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< | Copyright Protection but Without Punishing the Poor | >
> | Equality in the Algorithmic Age: Adapting Anti-Discrimination Law to Machine Learning Systems | | | | |
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< | The Current Copyright System | >
> | As machine learning models quietly arbitrate who receives a job offer, qualifies for a mortgage, or is targeted by certain educational interventions, the tools that shape social opportunity now rest less in human hands and more in the subtle calculus of predictive analytics. Whereas discriminatory intent once stood at the heart of legal inquiries, the gravest threats to equality today may arise without any deliberate animus at all. Instead, systemic biases embedded in historical data and opaque design choices can yield outcomes that disproportionately harm protected groups. This phenomenon of algorithmic discrimination demands a reckoning with current legal frameworks. While commentators often lament the law’s apparent lag behind technology, an underutilized doctrinal approach—disparate impact liability—offers a promising and, perhaps surprisingly, well-aligned conceptual resource. To address algorithmic discrimination fully, we must move beyond viewing disparate impact as a static mechanism transplanted wholesale from mid-twentieth-century contexts. Instead, we should reconceptualize it as a flexible doctrinal tool capable of engaging complex evidentiary challenges, shifting evidentiary burdens, and rewarding innovative compliance strategies. | | | | |
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< | "To promote the Progress of Science and useful Arts..." [1] This is the purpose of the United States copyright system. Spanning for life plus 70 years after, creators are granted an exclusive monopoly to their works to further this purpose. Through a monopoly grant, the copyright system essentially makes a bargain; legally enforceable exclusion in exchange for allowing the public to consume one's creative work. This idea may seem counterintuitive, as discretionary exclusion is seemingly at odds with public access. To truly understand how this bargain works, we need to parse out what exactly is being excluded. | >
> | Re-Theorizing Disparate Impact in the Algorithmic Context | | | | |
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< | 17 U.S. Code § 106 states that the owner of a copyright has the exclusive rights to reproduce, prepare derivates, distribute, publicly perform, and publicly display their work. [2] Understood broadly, these rights amount to the exclusivity of certain kinds of uses, and these uses were likely targeted because of their monetary potential. Reproduction and distribution are fundamental to commerce. By being able to reproduce a work and then distribute it to others, a person can participate in the marketplace, selling the work many times over. Derivatives, although not quite the same as the original, use just enough of the original's likeness and character to profit from the original's profitability. The statute's inclusion of the word "publicly" in relation to performance and display could be attributed to Congress' understanding that publicly displaying or performing, a musical score for example, could reasonably lead to, if not already predicated on the condition of, the performers being paid. | >
> | Rooted in Title VII of the Civil Rights Act and extended by cases like Griggs v. Duke Power Co. and Texas Department of Housing & Community Affairs v. Inclusive Communities Project, Inc., the disparate impact doctrine reframes discrimination as a structural phenomenon rather than a product of individual ill will. This shift is crucial in the algorithmic setting, where machine learning models may incorporate statistical patterns that correlate protected traits with adverse outcomes. Although these patterns may have no intentional origin, their persistence can be as pernicious as traditional prejudice. By looking directly at outcomes—and placing the burden on deployers of algorithms to justify results that fall disproportionately on certain groups—disparate impact doctrine resonates naturally with the complexity of digital decision-making. | | | | |
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< | Although financial control is an important part of copyright exclusion, attribution is another key element. Creators may understandably feel entitled to having their name associated with their work. After all, creators dedicate time and energy into producing their creative works. The exclusive rights of § 106 are meant to safeguard this association between creators and their works by baring others from uses that are specially reserved for the owner of the copyright. Conversely, creators may not want their work to be associated with certain ideas or concepts. Thus, § 106 forces a person to get a license or the owner's permission, if they want to engage in these kinds of uses. | >
> | Yet simply importing older frameworks into the digital arena is insufficient. Courts and regulators should embrace the doctrine’s latent adaptability. Traditionally, disparate impact claims involve policies—tests, criteria, or eligibility thresholds—that are identifiable and discrete. Algorithms, by contrast, operate as dynamic, evolving systems, updating themselves through iterative learning processes. This volatility challenges traditional legal conceptions of a stable “practice” subject to scrutiny. A more innovative approach to disparate impact can treat algorithmic models as ongoing decision-regimes, requiring regulated entities to periodically audit their models, examine changes in their predictions, and demonstrate ongoing compliance. Rather than a one-time challenge to a static policy, algorithmic disparate impact enforcement should be envisioned as a continuing obligation to monitor and adjust. | | | | |
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< | Problems in the Current Copyright System | >
> | Overcoming Data Complexity and Transparency Challenges
One might argue that machine learning models, with their opaque architectures and proprietary features, present insurmountable evidentiary barriers. Yet courts have long managed complexity and confidentiality through carefully calibrated procedural devices. In credit scoring and standardized testing litigation, for instance, courts have subjected intricate predictive tools to scrutiny under protective orders and through neutral experts. The lessons learned there apply here. Rather than seeing these novel systems as black boxes permanently sealed against judicial inquiry, courts can require algorithmic transparency compatible with trade secret protection, using in camera reviews, differential disclosure regimes, or the appointment of court-supervised data scientists. | | | | |
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< | The current system is off balance, with the bargain between exclusion and public access disproportionately favoring those who benefit from the former. If we accept that the exclusion rights of § 106 are meant to reserve a creative work's financial profitability for the copyright owner, then we must acknowledge that it achieves this goal by putting a paywall behind any meaningful use. Low budget creators who find inspiration in the creative works of others and low-income individuals who just want to indulge in the beauty of another person's creative genius are hurt by this paywall. By tightly gatekeeping a work's profitability, the current system punishes those who do not have enough money to enjoy what is at the heart of a creative work's creativity. | >
> | Critically, these procedural innovations can leverage the “business necessity” or “legitimate justification” prong of disparate impact analysis to incentivize greater algorithmic explainability and debiasing efforts. For example, if a company cannot articulate how its algorithm’s predictive features relate to job performance or creditworthiness—and fails to propose effective debiasing strategies—courts could treat that opacity as evidence that the practice is not justified. Over time, the specter of liability would encourage developers to adopt recognized fairness metrics, perform pre-deployment bias testing, and invest in “explainable AI” techniques. In short, the legal system need not passively accept black-box complexity; it can harness liability rules to foster more interpretable and equitable forms of algorithmic design. | | | | |
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< | Reimagining 17 U.S. Code § 106 | >
> | Regulatory Innovation and Cross-Border Models
While courts can adapt disparate impact doctrine to algorithmic contexts, legislative and regulatory guidance is equally important. Precisely because machine learning systems may change continuously and operate across multiple jurisdictions, a static, litigation-driven approach alone might prove insufficient. Regulatory agencies—such as the Equal Employment Opportunity Commission or the Consumer Financial Protection Bureau—could issue guidelines defining acceptable levels of predictive disparity, provide safe harbors for companies that adopt best-in-class debiasing techniques, and facilitate periodic third-party audits. These administrative interventions can shift the focus from post-hoc liability to proactive compliance, encouraging companies to identify and mitigate risk ex ante. | | | | |
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< | We need a new system that can protect the rights of creators without coming at the expense of people who cannot afford to spend money on a license for meaningful access to their creative works. A more equitable and balanced version of the copyright system would do away with § 106, and in its place provide that owners of a copyright have receive the following: (1) Right to Attribution, (2) Right to Integrity, and (3) Right to Commercial Compensation. | >
> | International comparisons enrich this vision. The European Union’s General Data Protection Regulation and related proposals on artificial intelligence underscore the importance of transparency, algorithmic accountability, and enforceable rights to explanation. While U.S. law has not historically mandated a “right to explanation,” disparate impact litigation—backed by tailored regulations—could de facto produce a similar effect, compelling defendants to justify and, if needed, revise their models. This approach aligns equality law with a broader transnational conversation on algorithmic governance, turning what might seem like insular domestic litigation into part of a global effort to ensure that emerging technologies do not eclipse longstanding commitments to human dignity and equal opportunity. | | | | |
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< | The Right to Attribution and the Right to Integrity | >
> | Conclusion | | | | |
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< | In the current system, attribution rights are one category of rights within the broader set of Moral Rights. Attribution rights secure a creator's right to be identified as the author of the work. It also protects against misattribution. The Right of Integrity, the other category, provides a creator with the right to protect their reputation or protect against any mutilation of their work. These two categories of Moral Rights match up perfectly with what I envision for The Right to Attribution and the Right to Integrity, respectfully. However, Moral Rights apply exclusively to works of visual arts. [3] All other forms of creative works must fabricate the right to attribution and the right to integrity through the exclusion rights of § 106. These creators are left to hope that others will recognize that they are the rightful author of a work given that they have § 106 rights. By gutting the current provisions of this section and expanding Moral Rights to include all types of copyrightable subject matter, we can properly account for the attribution and integrity rights. [4]
Right to Commercial Compensation
As I stated before, financial control is an important part of copyright exclusions, as all the § 106 uses have great potential for profitability. Gutting these rights would allow people to profit from another person's creative efforts. Attribution is simply not enough to incentivize creators to share their works with the public. Many want to protect their financial compensation as well.
Unlike the Right to Attribution and the Right to Integrity, we can find little inspiration from the United States' copyright statute to help us define the Right to Commercial Compensation. Instead, we can look to Creative Commons, which offer a range of free licenses that grant the public permission to use a person's creative work. [5] Creative Commons offers a path for the low budget creator and down-on-his-luck Dan to access creative content for free. A revised § 106 would include these licenses as default options for creators to pick from, but still allowing parties to create their own licenses. My proposed Right to Commercial Compensation would act as a silent provision and entail the following: When at any point commercial use of a copyrighted work leads to great or moderate profitability, the creator receives at least 1% of the profits from that year forward and every year thereafter or until the use stops generating great or moderate profits. Great or moderate profitability will vary based on copyrights subject matter and with economic variances across regions and time. Only commercial entities that have not already worked out a properly compensating license deal with creators will be subject to the Right to Commercial Compensation. This new provision should ensure that creators are fairly compensated for their works by large commercial entities that the original § 106 should have focused on targeting, while the default licenses protect the creative interests of those with little money to spare.
Footnotes
[1] U.S. Const. Art. I, § 8, C8.
[2] 17 U.S. Code § 106 - Exclusive rights in copyrighted works
[3] 17 U.S.C. Section 106(a)
[4] Per 17 U.S. Code § 102, the types of works that can receive copyright protection include the following categories: literary works; musical works; dramatic works; pantomimes and choreographic works; pictorial, graphics, and sculptural works (PGS); motion pictures and other audiovisual works, sound recordings; and architectural works.
[5] Creative Commons is an international nonprofit organization that empowers people to grow and sustain the thriving commons of shared knowledge and culture we need to address the world's most pressing challenges and create a brighter future for all. https://creativecommons.org/ | >
> | Disparate impact was never just about intentional bias; it recognizes that structural inequalities persist even when overt prejudice fades. This is newly urgent in an era of algorithmic decision-making, where discrimination emerges not from open hostility but from subtle data patterns and opaque modeling choices. We should reconceptualize disparate impact doctrine for the digital age, using it to spur technical innovation, procedural creativity, and sustained accountability. By insisting on substantive justifications and encouraging equitable design, this updated approach ensures algorithms remain aligned with core fairness principles. Thus, it reaffirms the promise of American anti-discrimination law: that equality cannot be sacrificed for convenience or buried under complexity. In a world increasingly guided by machine learning, a reimagined disparate impact doctrine can safeguard the quest for a more just and inclusive society. | | | \ No newline at end of file |
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