as part of art’s globalization occur beyond Venice, Basel, and Miami—to now include major gatherings in Sao Paulo, Dakar, Istanbul, Sharjah, Singapore, and Shanghai. Beyond high profile displays, small galleries are digitizing and advertising their holdings. As more images are incorporated into training databases, including art from Asia, Africa, and South America, and non-traditional art forms, such as street art or textile art, what people begin to regard as good or great art might become more encompassing and inclusive.

Could art become a popularity contest? As museums struggle to keep a public engaged, they might use AI to predict which kinds of art would draw in most viewers. Such a use of AI might narrow the range of art that are displayed. Similarly, some artists might choose to make art (in the traditional way), but shift their output to what AI predicts will sell. Over time, art could lose its innovation, its subversive nature, and its sheer variety. The nature of the artist might also change if the skills involved in making art change. An artist collaborating with AI might use machine learning outputs for the divergent phase of their creations and insert themselves along with additional AI assessments in the convergent evaluative phases of producing art.

The need for artistic services could diminish. Artists who work as illustrators for books, technical manuals, and other media such as advertisement, could be replaced by AI generating images. The loss of such paying jobs might make it harder for some artists to pursue their fine art dreams if they do not have a reliable source of income.

Many experts working in the field believe that AI will develop sentience. Exactly how is up for debate. Some believe that sentience can emerge from deep learning architectures given enough data and computational power. Others think that combining deep learning and classical programming, which includes the insertion of rules and symbols, is needed for sentience to emerge. Experts also vary in when they think sentience will emerge in computers. According to Ford (2021) , some think it could be in a decade and others in over a 100 years. Nobody can anticipate the nature of that sentience. When Gary Kasparov (world Chess Champion at the time) lost to the program Deep Blue, he claimed that he felt an alien intelligence ( Lincoln, 2018 ). Deep Blue was no sentient AI.

Artificial intelligence sentience will truly be an alien intelligence. We have no idea how or whether sentient AI will engage in art. If they do, we have no idea what would motivate them and what purpose their art would have. Any comments about these possibilities are pure speculation on my part.

Sentient AI could make art in the real world. Currently, robots find and move objects in large warehouses. Their movements are coarse and carried out in well-controlled areas. A robot like Rosey, the housekeeper in the Jetsons cartoon, is far more difficult to make since it has to move in an open world and react to unpredictable contingencies. Large movements are easier to program than fine movements, precision grips, and manual dexterity. The difficulty in making a robot artist would fall somewhere between a robot in an Amazon warehouse and Rosey. It would not have to contend with an unconstrained environment in its “studio.” It would learn to choose and grip different brushes and other instruments, manipulate paints, and apply them to a canvas that it stretched. Robot arms that draw portraits have been programed into machines ( Arman, 2022 ). However, sentient AI with intent would decide what to paint and it would be able to assess whether its output matched its goal- using generative adversarial systems. The art appreciation and art production abilities could be self-contained within a closed loop without involving people.

Sentient AI might not bother with making art in the real world. Marc Zuckerberg would have us spend as much time as possible in a virtual metaverse. Sentient AI could create art residing in fantastical digital realms and not bother with messy materials and real-world implementation. Should sentient AI or sentient AIs choose to make art for whatever their purpose might be, humans might be irrelevant to the art making and appreciating or evaluating loop.

Ultimately, we do not know if sentient AI will be benevolent, malevolent, or apathetic when it comes to human concerns. We don’t know if sentient AI will care about art.

As AI continues to insinuate itself in most parts of our lives, it will do so with art ( Agüera y Arcas, 2017 ; Miller, 2019 ). The beginnings of art appreciation and production that we see now, and the examples provided in the figures, might be like the video game Pong that was popular when I was in high school. Pong is a far cry from the rich immersive quality of games like Minecraft in the same way that Dall-E and Midjourney images might be a far cry from a future art making and appreciating machine.

The idea that creative pursuits are an unassailable bastion of humanity is untenable. AI is already being used as a powerful tool and even as a partner for some artists. The ongoing development of aesthetically sensitive machines will challenge our views of beauty and creativity and perhaps our understanding of the nature of art.

Author contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Acknowledgments

I appreciate the helpful feedback I received from Alex Christensen, Kohinoor Darda, Jonathan Fineberg, Judith Schaechter, and Clifford Workman.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Future of Arts & Culture

A study based on 300 participants, leaders in museums, galleries, performing arts, music, funding organizations, curators and producers, from North America, South America, Europe, Asia, Middle East, Africa.

What is the future of arts and culture?

Future of arts and culture is a qualitative probe into the future of arts and culture globally..

It examines emerging forces, drivers of change and the possible impacts that may reshape the cultural sector in the coming decade. The project was created as a collaboration among futurists, sector experts and stakeholders.

• arts and culture is increasingly profit-driven, commercially measured, and focused on experiences
• arts and culture take a more active role in supporting social innovation, inclusion and diversity.
• arts and culture shifts to many independent digital platforms.

The overarching theme was one of pragmatism and openness to “rigorous, smart adaptation,” finding ways to walk a line between embracing new ways of working, creating and sharing through innovation, while taking a front-stage role in expanding diversity and social connectedness.

Audiences will continue to move online, regardless of the short-term fluctuations of COVID. “The Metaverse” is coming, but it must be inclusive and not just open to those with the best technology, networks, and investment.

There is a recognition that historic models of funding and support are not suitable for the future, and that new models are possible, and already emerging. These include new ownership models, new economic tools, and even taxation models.

Arts and culture need to rethink its conception of “publics” as well as its role as a public good that can actively seed new futures.

Ranking of Root Issues by Frequency of First Selection

Visual representation of the root issues highlighted by the frequency with which they were selected first by respondents.