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< | AI Scraping: From Free “Knowledge” to Growing Ignorance | >
> | First Essay (Updated Draft) | | | | |
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< | The issue of data scraping is familiar to courts, evolving from manual collection to today’s AI-driven automation. As firms train AI models with significant amounts of data, the legal landscape struggles to define its boundaries, a dilemma regulators still face. | >
> | Outline | | | | |
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< | In Paul Tremblay et al. v. OpenAI? , Inc. et al., No. 3:23-cv-03223 (N.D. Cal. Feb. 12, 2024) (Dkt. 104), the Court granted in part and denied in part OpenAI? ’s motion to dismiss. Plaintiffs’ vicarious copyright infringement claims were dismissed, focusing on whether there is “substantial similarity” between original works and AI-generated outputs. This high bar for establishing similarity renders copyright infringement cases against AI models unlikely to succeed. The latter might appear beneficial. Copyright as a form of private property restricts access to knowledge, keeping it in the hands of the privileged few while others are left ignorant. In this regard, AI and scraping might rise as a liberating and empowering tool, unlocking knowledge for anyone by bypassing traditional ownership. Yet, is that always the case? | >
> | How can Workers Regain Freedom in an AI Era?
- Introduction
- Amargi
- Jubilee
- American Society
- AI era
- Problem - AI and Job Displacement
- Solutions
- Expanding Unions Protections
- Rethinking Careers & American “Amargi”
- Preserving Human Work-Product and Thought: Role of Libraries
- Conclusion
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< | From Free Knowledge to AI’s Knowledge | >
> | How can Workers Regain Freedom in an AI Era? | | | | |
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< | Costly Surveillance – Privatization | >
> | With AI rising, a Harris Poll survey commissioned by the American Staffing Association reported that 28% of Americans worry about job losses, particularly among Gen Z workers. 40% of workers’ career choices and freedom are shaped by financial concerns driven by student loans and mortgages. Since ancient times, debt bondage has dictated human behavior and restricted “freedom.” In Mesopotamia, a practice of debt amnesty, in Sumerian “amargi”––meaning “freedom from debt” or literally “return to mother”––allowed indentured servants to return to their families. Similarly, the Biblical term “jubilee” accounted for periodic forgiveness of debts to restore balance within society. These ancient concepts can be seen in today’s American society, where debt is pervasive for many people, from students to workers. The modern “debt bondage” will likely worsen in the era of AI, which would increase profitability and likely deepen the structural inequities with job losses and lower wages. In that era, how can workers attain freedom from debt? | | | | |
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< | Proponents may argue that AI democratizes learning, sharing the information it scrapes freely with those who can’t afford access to educational opportunities. Nevertheless, this so-called freedom comes with hidden costs—mass data collection driven by economies of “scale” (large quantities of data) and “scope” (any data types). A notable surveillance case with Clearview AI, which scraped pictures from millions of websites to train facial recognition models sold to law enforcement. Are we headed to a dystopian 2084 Orwell’s future? | >
> | AI and Job Displacement | | | | |
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< | As Zuboff warns, in a world of surveillance capitalism, companies harvest our data to steer our behavior, transforming AI into an engine of profitability rather than enlightenment. As a result, AI is increasingly becoming privatized. How could it still promote free learning for all when it isn’t open to everyone’s input? Or when powerful entities control the output? For instance, in early October 2024, OpenAI? , previously governed by a charitable nonprofit, closed a $6.6 billion funding haul with investment from Microsoft Corporation and Nvidia. Like Anthropic and Elon Musk’s xAI, already registered as for-profit corporations, OpenAI? will soon become a public-benefit corporation. Goodbye, OpenAI? . Hello ClosedAI? . | >
> | AI can replace human labor as it presents appealing opportunities for cost reduction to increase company profitability. The world is already witnessing this, from chatbots like ChatGPT? quickly supplanting customer service representatives to healthcare data entry. Even creative fields are not immune to the loss of human touch –– algorithms can now generate art (see Dataland), music, and even students’ AI-papers. | | | | |
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> | Proponents of AI argue it “will create at least 12 million more jobs than it destroys”; it can liberate workers trapped in repetitive jobs. Industries such as manufacturing, transportation, and retail place workers vulnerable to automation, requiring them to upskill and reskill. However, many of these jobs, even if routine, contribute to the dignity of today’s workers, which AI could take away instead of “liberating” workers. Perhaps that might be prevented by making Kant’s “dignity” an assumption of the machine… With the technology advancing rapidly, AI could potentially replace aspects of more complex jobs. However, such displacement might also not fully materialize, given AI’s “amorality, faux science and linguistic incompetence”, overgenerating with a mix of truths and falsehoods and undergenerating (noncommitment and “indifference to consequences”). | | | | |
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< | Bias Perpetuation | >
> | Considering potential AI’s impacts, workers might entirely lose some jobs or at least parts of their jobs, but their debt will remain and still constrain their freedom. Therefore, some solutions for restoring the freedom of workers along their path to debt repayment could include (1) increasing worker wages as they might upskill to leverage AI, (2) expanding union protections, (3) rethinking career paths in an AI economy & American “Amargi,” and (4) ensuring the superior quality of human work-product. | | | | |
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> | Some solutions appear inapplicable. While higher wages could be equitable, they will likely reduce corporate profitability, increasing company resistance. | | | | |
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< | Additionally, the AI tool designed to combat ignorance could instead deepen it, misinterpreting our data, misinforming us, and insidiously shaping our thoughts. Indeed, AI is built by humans and institutions marked by entrenched discrimination, and it is fed with data that may be unrepresentative of marginalized groups, such as people of color or women. As a result, bias may incorporate itself into the model’s outcomes, self-perpetuating. | | | | |
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> | Expanding Unions Protections | | | | |
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< | Data Scraping to Data Creation | | | | |
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> | Unions and regulators must be better positioned to protect workers from AI. The current administration tried not to push aggressively for AI regulations that would attract the ire of companies while still being vocal in its support for the workforce. | | | | |
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< | AI is not limited to scraping data. It may create new layers of information, crafting a distorted reflection of who we are, and positioning itself as the enemy of human autonomy. Most state comprehensive privacy laws exclude publicly available personal information from their scope. Yet, the contours of “publicly available” data are ever-shifting. The implications are far worse when data scraping moves beyond collection to infer new insights, such as people’s voting tendencies. What once was public becomes something entirely new—data created from data with unseen consequences. | >
> | In the federal government’s approach to strengthening union protections against AI, executive orders have been crucial. In 2023, President Biden issued a pro-union executive order regarding AI, instructing federal agencies to include labor unions in the decision-making. Interagency agreements are another critical component of the government’s initiatives, aiming to improve training, investigations, and information sharing. Additionally, the government relied on the National Labor Relations Board (NLRB), an agency seeking to regulate anti-union AI, such as automated management tools and electronic surveillance. | | | | |
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> | Even without federal or state-level regulation, some unions self-regulate with AI commissions and, similar to corporate entities, publish their own AI guidelines. Some also leverage collective bargaining to address AI, either incorporating new protections into contracts or changing the current language. For instance, after the expiration of labor contracts with Hollywood studios, the Writers Guild of America engaged in a five-month strike, prioritizing AI concerns. To expand AI protections, other unions could be encouraged to adopt such private initiatives. While Biden’s administration has recognized such efforts, there is a need for a more comprehensive regulatory framework driven by both public institutions and unions, which can match the technological acceleration. Unions’ initiatives can foster accountability, trust, and transparency, but at the same time, filling the federal void with actionable steps would harmonize agency requirements and enhance companies’ compliance. As AI grows rapidly, policymakers and unions should collaborate to find a balance between labor rights and AI innovations. | | | | |
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< | The question may not be whether to leverage AI to expand knowledge access but how to utilize it wisely. That is, how do we foster access to accurate information without being spied on, knowledge that enriches us without molding us into “bot-like” beings? | >
> | Rethinking Careers & American “Amargi” | | | | |
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< | A “Wise” Use of AI’s Knowledge – “Ethically” Collected Data? | >
> | Debt-free education, like for Columbia medical students, and debt forgiveness mechanisms, such as Biden’s partial cancellation or the PSLF, would give back some freedom to students. With that in mind, students might spend more time developing an entrepreneurial spirit to plan new practices disregarding AI concerns. They might embrace careers involving creativity levels that AI could never achieve. Additionally, choosing professions involving significant social or emotional aspects would reduce automation. For instance, teachers, professors, or therapists express empathy and develop close mentorship, which AI cannot replace. | | | | |
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< | Some may argue that a “wise” use of AI may be to train the model on “ethically” collected data––data where sources have “consented” to its use. However, this is lacking in substance. For instance, take the EU’s GDPR-compliant “consent” by a data subject to the processing of their data (from social media accounts): A controller will find it challenging in practice to identify the specific data owners whose data will be scraped to obtain their so-called “consent.” Thus, “ethically” collected data may only be a pretext for AI to still influence how we perceive and interact with the world, shaping us in its image. Consider how art museums, such as L.A.’s Dataland, will start featuring AI-generated art by claiming reliance on data collected “ethically.” These AI works raise crucial questions: Is Art still serving as a mode of human learning and expression? Or is it becoming an instrument for AI to shape our understanding of Art—and, by extension, of ourselves? No matter how “ethically” (ridiculously) generated, this would transform Art into a novel tool that leads humans to conform to AI-driven frameworks, which may be destructive of the critical, open-ended learning and thinking that Art usually pursues. | >
> | Preserving Human Work-Product and Thought: Role of Libraries | | | | |
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< | A Collaborative Learning System For a “Right” Use | >
> | Online readers leave a trail exploited by corporations for advertising and algorithmic training like Amazon and Google. Zuboff contends that software companies shape human behavior with data surveillance and erode our autonomy. Software has transformed human thought: While “machine reasoning is beyond human subjective experience and outside human understanding […], we already accept the veracity of most of their outputs." | | | | |
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> | Thus, restoring traditional libraries not only safeguards privacy but is essential to maintaining human thought and incentivizing better legal research and reliance on “scientific methods.” Studies have shown people retain better information while reading on paper than online. This would enhance students’ education and strengthen their output once in the workforce. Libraries are vital to improving intellectual autonomy without reliance on AI’s output and maintaining the quality of human work-product. Higher quality output might drive corporations’ profitability better than the cost reduction AI’s lower output could provide, potentially steering them away from job displacement and from "switching off" human thinking. | | | | |
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< | The responsibility lies with lawyers to teach the public the “right” use of AI, guiding us toward solutions that transcend the confines of surveillance, impracticable “consent” theory, and restrictive intellectual property. As Zuboff insightfully observes, “Privacy is not private.” Creativity, too, is not exclusive. Inspiration can spring from all corners of society. Therefore, lawyers must collaborate with professionals across various fields to establish a system that empowers humanity, not steels from it. A collaborative system might leverage a non-privatized “Open” AI with publicly-produced information as a free learning tool. Unlike projects of “SuperIntelligence,” it should not take away our freedom to think but maintain our dignity, free will, and “right to a future tense” (The Age of Surveillance Capitalism, Chapter 2 §6). For Zuboff, this right has long been lost, as our behavior is predicted by Google, making us unable to shape our future. Similarly, AI shouldn’t predict our future, especially if privatized; it cannot decide how we learn and thrive as humans. Whose futures are we talking about? Certainly not the ones filling their pockets. | | | | |
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> | Conclusion | | | | |
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< | AI, watching us, empty and cold, | >
> | We “confront a choice—between the comfort of [independent humans] and the possibilities of an entirely new partnership between human and machine.” Perhaps the choice might be a partnership between independent humans and machines. | | | | |
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< | Deprived of a mind, controlled,
Offering our data for perhaps flawed insights
Slowly, we transition from human to bot-like.
Let AI be the product.
I think the best route to improvement begins with a more disciplined analysis of this draft's unspoken assumptions. Neither as a matter of copyright law nor of social policy am I prohibited from reading as much of what is available on the public net as I want, or of memorizing as much of it as I can hold, or of using all the words I learned in new combinations to make new sentences, tunes or pictures. Anything I can do by myself I am equally allowed to do with a computer. If it would not be copyright infringement done by me, it still isn't done by software.
So we need a clear definition of the problem you are seeking to solve. You begin with one lower court case giving an obvious conclusion from these simple premises, which leads you to a large speculative list of concerns about "AI," none of which seem to be tightly coupled to the underlying propositions. If we can be clearer about the subject we can make better progress, surely.
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