Featured Post from Emory University, Goizueta Business School

AI Art: What Should Fair Compensation Look Like?

Jun 28, 2024

5 min

David Schweidel



New research from Goizueta’s David Schweidel looks at questions of compensation to human artists when images based on their work are generated via artificial intelligence.


Artificial intelligence is making art. That is to say, compelling artistic creations based on thousands of years of art production may now be just a few text prompts away. And it’s all thanks to generative AI trained on internet images. You don’t need Picasso’s skillset to create something in his style. You just need an AI-powered image generator like DALL-E 3 (created by OpenAI), Midjourney, or Stable Diffusion.


If you haven’t tried one of these programs yet, you really should (free or beta versions make this a low-risk proposal). For example, you might use your phone to snap a photo of your child’s latest masterpiece from school. Then, you might ask DALL-E to render it in the swirling style of Vincent Van Gogh. A color printout of that might jazz up your refrigerator door for the better.


Intellectual Property in the Age of AI


Now, what if you wanted to sell your AI-generated art on a t-shirt or poster? Or what if you wanted to create a surefire logo for your business? What are the intellectual property (IP) implications at work?


Take the case of a 35-year-old Polish artist named Greg Rutkowski. Rutkowski has reportedly been included in more AI-image prompts than Pablo Picasso, Leonardo da Vinci, or Van Gogh. As a professional digital artist, Rutkowski makes his living creating striking images of dragons and battles in his signature fantasy style. That is, unless they are generated by AI, in which case he doesn’t.


“They say imitation is the sincerest form of flattery. But what about the case of a working artist? What if someone is potentially not receiving payment because people can easily copy his style with generative AI?” That’s the question David Schweidel, Rebecca Cheney McGreevy Endowed Chair and professor of marketing at Goizueta Business School is asking. Flattery won’t pay the bills. “We realized early on that IP is a huge issue when it comes to all forms of generative AI,” Schweidel says. “We have to resolve such issues to unlock AI’s potential.”


Schweidel’s latest working paper is titled “Generative AI and Artists: Consumer Preferences for Style and Fair Compensation.” It is coauthored with professors Jason Bell, Jeff Dotson, and Wen Wang (of University of Oxford, Brigham Young University, and University of Maryland, respectively). In this paper, the four researchers analyze a series of experiments with consumers’ prompts and preferences using Midjourney and Stable Diffusion. The results lead to some practical advice and insights that could benefit artists and AI’s business users alike.


Real Compensation for AI Work?


In their research, to see if compensating artists for AI creations was a viable option, the coauthors wanted to see if three basic conditions were met:


– Are artists’ names frequently used in generative AI prompts?

– Do consumers prefer the results of prompts that cite artists’ names?

– Are consumers willing to pay more for an AI-generated product that was created citing some artists’ names?


Crunching the data, they found the same answer to all three questions: yes.


More specifically, the coauthors turned to a dataset that contains millions of “text-to-image” prompts from Stable Diffusion. In this large dataset, the researchers found that living and deceased artists were frequently mentioned by name. (For the curious, the top three mentioned in this database were: Rutkowski, artgerm [another contemporary artist, born in Hong Kong, residing in Singapore] and Alphonse Mucha [a popular Czech Art Nouveau artist who died in 1939].)


Given that AI users are likely to use artists’ names in their text prompts, the team also conducted experiments to gauge how the results were perceived. Using deep learning models, they found that including an artist’s name in a prompt systematically improves the output’s aesthetic quality and likeability.


The Impact of Artist Compensation on Perceived Worth


Next, the researchers studied consumers’ willingness to pay in various circumstances. The researchers used Midjourney with the following dynamic prompt:


“Create a picture of ⟨subject⟩ in the style of ⟨artist⟩”.


The subjects chosen were the advertising creation known as the Most Interesting Man in the World, the fictional candy tycoon Willy Wonka, and the deceased TV painting instructor Bob Ross (Why not?). The artists cited were Ansel Adams, Frida Kahlo, Alphonse Mucha and Sinichiro Wantabe. The team repeated the experiment with and without artists in various configurations of subjects and styles to find statistically significant patterns. In some, consumers were asked to consider buying t-shirts or wall art. In short, the series of experiments revealed that consumers saw more value in an image when they understood that the artist associated with it would be compensated.



Here’s a sample of imagery AI generated using three subjects names “in the style of Alphonse Mucha.”
Source: Midjourney cited in http://dx.doi.org/10.2139/ssrn.4428509


“I was honestly a bit surprised that people were willing to pay more for a product if they knew the artist would get compensated,” Schweidel explains. “In short, the pay-per-use model really resonates with consumers.” In fact, consumers preferred pay-per-use over a model in which artists received a flat fee in return for being included in AI training data. That is to say, royalties seem like a fairer way to reward the most popular artists in AI. Of course, there’s still much more work to be done to figure out the right amount to pay in each possible case.


What Can We Draw From This?

We’re still in the early days of generative AI, and IP issues abound. Notably, the New York Times announced in December that it is suing OpenAI (the creator of ChatGPT) and Microsoft for copyright infringement. Millions of New York Times articles have been used to train generative AI to inform and improve it.


“The lawsuit by the New York Times could feasibly result in a ruling that these models were built on tainted data. Where would that leave us?” asks Schweidel.


"One thing is clear: we must work to resolve compensation and IP issues. Our research shows that consumers respond positively to fair compensation models. That’s a path for companies to legally leverage these technologies while benefiting creators."


David Schweidel


To adopt generative AI responsibly in the future, businesses should consider three things. First, they should communicate to consumers when artists’ styles are used. Second, they should compensate contributing artists. And third, they should convey these practices to consumers. “And our research indicates that consumers will feel better about that: it’s ethical.”



AI is quickly becoming a topic of regulators, lawmakers and journalists and if you're looking to know more - let us help.


David A. Schweidel, Professor of Marketing, Goizueta Business School at Emory University


To connect with David to arrange an interview - simply click his icon now.

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David Schweidel

David Schweidel

Professor of Marketing & Goizueta Chair in Business Technology

Marketing analytics expert focused on the opportunities at the intersection of marketing and technology

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Attendance shows the enthusiasm of fans in the market to pay for tickets and take the time to travel and attend. Social media following reveals how many fans the team has in and out of their home market. Each metric has advantages and limitations. Social media following provides an indication of national fandom, but it also captures casual fans who would never pay for a ticket. The second aspect of the analysis focuses on market potential. NFL markets vary from New York, with a population of 20 million, to Green Bay, with a few hundred thousand. Income levels in San Francisco are far higher than in Jacksonville or Cleveland. I use a range of demographics, but income and population are the major factors. Again, the metrics are good but not perfect. For example, using MSA populations isn’t perfect because teams have different footprints. The Packers are more of a Wisconsin team than a Green Bay team. The teams in New York and LA share a market. Should they each get half of the metro area population? One factor that I do not control for is competition. In the southeast, NFL teams may compete with SEC teams. I have debated this issue (with myself) and have decided to neglect it. This year's analysis includes a significant change from last year. The significant change is that I am not controlling for team performance. Controlling for team performance is helpful because it isolates core or unchanging fandom. This approach has appeal, as we can argue that teams with more passionate fandoms will be more resilient against losing seasons. The downside of controlling for performance is that we get less of a measurement of the fandom's overall value. If a team like Kansas City is on an extended winning streak, then the Chiefs brand is very valuable at the moment. Controlling for winning makes the analysis more about the core, near-permanent passion of a fandom, while not controlling makes the results more relevant to current brand power. The analysis involves three steps. The first step creates measures of each team’s relative fandom outcomes and market potential. The second step develops a statistical model of the relationship between market potential and fandom outcomes. The third step compares each team's fandom outcomes with the statistical model's predictions. The third step is a comparison of actual results versus predicted – the key point is that the prediction is based on leaguewide data. As these analyses are always imperfect, the best way to consider the fandom rankings is as tiers. I like the idea of quadrants. Some brief comments on the members of each quadrant (Elite, Solid, Role Players, Benchwarmers). I will be discussing each fandom on social media. TikTok: @fanalyticspodcast Instagram: @fanalyticsmikelewis YouTube: @fanalyticsmike A bonus figure follow the Quad overviews. The Results Quadrant 1: The Elite The Dallas Cowboys lead the top group of teams, followed by the Packers, Eagles, Chiefs, 49ers, Raiders, Patriots, and Steelers. Sounds a lot like what the man on the street would list as the top NFL brands. The Cowboys and Packers leading the way is no surprise. The Cowboys are second in social following and the leaders in attendance. The Packers are an astonishing fandom story as the team is located in the definitive small market. The Eagles leading the Steelers is going to be troubling in Western Pennsylvania, but the Eagles have more pricing power and more social following. The 49ers are a solid NFL fandom with few weaknesses. The Patriots are in a new era, and it will be fascinating to see if they maintain their top-tier position as Brady and Belichick become memories. The Chiefs' presence in the top group is a change from past years and is due to the shift away from controlling for performance. The Chiefs have a great fandom, but the team’s success currently pumps them up. The Chiefs are in a brand-building phase as the team continues building its dynasty. The question for the Chiefs is where they end up long-term. I don't fully understand the Raiders' ranking. The Raiders are midrange in attendance and social following but do well because are reported to have the highest prices in the league. I suspect this is more an idiosyncrasy of the Las Vegas market than a reflection of significant passionate fandom. Quadrant 2: Solid Performers The Quadrant 2 teams are the Broncos, Giants, Panthers, Seahawks, Saints, Ravens, Texans, and Browns. These are the solid performers of NFL fandoms (brands). These are teams with above expected fandom outcomes for their relative market potentials. The Quadrant 2 clubs are all passionate fanbases (maybe one exception) despite very different histories. For example, the AFC North rival Ravens and Browns differ in both relative history and frequency of winning. Cleveland fandom involves significant character, while the Ravens are a “blue-collar” brand that has been a consistent winner. There are a lot of great stories in Quad 2. The Saints were once the Aints but are now a core part of New Orleans. The Broncos and Giants are great fandoms who are probably angry to be left out of Quad 1. The Panthers' position is unexpected and may be due to some inflated social media numbers. This is the challenge when an analysis is based only on data. When data gets a little weird, like an inflated social media follower count dating back to Cam Newton's days, the results can also get a little weird. This is a teachable moment—do not analyze and interpret data without knowing the context (the data-generating processes). Quadrant 3: Role Players Quadrant 3 fandoms are teams whose fandom outcomes are slightly below average league performance (for similar markets). The Quadrant 3 teams include (in order) the Bills, Falcons, Buccaneers, Jets, Vikings, Bears, Dolphins, and Bengals. There are some interesting teams in Quad 3. The Bills have a great and notorious fandom. Jumping through flaming tables in subzero weather should get you into the top half of the rankings? The big-market Jets and the small-market Bengals have two of the most fascinating QBs in the league. Both clubs could be poised to get to Quad 2 with a Super Bowl or two. Da’Bears may be one of the most disappointing results. A team with an SNL skit devoted to their fandom in a market like Chicago shouldn’t be in Quad 3. Other quick comments: The Falcons need to win a title. Florida is tough for professional teams. The Vikings should play outside. Quadrant 4: Hopium These are the NFL's weakest fandoms, with the key phrase being “the NFL’s.” The Quad 4 teams, in order, are the Lions, Rams, Jaguars, Colts, Titans, Commanders, Chargers, and Cardinals. It’s a lot of teams who have not won regularly and have many moves and name changes. The Lions are poised for a move upward and maybe a sleeping giant of a fandom. They have the most watchable coach in the league and the most surprising celebrity fan. An interesting side story in Quad 4 is the battle for Los Angeles between the Rams (formerly of Saint Louis) and the Chargers (previously San Diego). They play in the same market, but the Rams have won more. But will Herbert lead the Chargers past the Rams? Quad 4 illustrates an important lesson: consistency. The Rams moved from St. Louis and then back to LA. The Chargers went from San Diego to LA. The Colts left Baltimore in the middle of the night. The Titans were the Oilers and moved from Houston to Nashville. The Cardinals were the other NFL team Saint Louis lost. The Commanders should have stopped with their previous name. The Fandom Outcomes / Market Potential Matrix The following figure is a bit of bonus material that may provide some insight into the inner workings of the analysis. The figure below shows the performance of each team on the Fandom Outcome and the Market Potential Indexes. The upper left region features teams with less lucrative markets but above-average fandoms, like the Packers, Steelers, and Chiefs. The lower right region is the teams with below-average fandom outcomes despite high potential markets, like the Commanders, Chargers, and Rams. This pictorial representation is also interesting as it shows teams with similar positions. These similarities can be somewhat surprising. For example, the Lions and Dolphins have very similar profiles despite the differences between Detroit and Miami. Mike Lewis is an expert in the areas of analytics and marketing. This approach makes Professor Lewis a unique expert on fandom as his work addresses the complete process from success on the field to success at the box office and the campaign trail. Michael is available to speak with media - simply click on his icon now to arrange an interview today. Interested in following Future Fandom! Subscribe for free to receive new posts.

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