Consent and Compensation: Resolving Generative AI’s Copyright Crisis

Pasquale, F., & Sun, H. (2024).

SSRN Electronic Journal.

https://doi.org/10.2139/ssrn.4826695

Abstract

Generative artificial intelligence (AI) has the potential to augment and democratize creativity. However, it is undermining the knowledge ecosystem that now sustains it. Generative AI may unfairly compete with creatives, displacing them in the market. Most AI firms are not compensating creative workers for composing the songs, drawing the images, and writing both the fiction and non-fiction books that their models need in order to function. AI thus threatens not only to undermine the livelihoods of authors, artists, and other creatives, but also to destabilize the very knowledge ecosystem it relies on.

Alarmed by these developments, many copyright owners have objected to the use of their works by AI providers. To recognize and empower their demands to stop non-consensual use of their works, we propose a streamlined opt-out mechanism that would require AI providers to remove objectors’ works from their databases once copyright infringement has been documented. Those who do not object still deserve compensation for the use of their work by AI providers. We thus also propose a levy on AI providers, to be distributed to the copyright owners whose work they use without a license. This scheme is designed to ensure creatives receive a fair share of the economic bounty arising out of their contributions to AI. Together these mechanisms of consent and compensation would result in a new grand bargain between copyright owners and AI firms, designed to ensure both thrive in the long-term.

Here are some thoughts.

This essay discusses the copyright challenges presented by generative artificial intelligence (AI). It argues that AI's ability to create content and replicate existing works threatens the livelihoods of authors and other creatives, destabilizing the knowledge ecosystem that AI relies on. The authors propose a legislative solution involving an opt-out mechanism that would allow copyright owners to remove their works from AI training databases and a levy on AI providers to compensate copyright owners whose work is used without a license.

The essay emphasizes the urgency of addressing the issue, asserting that the free use of copyrighted works by AI providers devalues human creativity and could undermine AI's future development by removing incentives for creating the training data it needs. It highlights the disruption of the knowledge ecosystem caused by the opacity and scale of AI systems, which erodes authors' control over their works. The authors point out that AI firms are unlikely to offer compensation for the use of copyrighted works.

Ultimately, the essay advocates for a new agreement between copyright owners and AI firms, facilitated by the proposed mechanisms of consent and compensation. This would ensure the long-term viability of both AI and the human creative input it depends on. The authors believe that their proposed framework offers a promising legislative solution to the copyright problems created by new technological uses of works.

Generative AI Has a 'Shoplifting' Problem

Kate Knibbs

Wired.com

Originally posted 8 AUG 24

Bill Gross made his name in the tech world in the 1990s, when he came up with a novel way for search engines to make money on advertising. Under his pricing scheme, advertisers would pay when people clicked on their ads. Now, the “pay-per-click” guy has founded a startup called ProRata, which has an audacious, possibly pie-in-the-sky business model: “AI pay-per-use.”

Gross, who is CEO of the Pasadena, California, company, doesn’t mince words about the generative AI industry. “It’s stealing,” he says. “They’re shoplifting and laundering the world’s knowledge to their benefit.”

AI companies often argue that they need vast troves of data to create cutting-edge generative tools and that scraping data from the internet, whether it’s text from websites, video or captions from YouTube, or books pilfered from pirate libraries, is legally allowed. Gross doesn’t buy that argument. “I think it’s bullshit,” he says.

The info is here.

Here are some thoughts:

Bill Gross, founder of ProRata, is revolutionizing the generative AI industry with a novel "AI pay-per-use" business model. Gross criticizes the industry for stealing and laundering knowledge without fair compensation. ProRata aims to address this issue by arranging revenue-sharing deals between AI companies and content creators, ensuring fair payment for used work.

ProRata's approach involves using algorithms to break down AI output into components, identifying sources, and attributing percentages to copyright holders for payment. The company has already secured partnerships with prominent companies like Universal Music Group, Financial Times, and The Atlantic. Additionally, ProRata is launching a subscription chatbot-style search engine in October, which will use exclusively licensed data, setting a new standard for the industry.

The company's model offers a solution to the ongoing copyright lawsuits against AI companies, providing a fair and transparent way to compensate content creators. ProRata's emergence is part of a larger trend, with other startups and nonprofits, like TollBit and Dataset Providers Alliance, also entering the training-data licensing space. Gross plans to license ProRata's attribution and payment technologies to other companies, including major AI players, with the goal of making the system affordable and widely adopted, similar to a Visa or Mastercard fee.

Overall, ProRata's innovative approach addresses the pressing issue of fair compensation in the generative AI industry. With its impressive partnerships and promising technology, ProRata is poised to make a significant impact and potentially transform the industry's practices.

Virtuous Victims

Jordan, Jillian J., and Maryam Kouchaki

Science Advances 7, no. 42 (October 15, 2021).

Abstract

How do people perceive the moral character of victims? We find, across a range of transgressions, that people frequently see victims of wrongdoing as more moral than nonvictims who have behaved identically. Across 17 experiments (total n = 9676), we document this Virtuous Victim effect and explore the mechanisms underlying it. We also find support for the Justice Restoration Hypothesis, which proposes that people see victims as moral because this perception serves to motivate punishment of perpetrators and helping of victims, and people frequently face incentives to enact or encourage these “justice-restorative” actions. Our results validate predictions of this hypothesis and suggest that the Virtuous Victim effect does not merely reflect (i) that victims look good in contrast to perpetrators, (ii) that people are generally inclined to positively evaluate those who have suffered, or (iii) that people hold a genuine belief that victims tend to be people who behave morally.

Discussion

Across 17 experiments (total n = 9676), we have documented and explored the Virtuous Victim effect. We find that victims are frequently seen as more virtuous than nonvictims—not because of their own behavior, but because others have mistreated them. We observe this effect across a range of moral transgressions and find evidence that it is not moderated by the victim’s (white versus black) race or gender. Humans ubiquitously—and perhaps increasingly (1, 2)—encounter narratives about immoral acts and their victims. By demonstrating that these narratives have the power to confer moral status, our results shed new light on the ways that victims are perceived by society.

We have also explored the boundaries of the Virtuous Victim effect and illuminated the mechanisms that underlie it. For example, we find that the Virtuous Victim effect may be especially likely to flow from victim narratives that describe a transgression’s perpetrator and are presented by a third-person narrator (or perhaps, more generally, a narrator who is unlikely to be doubted). We also find that the effect is specific to victims of immorality (i.e., it does not extend to accident victims) and to moral virtue (i.e., it does not extend equally to positive but nonmoral traits). Furthermore, the effect shapes perceptions of moral character but not predictions about moral behavior.

We have also evaluated several potential explanations for the Virtuous Victim effect. Ultimately, our results provide evidence for the Justice Restoration Hypothesis, which proposes that people see victims as virtuous because this perception serves to motivate punishment of perpetrators and helping of victims, and people frequently face incentives to enact or encourage these justice-restorative actions.

The research is here.

Virtuous Victims

Jordan, J., & Kouchaki, M. (2020, April 11).

https://doi.org/10.31234/osf.io/yz8r6

Abstract

Humans ubiquitously encounter narratives about immoral acts and their victims. Here, we demonstrate that these narratives can influence perceptions of victims’ moral character. Specifically, across a wide range of contexts, victims are seen as more moral than non-victims who have behaved identically. Using 13 experiments (total n = 8,358), we explore this Virtuous Victim effect. We show that it is specific to victims of immorality (i.e., it does not extend equally to victims of accidental misfortune) and to moral virtue (i.e., it does not extend equally to positive nonmoral traits). We also show that the Virtuous Victim effect can occur online and in the lab, when subjects have other morally relevant information about the victim, when subjects have a direct opportunity to condemn the perpetrator, and in the context of both third- and first-person victim narratives. Finally, we provide support for the Justice Restoration Hypothesis, which posits that people see victims as moral in order to motivate adaptive justice-restorative action (i.e., punishment of perpetrators and helping of victims). We show that people see victims as having elevated moral character, but do not expect them to behave more morally or less immorally—a pattern that is consistent with the Justice Restoration Hypothesis, but not readily explained by alternative explanations for the Virtuous Victim effect. And we provide both correlational and causal evidence for a key prediction of the Justice Restoration Hypothesis: when people do not perceive incentives to help victims and punish perpetrators, the Virtuous Victim effect disappears.

From the Discussion

Our theory and results negate the hypothesis that people see victims as morally deserving of mistreatment in order to maintain just world beliefs. We suggest that, when exposed to apparent injustice, the default reaction is not to justify what has occurred, but rather to seek to restore justice (by punishing the perpetrator and/or helping the victim)  .It has been proposed that restoring justice is another route through which people can maintain just world beliefs(25, 26). And we have argued it is typically a more adaptive response to wrongdoing, because people frequently face incentives for justice-restorative action.  Our experiments are consistent with the hypothesis that in order to adaptively motivate such action, people see victims as morally good. Future research should investigate whether people also see victims as possessing other traits (e.g., helpless, neediness, or innocence) that might motivate justice-restorative action.

Children’s evaluations of third-party responses to unfairness: Children prefer helping over punishment.

Lee, Y., & Warneken, F. (2020, June 13).
https://doi.org/10.31234/osf.io/x8e7w

Abstract

Third-party punishment of selfish individuals is an important mechanism to intervene against unfairness. However, there is another way in which third parties can intervene. Rather than focusing on the unfair individual, third parties can choose to help those who were treated unfairly by reducing inequality. Such third-party helping as an alternative to third-party punishment has received little attention in studies with children. Across four studies, we examined the evaluations of third-party punishment versus third-party helping in N = 322 5- to 9-year-old children. Study 1, 3 and 4 showed that when asked about the agents directly, children evaluated both helpers and punishers positively, but they preferred helpers over punishers overall. When asked about the type of intervention itself, children preferred helping over punishment, suggesting that their preference for the type of intervention corresponds to how children think about the agents performing these interventions. Study 2 showed that children’s preference for third-party helping is driven by distributive justice concerns and not a mere preference for giving or resource maximization as children consider which type of third-party intervention decreases inequality. Together, this series of studies demonstrate that children between 5 and 9 years of age develop a sophisticated understanding of punishment and helping as two adequate forms of intervention but also display a preference for third-party helping. We discuss how these findings and prior work with adults supports the hypothesis of developmental continuity, showing that a preference for helping over punishment is deeply rooted in ontogeny.

From the Discussion:

The current study contributes to the literature by moving beyond the focus on punishment alone and probing children’s thinking about punishment and helping side by side. Prior developmental research focused on comparing punishers with third parties such as onlookers who choose not to intervene after witnessing a transgression (e.g., Vaish et al., 2016) or givers who reward a transgressor(e.g., Hamlin et al., 2011), which might have led to inflating children’s preference for punishers. Instead, the current study compared punishment with helping, a valid and common form of third-party intervention. Additionally, our study assessed children’s evaluations of punishment intervention per se and revealed a subtle but meaningful difference in understanding punishers vs. punishment, which was especially remarkable in young children. With the use of various measures and comparisons, the current study provided a more comprehensive understanding of the development of third-party punishment in children

Punish the Perpetrator or Compensate the Victim?

Yingjie Liu, Lin Li, Li Zheng, and Xiuyan Guo
Front. Psychol., 28 November 2017

Abstract

Third-party punishment and third-party compensation are primary responses to observed norms violations. Previous studies mostly investigated these behaviors in gain rather than loss context, and few study made direct comparison between these two behaviors. We conducted three experiments to investigate third-party punishment and third-party compensation in the gain and loss context. Participants observed two persons playing Dictator Game to share an amount of gain or loss, and the proposer would propose unfair distribution sometimes. In Study 1A, participants should decide whether they wanted to punish proposer. In Study 1B, participants decided to compensate the recipient or to do nothing. This two experiments explored how gain and loss contexts might affect the willingness to altruistically punish a perpetrator, or to compensate a victim of unfairness. Results suggested that both third-party punishment and compensation were stronger in the loss context. Study 2 directly compare third-party punishment and third-party compensation in the both contexts, by allowing participants choosing between punishment, compensation and keeping. Participants chose compensation more often than punishment in the loss context, and chose more punishments in the gain context. Empathic concern partly explained between-context differences of altruistic compensation and punishment. Our findings provide insights on modulating effect of context on third-party altruistic decisions.

The research is here.