When Machines Act Independently the Law Must Evolve
Navigating Law and Justice in a World of AI-Driven Decision-Making
This report examines the evolving intersection of artificial intelligence (AI) and the legal system. Drawing on recent AI court cases cases, it explores the challenges AI poses to intellectual property, algorithmic bias, privacy, and the ethical governance of technology.
Revenant Research systematically tracks legal cases involving AI as one of the core pillars, along with infrastructure and applications, to understanding the advancements of AI.
Introduction: The Legal Challenges of AI—A Global Reckoning
The integration of artificial intelligence (AI) into critical domains has exposed foundational gaps in the legal system’s ability to address non-human agency. Unlike human actors, AI systems function autonomously, generating decisions, outputs, and consequences that challenge long-standing legal principles rooted in intent and responsibility. As these systems become more sophisticated, they strain traditional frameworks designed to govern human behavior, requiring a shift in how accountability, fairness, and justice are conceptualized. This tension invites a deeper examination of how legal systems can evolve to confront the realities of algorithmic agency and its implications for governance, ethics, and societal norms.
This thought piece explores pivotal areas where AI intersects with law, focusing on intellectual property, algorithmic bias, privacy, and ethical governance. Through real-world cases and broader analysis, it provides a framework for addressing the unique challenges posed by systems whose actions blur the boundaries between human intent and machine autonomy.
I. Intellectual Property and Fair Use in AI Training
The Core Legal Tensions
The lawsuits RIAA et al. v. Suno and Udio and Authors Guild v. Anthropic underscore the growing tension between intellectual property rights and technological innovation. In both cases, plaintiffs argued that generative AI developers unlawfully used copyrighted materials to train their models without obtaining licenses. The defendants countered that their use of copyrighted works fell under the doctrine of fair use.
Fair use, as codified in 17 U.S.C. § 107, allows limited use of copyrighted material without permission for purposes like commentary, criticism, and education. It hinges on four factors: the purpose and character of the use, the nature of the copyrighted work, the amount and substantiality of the material used, and the impact on the market for the original work. These factors were central to landmark decisions such as Campbell v. Acuff-Rose Music, Inc. (1994) and Google LLC v. Oracle America, Inc. (2021), which emphasized the transformative nature of certain uses.
However, generative AI presents new challenges. While courts have previously ruled on transformative works like parodies and functional adaptations, AI models ingest vast quantities of data to generate entirely new outputs. Defendants argue that this process is transformative because it enables innovation, but plaintiffs contend that it amounts to wholesale duplication without compensation.
The Stakes for Artists and Developers
The outcomes of these cases have far-reaching implications for creators and AI developers alike. For creators, the use of copyrighted materials in training datasets raises questions about the value of their intellectual property and their ability to control its use. For AI developers, restrictions on training data could stifle innovation and limit the capabilities of their models.
The market effects of generative AI are particularly contentious. Plaintiffs argue that AI-generated outputs compete directly with original works, reducing demand for human creators. For example, a trained AI model might generate images or text that closely resemble existing copyrighted materials, potentially undermining creators’ economic interests. Defendants, on the other hand, assert that generative AI expands creative possibilities and opens new markets.
Global Perspectives on Copyright and AI
The legal landscape is further complicated by divergent global standards. In the European Union, the Copyright Directive imposes stricter requirements for text and data mining, mandating explicit permissions unless specific exemptions apply. By contrast, the United States’ fair use doctrine provides broader flexibility, enabling developers to experiment more freely.
These differences create challenges for multinational companies, which must navigate conflicting legal regimes. They also highlight the need for greater international cooperation to establish consistent standards for AI governance.
Toward a Balanced Approach
Resolving these tensions requires a balanced approach that respects creators’ rights while fostering innovation. Policy solutions might include:
Statutory Licensing: A framework where AI developers pay royalties to creators for the use of their works in training datasets.
Open Access Datasets: Encouraging the development of publicly available datasets that are free from copyright restrictions.
Dynamic Fair Use Guidelines: Tailored standards that account for the unique characteristics of AI training and generation.
By adapting copyright laws to address the complexities of AI, legal systems can promote both fairness and technological progress.
II. Algorithmic Bias and Discrimination
The Real-World Impact of Bias
Algorithmic bias has become a critical issue as AI systems are increasingly used in decision-making processes. In Mobley v. Workday, Inc., the plaintiff alleged that AI-powered hiring systems excluded candidates based on race, age, and disability, violating Title VII of the Civil Rights Act. Similar concerns have been raised in criminal justice, where AI has faced scrutiny for perpetuating racial and socioeconomic biases, as highlighted in State v. Black.
These cases illustrate how biases embedded in training data can lead to discriminatory outcomes. AI systems learn from historical data, which may reflect existing inequalities. For example, hiring algorithms trained on industry-specific datasets may disproportionately exclude women and minorities if those groups were underrepresented in the past.
Legal Precedents and Challenges
The U.S. legal framework addresses discrimination through doctrines like disparate impact, which prohibits practices that disproportionately harm protected groups even if the intent was neutral. This principle was established in Griggs v. Duke Power Co. (1971) and has since been applied to various forms of discrimination.
However, AI introduces new challenges. Unlike human decision-makers, algorithms operate as "black boxes," making it difficult to understand or challenge their decisions. This lack of transparency complicates efforts to prove discrimination and hold developers accountable.
International Approaches to Bias
The European Union has taken a proactive stance with its AI Act, which classifies AI systems based on their potential risks and imposes stringent requirements on high-risk systems. These include mandatory bias testing, transparency obligations, and oversight mechanisms. In contrast, the United States relies more heavily on litigation and sector-specific regulations, resulting in a fragmented approach.
Addressing Algorithmic Bias
To mitigate bias and ensure fairness, legal and technical interventions are necessary:
Implement adaptive bias mitigation techniques that dynamically adjust model parameters during training to minimize bias. For instance, a study on AI-driven recruitment systems demonstrated the effectiveness of an adaptive framework in real-time bias detection and correction, maintaining fairness across demographic groups while ensuring accuracy (Journal of Artificial Intelligence and Machine Learning in Management, 2022).
Conduct comprehensive impact assessments prior to deploying AI systems. These assessments evaluate potential biases and societal implications, ensuring alignment with ethical standards. Research highlights the need for such assessments to address practical applicability limitations and regulatory compliance issues associated with existing bias mitigation methods (King’s College London, Recommendations for Bias Mitigation Methods).
Establish continuous feedback loops with end-users to identify and correct biases in real-time. This approach ensures AI systems remain responsive to societal changes and user needs. Studies suggest that dynamic bias mitigation frameworks can effectively adapt to new data inputs, offering a responsive solution to bias challenges (Nature Machine Intelligence, Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning, 2023).
III. Privacy, Reputational Harm, and AI-Generated Content
The Rise of Synthetic Media
AI-generated content, including deepfakes and other synthetic media, poses significant challenges to privacy and reputational rights. In Rafal Brzoska v. Meta Platforms, Inc., the plaintiff alleged that deepfake advertisements caused reputational harm, highlighting the dangers of AI-generated misinformation. Similar concerns have arisen in cases involving explicit deepfake imagery, such as R v. Nelson in the UK.
Legal Gaps and Challenges
Existing privacy laws, such as the General Data Protection Regulation (GDPR) in Europe, provide individuals with rights over their personal data. However, these frameworks struggle to address synthetic content that combines real and fabricated elements. Victims of deepfakes often face barriers to redress, including jurisdictional challenges and difficulties proving harm.
Deepfake technology also raises questions about intent. Traditional defamation laws focus on the perpetrator’s intent, but AI-generated harms often result from automated processes rather than deliberate actions.
Policy Proposals for Synthetic Harms
To address these challenges, lawmakers should consider:
Synthetic Defamation Laws: Creating new legal categories that focus on the effects of AI-generated content rather than intent.
Platform Accountability: Requiring platforms to deploy detection tools and remove harmful content promptly.
Digital Identity Protections: Expanding privacy laws to include safeguards against the unauthorized use of likenesses in synthetic media.
By modernizing legal frameworks, governments can protect individuals from the unique harms posed by AI-generated content.
IV. Ethical Governance of AI in Legal Practice
The Role of AI in the Judiciary
AI is increasingly being used in legal practice, from drafting documents to analyzing case law. However, its use raises ethical and procedural concerns. In State v. Black, the reliance on opaque AI tools highlighted the risks of admitting algorithmic evidence without proper validation.
Ensuring Transparency and Accountability
The Daubert standard, which governs the admissibility of expert testimony in U.S. courts, requires that evidence be both reliable and relevant. Applying this standard to AI tools necessitates explainability, open-source algorithms, and rigorous validation processes. Without these safeguards, the judiciary risks undermining public trust.
Recommendations for Ethical AI Use
To integrate AI responsibly into legal practice, courts and legal professionals should:
Launch an AI Judiciary Fellowship Program that selects a diverse cohort of judges and legal professionals to undergo intensive training in AI technologies and their implications for the legal system. This program would involve hands-on experience with AI tools, mentorship from leading technologists, and opportunities to engage in real-world case studies. Participants would be tasked with developing actionable guidelines and best practices for AI use in their jurisdictions, thereby creating a network of informed advocates who can lead the charge in ethical AI implementation within the judiciary.
Foster dialogue between technologists, legal scholars, and policymakers to anticipate emerging risks. Interdisciplinary collaboration can lead to the development of robust frameworks that address both technical and ethical concerns. By working together, these groups can identify potential pitfalls and create solutions that balance innovation with the protection of fundamental rights.
Ethical governance of AI in the judiciary can enhance access to justice while maintaining the integrity of legal processes. By implementing these strategies, the legal system can leverage AI's benefits without compromising fairness or transparency.
AI systems are evolving to no longer merely extend human capabilities—they will act independently, generating decisions, creative works, and consequences without direct human input. This autonomy challenges the foundational principles of legal accountability: how can responsibility be attributed when the actor lacks intent, awareness, or moral agency?
These questions demand a reimagining of legal frameworks. Should AI systems, as autonomous decision-makers, be treated as legal entities with obligations and liabilities? Or does the complexity of their actions require hybrid models of accountability, distributing responsibility across developers, users, and the systems themselves? These approaches reflect the growing need to move beyond human-centric legal constructs.
The rise of non-human agency also compels us to confront deeper philosophical questions: What does it mean to act, to create, to harm, and to be accountable in a world where machines increasingly shape outcomes? Such questions force a reassessment of the legal system’s role in governing actions that no longer fit neatly into traditional definitions of agency and intent.