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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The first was a set of electronic dashboards programmed to record and display the average OR turnover performance for teams on a weekly basis, and segment these into averages unique to individuals working in each of the core roles within any given OR turnover team. The dashboard displayed weekly scores and ranked them from best to worst on large TV monitors with interactive capabilities—users could filter the data for types of surgery and other dimensions. Broadcasting metrics this way afforded Williams and his team a means of identifying and then publicly recognizing top-performing staff, but that’s not all. The dashboards also provided a mechanism with which to filter out team dynamics, and home in on individual efforts. “If you are put in a room with one team, and they are slower than others, then you are going to be penalized. Your efforts will not shine. Now, say you are put in with a bigger or faster team, your day’s numbers are going to be much higher. So, we had to find a way to accommodate and allow for the team effect, to observe individual effort. The dashboards meant we could do this. Over the period of a week or a month, the effect of other people in the team is washed out. You begin to see the key individuals pop up again and again over time, and you can see those who are far above their peers versus those who, for whatever reason, are not so efficient.” Sharing “relative performance” information has been shown to be highly motivating in many settings. The hope was that it would here, too. Three core roles: Who’s who in the Operating Room turnover team? OR turnover teams consist of three roles: circulating nurse, scrub tech, and anesthetist. While other surgery staff might be present during a turnover, depending on the needs of consecutive procedures, these are the three core roles in the team, and they are not interchangeable in any way: each individual assumes the same responsibilities in every team they join. Typically, turnover tasks will include removing instruments and equipment from the previous surgery and setting up for the next: restocking supplies and restoring the sterile environment. Turnover tasks and activities will vary according to the type of procedure coming next, but these tasks are always performed by the same three roles: nurse, scrub tech, and anesthetist, working within their own area of expertise and specialty. OR turnover teams are assembled based on staff schedules and availability, making them highly fluid. Different nurses will work with different scrub techs and different anesthetists depending on who is free and available at any given time. With dashboards on display across the hospital’s surgery department, Williams decided to trial a second motivational mechanism; this time something more tangible. “We decided to offer a simple $40 Dollar Store gift card to each week’s top performing anesthetist, nurse, or scrub technician to see if it would incentivize people even more. And to keep things interesting, and sustain motivation, we made sure that anyone who’d won the contest two weeks in a row would be ineligible to win the gift card the following week,” says Williams. “It was a bit of a shot in the dark, and we didn’t know if it would work.” Altogether, the dashboards remained in situ over a period of about 33 months while the gift card promotion ran for 73 weeks. It was important to stress the foundational importance of safety and then allow individuals to come up with their own ways to tighten procedures. This was a bottom-up, grassroots experience where the people doing the work came up with their own ways to make their times better, without cutting corners, without cutting quality, and without cutting any safety measures. Jason Williams MD 02MR 20MBA Incentives: Make it Something Special and Unique Crunching all of this data, Sedatole and her colleagues could isolate the effect of each mechanism on performance and turnover times at Phoebe. While the dashboards had “negligible” effect on productivity, the addition of the store gift cards had immediate, significant, and sustained impact on individuals’ efforts. Differences in the effectiveness of the two incentives—the relative performance dashboard and the gift cards—are attributable to team fluidity, says Sedatole. “It’s all down to familiarity. Dashboards are effective if you care about your reputation and your standing with peers. And in fluid team settings, where people don’t really know each other, reputation seems to matter less because these individuals may never work together again. They simply care less about rankings because they are effectively strangers.” Tangible rewards, on the other hand, have what Sedatole calls a “hedonic” value: they can feel more special and unique to the recipient, even if they carry relatively little monetary value. Something like a $40 gift card to Target can be more motivating to individuals even than the same amount in cash. There’s something hedonic about a prize that differentiates it from cash—after all, you will just end up spending that $40 on the electricity bill. Asa Griggs Candler Professor of Accounting, Karen Sedatole “A tangible reward is something special because of its hedonic nature and the way that human beings do mental accounting,” says Sedatole. “It occupies a different place in the brain, so we treat it differently.” In fact, analyzing the results, Sedatole and her colleagues find that the introduction of gift cards at Phoebe equates to an average incremental improvement of 6.4% in OR turnover performance; a finding that does not vary over the 73-week timeframe, she adds. To get the same result by employing more staff to build more stable teams, Sedatole calculates that the hospital would have to increase peer familiarity to the 98th percentile: a very significant financial outlay and a lot of excess capacity if those additional team members are not working 100% of the time. These are key findings for healthcare systems and for administrators and decision-makers in any setting or sector where fluid teams are the norm, says Sedatole: from consultancy to software development to airline ground crews. Wherever diverse professionals come together briefly or sporadically to perform tasks and then disperse, individual motivation can be optimized by simple mechanisms—cost-effective tangible rewards—that give team members a fresh opportunity to earn the incentive in different settings on different occasions—a recurring chance to succeed that keeps the incentive systems engaging and effective over time. For healthcare in particular, this is a win-win-win, says Williams. “In the United States we are faced with lower reimbursements and higher costs, so we have to look for areas where we can gain efficiencies and minimize costs. In the healthcare value model, time and costs are denominators, and quality and service are numerators. Any way we can save on costs and improve efficiencies allows us to take care of more patients, and to be able to do that effectively. “We made some incredible improvements here. We went from just average to best in class, right to the frontier of operative efficiency. And there is so much more opportunity out there to pull more levers and reach new levels, which is truly encouraging.” Looking to know more or connect with Asa Griggs Candler Professor of Accounting, Karen Sedatole?  Simply click on her icon now to arrange an interview or time to talk today.

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