AI Copyright Neg - Emory 2024

Case Neg – AI Copyright – ENDI ‘24
Solvency Answers
AT: Solvency---Plan Fails---1NC
Copyright reforms alone fail to protect creators.
Zoë Brown & Akshat Agarwal 23. *Intern at the Information Policy Lab; **JD Candidate at Yale Law School; “Sustaining Journalism, Sustaining Democracy: A Policy Guide on Platforms and the Press.” Institute for Technology, Law and Policy. https://itlp.law.ucla.edu/wp-content/uploads/2023/04/ITLP_PLATFORMS_AND_THE_PRESS.pdf
Conclusion
The crisis of news sustainability continues to be a global problem, and it has become increasingly clear that copyright-centric approaches are not a silver bullet. As a multivariate problem involving economics, technology and politics, solution building requires continuous and dynamic engagement from policymakers, who must use a variety of tools and approaches at their disposal while remaining sensitive to local conditions and realities. It is important to look at how other industries like music have tackled similar problems, and to effectively identify benefits and pitfalls inherent in their solutions.
There is no one-size-fits-all fix to this problem. Copyright, licensing and collective bargaining schemes will likely need to be combined with other policy solutions (such as transparency and antitrust solutions) in order to be effective.
AT: Solvency---Plan Fails---2NC
Licensing can’t guarantee fair deals for publishers.
Zoë Brown & Akshat Agarwal 23. *Intern at the Information Policy Lab; **JD Candidate at Yale Law School; “Sustaining Journalism, Sustaining Democracy: A Policy Guide on Platforms and the Press.” Institute for Technology, Law and Policy. https://itlp.law.ucla.edu/wp-content/uploads/2023/04/ITLP_PLATFORMS_AND_THE_PRESS.pdf
It is becoming increasingly clear that focusing on enhancing news publishers’ copyright protections does not by itself enable them to obtain better deals from digital platforms. Digital platforms remain extremely powerful, enabling them to pursue tactics of attrition in negotiations, like withdrawing or modifying their news products to circumvent the intended outcome of the law. In the absence of additional measures, such as those in France, news publishers may be given take-it-or-leave-it deals, with the underlying threat of diminished reach and viewership as a consequence of not having their content on platforms. By themselves ancillary rights may not be successful in compelling platforms to come to the negotiating table, and they cannot guarantee a fair deal will be struck. Greater focus is needed on the nature of the negotiations themselves, to ensure they are carried out in a fair manner, free from the undue influence that platforms may enjoy by virtue of their deep pockets and scale – millions of users that publishers may depend on them to reach.
The plan can’t solve without broader reforms to limit big tech.
Zoë Brown & Akshat Agarwal 23. *Intern at the Information Policy Lab; **JD Candidate at Yale Law School; “Sustaining Journalism, Sustaining Democracy: A Policy Guide on Platforms and the Press.” Institute for Technology, Law and Policy. https://itlp.law.ucla.edu/wp-content/uploads/2023/04/ITLP_PLATFORMS_AND_THE_PRESS.pdf
France’s experience with the Directive suggests that merely granting new rights to news publishers may prove ineffective without additional interventions that go beyond copyright – in this case strict antitrust enforcement. This requires governments and their agencies to be intentional (and often aggressive) about licensing negotiations, stepping in to address news publishers’ lack of bargaining power relative to powerful platforms, and compelling platforms to negotiate in good faith. However, it remains uncertain whether all member states share the political will of the French government to supervise and direct negotiations. It is worth noting that despite the deadline for national transposition being 7 June 2021, 13 out of 27 countries had not fully transposed the Directive into national law as of 19 May 2022.26 Thus, the effectiveness of additional ancillary rights remains dubious, without additional measures in areas such as antitrust and guidance for licensing processes to supplement them.
AT: Alignment Add-On---2NC
1. Licensing does not align AI. It creates less ethical AI.
Micaela Mantegna 24. Affiliate at the Berkman Klein Center at Harvard University (United States) and the Center for Technology and Society at San Andres University (Argentina). TED Fellow, member of the Chatham House’s Responsible AI Taskforce and the World Economic Forum’s Metaverse Council."ARTificial: Why Copyright Is Not the Right Policy Tool to Deal with Generative AI". Yale Law Journal. 4-1-2024. https://www.yalelawjournal.org/forum/artificial-why-copyright-is-not-the-right-policy-tool-to-deal-with-generative-ai#_ftnref164
6. Licenses Incentivize Data Substitution, Impacting Core AI Ethics Principles
Another consideration is that a decision made in a GAI case will have an impact well beyond it, affecting the whole AI-development ecosystem, as it might create incentives for data substitution. AI models based on ML learn from the training data. If the most adequate data to train a model requires paying a licensing fee, it would incentivize developers to cut costs and substitute that quality data for other sources that might not be as appropriate, but less costly. Doing so would be akin to changing the ingredients of a recipe with inferior substitutes. Inferior data could result in a concept commonly referred to as “garbage in, garbage out,”173 where the quality of an AI model’s output is directly affected by the quality of the input data.
Web-scraping, data collection, and databases are a vital component of the AI ecosystem. Legally, there are different approaches to regulating databases. In the EU there is a “sui generis” right to protect the content of the database, and the structure under copyright, if certain conditions are met.174 In the United States, as explained above, the content of the database will only be protectable if there is enough originality in the criteria, selection, arrangement, or composition of the data.
This difference in regulatory approaches has an enormous geopolitical impact. It influences where AI companies will locate themselves, and which databases they will use to train the systems, as companies will try to reduce the cost of having to license the data.
Strong copyright provisions and a lack of carve-outs for data mining for training purposes could potentially incentivize the use of data substitutions as a way to avoid paying for licensed datasets. This would translate directly to subpar performance from AI models for the reasons explained above, which would likely be more biased and less fair—and consequently, human beings and society will be harmed in the process.
2. Superintelligent AI is impossible.
Bert Gambini 22, News Content Manager, Humanities, Economics, Social Sciences, Social Work, Libraries, University of Buffalo, citing Jobst Landgrebe & Barry Smith, philosophers at University of Buffalo, “New book co-written by UB philosopher claims AI will “never” rule the world,” UB, 8/22/22, https://www.buffalo.edu/news/releases/2022/08/014.html#:~:text=Barry%20Smith%2C%20PhD%2C%20SUNY%20Distinguished,%3A%20Artificial%20Intelligence%20without%20Fear.%E2%80%9D
Elon Musk in 2020 said that artificial intelligence (AI) within five years would surpass human intelligence on its way to becoming “an immortal dictator” over humanity. But a new book co-written by a University at Buffalo philosophy professor argues that won’t happen – not by 2025, not ever!
Barry Smith, PhD, SUNY Distinguished Professor in the Department of Philosophy in UB’s College of Arts and Sciences, and Jobst Landgrebe, PhD, founder of Cognotekt, a German AI company, have co-authored “Why Machines Will Never Rule the World: Artificial Intelligence without Fear.”
Their book presents a powerful argument against the possibility of engineering machines that can surpass human intelligence.
Machine learning and all other working software applications − the proud accomplishments of those involved in AI research − are for Smith and Landgrebe far from anything resembling the capacity of humans. Further, they argue that any incremental progress that’s unfolding in the field of AI research will in practical terms bring it no closer to the full functioning possibility of the human brain.
Smith and Landgrebe offer a critical examination of AI’s unjustifiable projections, such as machines detaching themselves from humanity, self-replicating, and becoming “full ethical agents.” There cannot be a machine will, they say. Every single AI application rests on the intentions of human beings – including intentions to produce random outputs.
This means the Singularity, a point when AI becomes uncontrollable and irreversible (like a Skynet moment from the “Terminator” movie franchise) is not going to occur. Wild claims to the contrary serve only to inflate AI’s potential and distort public understanding of the technology’s nature, possibilities and limits.
Reaching across the borders of several scientific disciplines, Smith and Landgrebe argue that the idea of a general artificial intelligence (AGI) − the ability of computers to emulate and go beyond the general intelligence of humans − rests on fundamental mathematical impossibilities that are analogous in physics to the impossibility of building a perpetual motion machine. AI that would match the general intelligence of humans is impossible because of the mathematical limits on what can be modelled and is “computable.” These limits are accepted by practically everyone working in the field; yet they have thus far failed to appreciate their consequences for what an AI can achieve.
“To overcome these barriers would require a revolution in mathematics that would be of greater significance than the invention of the calculus by Newton and Leibniz more than 350 years ago,” says Smith, one of the world’s most cited contemporary philosophers. “We are not holding our breath.”
Landgrebe points out that, “As can be verified by talking to mathematicians and physicists working at the limits of their respective disciplines, there is nothing even on the horizon which would suggest that a revolution of this sort might one day be achievable. Mathematics cannot fully model the behaviors of complex systems like the human organism,” he says.
AI has many highly impressive success stories, and considerable funding has been dedicated toward advancing its frontier beyond the achievements in narrow, well-defined fields such as text translation and image recognition. Much of the investment to push the technology forward into areas requiring the machine counterpart of general intelligence may, the authors say, be money down the drain.
“The text generator GPT-3 has shown itself capable of producing different sorts of convincing outputs across many divergent fields,” says Smith. “Unfortunately, its users soon recognize that mixed in with these outputs there are also embarrassing errors, so that the convincing outputs themselves began to appear as nothing more than clever parlor tricks.”
AI’s role in sequencing the human genome led to suggestions for how it might help find cures for many human diseases; yet, after 20 years of additional research (in which both Smith and Landgrebe have participated), little has been produced to support optimism of this sort.
“In certain completely rule-determined confined settings, machine learning can be used to create algorithms that outperform humans,” says Smith. “But this does not mean that they can ‘discover’ the rules governing just any activity taking place in an open environment, which is what the human brain achieves every day.”
Technology skeptics do not, of course, have a perfect record. They’ve been wrong in regard to breakthroughs ranging from space flight to nanotechnology. But Smith and Landgrebe say their arguments are based on the mathematical implications of the theory of complex systems. For mathematical reasons, AI cannot mimic the way the human brain functions. In fact, the authors say that it’s impossible to engineer a machine that would rival the cognitive performance of a crow.
“An AGI is impossible,” says Smith. “As our book shows, there can be no general artificial intelligence because it is beyond the boundary of what is even in principle achievable by means of a machine.”
Journalism Advantage Answers
Turn---Democracy---1NC
The plan’s weakening of “fair use” undermines democracy.
Benj Edwards 24. Benj Edwards is an AI and Machine Learning Reporter for Ars Technica with more than 16 years of experience, citing Jeff Jarvis, Director of the Tow-Knight Center for Entrepreneurial Journalism a professor in Journalism Innovation at CUNY. "At Senate AI hearing, news executives fight against “fair use” claims for AI training data". Ars Technica. 1-11-2024. https://arstechnica.com/information-technology/2024/01/at-senate-ai-hearing-news-executives-fight-against-fair-use-claims-for-ai-training-data/
The fair use debate
Overall, the meeting seemed friendly to the news executives on matters of content licensing, with lawmakers largely echoing the executives' concerns and calling for measures that would ensure compensation for the use of journalistic content in AI development.
The sole dissenter was Jarvis, who argued against limiting fair use. "I am concerned about all the talk I hear about limiting fair use around this table because fair use is used every day," said Jarvis. "Fair use is used every day by journalists. We ingest data. We ingest information. And we put it out in a different way." He cited concerns about privacy and freedom being curtailed by limiting fair use. "Newspapers complained about radio over the years for doing rip and read. They tried to stop it. They built more agreements to stop newspapers, to stop radio from being in news at all," he said. "In the end, democracy was better served because journalists could read each other and use each other's information."
Licensing sets a dangerous precedent that limits fair use. That spills over to undermine news media.
Jeff Jarvis 24. Director of the Tow-Knight Center for Entrepreneurial Journalism a professor in Journalism Innovation at CUNY. "In the echo chamber". BuzzMachine. 1-11-2024. https://buzzmachine.com/2024/01/11/in-the-echo-chamber/
It was a privilege and honor to be invited to air my views on technology and the news. I went in knowing I had a role to play, as the odd man out. The other witnesses were lobbyists for the newspaper/magazine and broadcast industries and the CEO of a major magazine company. The staff knew I would present an alternative perspective. My fellow panelists noted before we sat down — nicely — that they disagreed with my written testimony. Job done. There was little opportunity to disagree in the hearing, for one speaks only when spoken to.
What struck me about the experience is not surprising: They call the internet an echo chamber. But, of course, there’s no greater echo chamber than Congress: lobbyists and legislators agreeing with each other about the laws they write and promote together. That’s what I witnessed in the hearing in a few key areas:
Licensing: The industry people and the politicians all took as gospel the idea that AI companies should have to license and pay for every bit of media content they use.
I disagree. I draw the analogy to what happened when radio started. Newspapers tried everything to keep radio out of news. In the end, to this day, radio rips and reads newspapers, taking in and repurposing information. That’s to the benefit of an informed society.
Why shouldn’t AI have the same right? I ask. Some have objected to my metaphor: Yes, I know, AI is a program and the machine doesn’t read or learn or have rights any more than a broadcast tower can listen and speak and vote. I spoke metaphorically, for if I had instead argued that, say, Google or Meta has a right to read and learn, that would have opened up a whole can of PR worms. The point is obvious, though: If AI creators would be required by law to license *everything* they use, that grants them lesser rights than media — including journalists, who, let’s be clear, read, learn from, and repurpose information from each other and from sources every day.
I think there’s a difference in using content to train a model versus producing output. It’s one matter for large language models to be taught the relationship of, say, the words “White” and “House.” I say that is fair and transformative use. But it’s a fair discussion to separate out questions of proper acquisition and terms of use when an application quotes from copyrighted material from behind a paywall in its output. The magazine executive cleverly conflated training and output, saying *any* use required licensing and payment. I believe that sets a dangerous precedent for news media itself.
If licensing and payment is required for all use of all content, then I say the doctrine of fair use could be eviscerated. The senators argued just the opposite, saying that if fair use is expanded, copyright becomes meaningless. We disagree.
AT: Journalism---Status Quo Solves---1NC
Deals between publishers and AI companies solve without requiring licensing.
Natalie Korach & Sharon Knolle 24. Media Reporter for TheWrap; Knolle is an award-winning TV Reporter at TheWrap. has covered entertainment news for more than 20 years. "Litigate or License? News Publishers Battle With AI Over Content". TheWrap. 5-29-2024. https://archive.ph/FoGrO#selection-2121.19-2121.26
Industry financial strain spurs dealmaking
The push by Big Tech to lock up content to feed its chatbots comes at a difficult time for the news media industry. Media companies have struggled with falling digital advertising revenue — which is now dominated by Big Tech companies — and struggled to maintain subscribers, leading to a wave of mass layoffs and other draconian cost-cutting measures. Just Thursday The Washington Post reported a 50% decline in its web traffic, a $77 million loss in 2023 and looming cutbacks.
The intense financial pressure means media companies are tempted by the prospect of incorporating AI in their newsrooms but also conflicted by what it could mean for the value of their content. At the same time, Big Tech companies have told investors that they plan to spend tens of billions of dollars a year to develop AI and are in an all-out race to gain a foothold in what is predicted to be a multi-trillion-dollar industry.
The licensing deals between media organizations and OpenAI so far have been conducted on an individual basis, with each company granting the AI firm varying degrees of access to content. The AP’s partnership, which is valued in the single-digit millions a year in compensation, according to WSJ, is limited to the use of archival content for training LLMs.
News Corp.’s deal, which is much larger and more comprehensive, allows OpenAI to display content from the organization’s portfolio of mastheads in response to ChatGPT questions from users. The intention is to “enhance” OpenAI’s product offerings, “with the ultimate objective of providing people the ability to make informed choices based on reliable information,” the company said in a release. Alongside the licensing agreement, News Corp. will provide “journalistic expertise” to OpenAI in an effort to ensure quality control of the tech company’s journalistic standards.
Experts say that while some media companies are eager to embrace AI technology and welcome revenue from cash-flush tech giants, it is impossible to ignore the similarities to previous cycles of tech development. Over the past decade, news publishers molded their business and editorial strategies around tech initiatives, including chasing search functions and social features, only to see the tech companies reap the real benefits — as with Google — or suddenly t

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