Neg - Generative AI Copyright - MSDI 2024

MSDI 2024 – Generative AI Copyright NEG
Solvency
1NC – Industry Self-Regulation
Government regulation of AI fails, it’s unruly, risks overreach that tanks the industry and lacks expertise, only self-regulation can solve
Hoffman 2023. Adonis Hoffman is CEO of The Advisory Counsel LLC. He served in senior roles at the FCC and in the U.S, House of Representatives and is a former adjunct professor at Georgetown University. “ Why self-regulation is best for artificial intelligence” The Hill November 8, 2023. <https://thehill.com/opinion/4300288-why-self-regulation-is-best-for-artificial-intelligence/> Accessed 7/4/24. ARJSH
As the president and policymakers consider rules to govern AI, they face intractable challenges. Trying to regulate every expression of AI will be an elusive exercise; the technology is far too ingrained already in our lives to even think it possible. Equally important, government regulation, however well-intentioned, will always be several steps behind even in its best iteration. All things considered, self-regulation is the best policy course for AI regulation. Here is why: Industry expertise AI is vast, complex and rife with nuances. Technology experts immersed in its intricacies are best suited to draft and enforce guidelines. Even the most expert federal regulatory agencies, while motivated, are sure to lack the depth of understanding necessary to effectively oversee AI innovations. Those within the AI industry possess the hands-on experience and technical familiarity to establish guidelines that are both practical and effective. Agility and responsiveness The pace of AI advancement is dynamic and staggering. By the time traditional regulatory frameworks catch up with the latest development, several new ones will have emerged. Self-regulation offers a flexibility that can adapt swiftly to the ever-changing AI landscape. This agility ensures that guidelines remain relevant and fosters responsible innovation without stifling it. Cost-effectiveness The bureaucratic nature of government-led regulations often entails prolonged processes and significant financial expenditures. In contrast, self-regulation, led by industry stakeholders, can be more streamlined and cost-effective, reducing the financial burden on both the industry and taxpayers. Preempting stricter oversight In the absence of self-regulation, government might impose even stricter, potentially stifling regulations. By adopting a proactive stance, the AI industry can set standards that are both high and realistic, preempting the need for burdensome regimes. Building trust through transparency One of the critiques of AI is the perceived “black box” nature of algorithms, which can lead to mistrust among regulators and the public. By self-imposing transparency and ethical standards, the AI industry can build credibility and trust. To be fair, there are downsides to self-regulation that augur for government regulation. Proponents of federal regulation argue that the Federal Trade Commission (FTC) and Department of Justice would be more inclined than industry to protect individual rights, monitor and prevent abuses and strictly enforce the rules. With concerns over AI’s influence on privacy, federal regulations can provide a standardized framework to ensure that personal data is not misused. In areas like autonomous vehicles and healthcare AI, regulations can ensure the development and deployment of safe, reliable AI technologies. Without proper oversight, AI systems can inadvertently perpetuate or even amplify biases present in the data they are trained on. Regulation can ensure systems are developed and deployed in a manner that mitigates these biases. Regulations can require companies to make their AI systems more understandable and transparent, which can help in building public trust. By setting rigorous standards, the U.S. might position itself as a producer of high-quality, reliable and trustworthy AI solutions, which could become a selling point on the international stage. Just as the FDA ensures the safety of drugs and the FAA regulates aviation safety, AI regulation can be a way to protect consumers from potentially harmful or misleading AI-driven products and services. Regulations can also drive companies to adopt ethical AI practices, ensuring technology benefits humanity as a whole. But enforcing federal AI regulations can be challenging, given the complexity of AI itself. The risk of governmental overreach is as real as the infringement of individual freedom. As AI companies often operate globally, navigating a patchwork of different regulatory regimes across countries can be complex and costly. We must also be concerned about competition; if the U.S. over-regulates, it may fall behind in the global AI race. In addition, the economic impact of over-regulation might drive AI companies to relocate to more lenient jurisdictions, potentially leading to job losses and a downturn in economic contributions from a rapidly growing sector. Finally, ill-advised federal regulation has direct economic implications for small- and medium-sized businesses. Complying with regulations often comes with costs. While large corporations might easily absorb these, small businesses and startups could struggle, potentially hampering innovation at grassroots levels. To preserve and advance the many benefits of artificial intelligence, U.S. policymakers should encourage the industry to develop a robust and prudent self-regulatory regime that fosters accountability, ethical responsibility, innovation, and security. As AI continues to permeate our everyday lives, there is no doubt that regulation is necessary. But its direction should come from industry, not government, and be guided by a set of industry-derived principles and best practices that are fair, transparent and enforceable.
Extension – Industry Self-Regulation
Gov’t action fails, only self-regulation through the market solves AI issues. Negotiations are fastest and sidestep the need for regulations. The courts can fill gaps where necessary
Sokler et al 2024. Bruce D. Sokler Member / Co-chair, Antitrust Practice, Mintz P.C.; Alexander Hecht is Executive Vice President & Director of Operations of ML Strategies, Washington, DC.; Christian Tamotsu Fjeld is a Vice President of ML Strategies in the firm’s Washington, DC office; Raj Gambhir is a Project Analyst in the firm’s Washington DC office. Mintz Insights. “(Un)fair Use? Copyrighted Works as AI Training Data — AI: The Washington Report” January 10 2024. <https://www.mintz.com/insights-center/viewpoints/54731/2024-01-10-unfair-use-copyrighted-works-ai-training-data-ai> Accessed 6/27/24. ARJSH
US: Efforts in the Three Branches and the Marketplace to Resolve the AI and Copyright Dilemma Executive Branch and Copyright Office Efforts on AI and Copyright Here in the United States, in August 2023, the US Copyright Office published a request for comments on the question of how the office should address governance challenges posed by the emergence of generative AI. Among other issues, the Copyright Office requested comment on “whether legislative or regulatory steps” are needed to address “the use of copyrighted works to train AI models.” The comment period for this inquiry closed on December 6, 2023. The August 2023 inquiry, along with previous efforts by the Copyright Office, are intended to provide the material for future legislative reforms. Policy proposals on AI and copyright made on the basis of the Copyright Office’s inquiries may come as early as summer 2024. President Joe Biden’s October 2023 “Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence” directs the Copyright Office to “issue recommendations to the President on potential executive actions relating to copyright and AI… including the scope of protection for works produced using AI and the treatment of copyrighted works in AI training.” These recommendations are to be issued to the President by the end of July 2024 at the latest. Congressional Proposals on AI and Copyright In parallel with the executive branch and the Copyright Office, lawmakers in Congress are wrestling with the copyright issues posed by AI. On December 22, 2023, Representatives Don Beyer (D-VA-8) and Anna Eshoo (D-CA-16), respectively the Vice Chair and Co-Chair of the Congressional AI Caucus, introduced the AI Foundation Model Transparency Act. Under the act, developers of certain AI models may be required to disclose the sources of data used to train their models, including “information necessary to assist copyright owners or data license holders with enforcing their copyright or data license protections...” As members and committees propose piecemeal AI bills, Congressional leadership has been working towards the goal of comprehensive AI legislation. From September to December 2023, a bipartisan group of senators led by Majority Leader Chuck Schumer (D-NY) hosted a series of senate-wide “AI Insight Forums,” or closed-door meetings with a variety of stakeholders on pertinent governance issues raised by AI. The purpose of these forums is to develop expertise within the Senate needed to produce effective bi-partisan AI legislation within “months.” On November 29, 2023, Senator Schumer hosted a forum on “Transparency, Explainability, Intellectual Property, and Copyright.” In his opening statement for the forum, the senator asserted that “there is a role for Congress to play to promote transparency in AI systems and protect the rights of creators.” Will the Courts Decide the Issue Under Current Law? On December 27, 2023, the New York Times sued leading generative AI companies for “unlawful use of The Times’s work to create artificial intelligence products that compete with” the publication. The complaint alleges that the defendants used “Times content without permission to develop their models and tools.” According to the complaint, after a months-long attempt to reach a negotiated agreement stalled, the publication filed suit. The New York Times argues that the defendants’ use of “The Times’s content without payment to create products that… closely mimic the inputs used to train them” is not protected by fair use and therefore constitutes copyright infringement. Given the high profile of both the plaintiff and defendants, some experts have predicted that this case, if tried, will eventually be appealed to the Supreme Court. Regardless of whether this ends up being the case, the outcome of this suit could have significant implications for not just the news industry, but all creators whose content has been used to train AI models. In the absence of regulation from the executive or legislative branch, a decisive ruling on this or a related case may set industry standards regarding the use of copyrighted works as training data for AI models—or be the catalyst for codifying a set of standards. Self-Regulation as the Solution? Aside from government agencies, Congress, and the courts, the ultimate solution to the issues posed by the use of copyrighted works as AI training data may come from the marketplace. Prior to filing their suit, the New York Times engaged with generative AI companies in months of negotiations over the use of its content. Had these negotiations succeeded, the resulting arrangement could have been used as a model for other publishers and content creators. As the New York Times suit progresses, there is nothing preventing marketplace agreements (including from the litigants). The emergence of AI is moving at warp speed. Regulatory, legislative, and litigation-based solutions all move at a slower pace. In all likelihood, the longer it takes for regulators to create and implement public policy solutions to this issue, the more likely it is that creators and AI developers will come to their own arrangements.
New industry standards are already being developed to protect creators and promote responsible AI development
David 2024. Emilia David, a reporter who covers AI for the Verge. Prior to joining The Verge, she covered the intersection between technology, finance, and the economy. “AI models that don’t violate copyright are getting a new certification label” The verge January 17, 2024, <https://www.theverge.com/2024/1/17/24041518/generative-ai-copyright-violation-fair-training-label-certification> Accessed June s29, 2024.
As much of the potential legislation around AI requires reviews of models, groups have begun offering certification programs to AI companies to show how their work doesn’t violate copyright. Bloomberg reported Fairly Trained — founded by former Stability AI vice president for audio Ed Newton-Rex — adds a label to companies that prove they asked for permission to use copyrighted training data. Newton-Rex started Fairly Trained after he quit Stability AI in November, citing generative AI was “exploiting creators.” Its first accreditation, which it calls the Licensed Model certification, will be awarded to companies that license protected data to train its models. Fairly Trained claims it will not issue the certification to developers that rely on the fair use argument to train models. Fairly Trained said in a blog post that it has already certified nine generative AI companies that work in image, music, and voice generation. These include Beatoven.ai, Boomy, BRIA.ai, Endel, LifeScore, Rightsify, SOMMS.AI, Soundful, and Tuney. Training AI models with copyrighted data has been a fraught issue in generative AI, especially after artists and authors sued several AI companies for copyright infringement. The New York Times also filed a lawsuit against OpenAI and Microsoft for violating its copyright when training its GPT models. Some proposed legislation currently making its way through Congress would require AI companies to disclose where they got their training data. The information will let copyright holders know if their work was used without permission.
1NC Climate Turn
AFF undercuts the environmental impact of AI, and the massive threat to ecosystems it creates. Vote neg to prioritize climate damage that often flies under the radar
Jobin et al 2019. Anna Jobin, Marcello Ienca and Effy Vayena, Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland. The global landscape of AI ethics guidelines Nat Mach Intell 1, 389–399 (2019). https://doi.org/10.1038/s42256-019-0088-2 Accessed 6/27/24 ARJSH
Sustainability, dignity and solidarity are significantly underrepresented compared to other ethical dimensions, which suggests that these issues might be currently flying under the radar of the mainstream AI ethics debate. This underrepresentation is particularly problematic in light of recent evidence that AI requires massive computational resources, which, in turn, require high energy consumption129. The environmental impact of AI, however, involves not only the negative effects of high-footprint digital infrastructures, but also the possibility of harnessing AI for the benefit of ecosystems and the entire biosphere. As the humanitarian cost of anthropogenic climate change is rapidly increasing130, the principles of sustainability and solidarity appear strictly intertwined. In addition, the ethical principle of solidarity is referenced even more rarely, typically in association with the development of inclusive strategies for the prevention of job losses and unfair sharing of burdens. Little attention is devoted to promoting solidarity through the emerging possibility of using AI expertise for solving humanitarian challenges, a mission that is currently being pursued, among others, by companies such as Microsoft131 and intergovernmental organizations such as the United Nations Office for Project Services (UNOPS)132 or the World Health Organization (WHO). A better appraisal of currently underrepresented ethical principles is likely to result in a more inclusive AI ethics landscape.
Climate change is a non-linear, existential risk.
Kemp et al. 22, *Luke Kemp, Centre for the Study of Existential Risk, University of Cambridge; *Chi Xu, School of Life Sciences, Nanjing University; *Joanna Depledge, Cambridge Centre for Environment, Energy and Natural Resource Governance, University of Cambridge; *Timothy Lenton, Global Systems Institute, University of Exeter; (August 1st, 2022, “Climate Endgame: Exploring catastrophic climate change scenarios”, https://www.pnas.org/doi/10.1073/pnas.2108146119#sec-3)
Worst-Case Climate Change
Despite 30 y of efforts and some progress under the United Nations Framework Convention on Climate Change (UNFCCC) anthropogenic greenhouse gas (GHG) emissions continue to increase. Even without considering worst-case climate responses, the current trajectory puts the world on track for a temperature rise between 2.1 °C and 3.9 °C by 2100 (11). If all 2030 nationally determined contributions are fully implemented, warming of 2.4 °C (1.9 °C to 3.0 °C) is expected by 2100. Meeting all long-term pledges and targets could reduce this to 2.1 °C (1.7 °C to 2.6 °C) (12). Even these optimistic assumptions lead to dangerous Earth system trajectories. Temperatures of more than 2 °C above preindustrial values have not been sustained on Earth’s surface since before the Pleistocene Epoch (or more than 2.6 million years ago) (13).
Even if anthropogenic GHG emissions start to decline soon, this does not rule out high future GHG concentrations or extreme climate change, particularly beyond 2100. There are feedbacks in the carbon cycle and potential tipping points that could generate high GHG concentrations (14) that are often missing from models. Examples include Arctic permafrost thawing that releases methane and CO2 (15), carbon loss due to intense droughts and fires in the Amazon (16), and the apparent slowing of dampening feedbacks such as natural carbon sink capacity (17, 18). These are likely to not be proportional to warming, as is sometimes assumed. Instead, abrupt and/or irreversible changes may be triggered at a temperature threshold. Such changes are evident in Earth’s geological record, and their impacts cascaded across the coupled climate–ecological–social system (19). Particularly worrying is a “tipping cascade” in which multiple tipping elements interact in such a way that tipping one threshold increases the likelihood of tipping another (20). Temperature rise is crucially dependent on the overall dynamics of the Earth system, not just the anthropogenic emissions trajectory.
The potential for tipping points and higher concentrations despite lower anthropogenic emissions is evident in existing models. Variability among the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models results in overlap in different scenarios. For example, the top (75th) quartile outcome of the “middle-of-the-road” scenario (Shared Socioeconomic Pathway 3-7.0, or SSP3-7.0) is substantially hotter than the bottom (25th) quartile of the highest emissions (SSP5-8.5) scenario. Regional temperature differences between models can exceed 5 °C to 6 °C, particularly in polar areas where various tipping points can occur (SI Appendix).
There are even more uncertain feedbacks, which, in a very worst case, might amplify to an irreversible transition into a “Hothouse Earth” state (21) (although there may be negative feedbacks that help buffer the Earth system). In particular, poorly understood cloud feedbacks might trigger sudden and irreversible global warming (22). Such effects remain underexplored and largely speculative “unknown unknowns” that are still being discovered. For instance, recent simulations suggest that stratocumulus cloud decks might abruptly be lost at CO2 concentrations that could be approached by the end of the century, causing an additional ∼8 °C global warming (23). Large uncertainties about dangerous surprises are reasons to prioritize rather than neglect them.
Recent findings on equilibrium 

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