What are AI Policy Areas? A Guide to Key Legal and Regulatory Questions

June 20, 2025

Artificial intelligence (AI) is transforming technology, business, and society, prompting urgent legal and policy considerations. Lawmakers, legal scholars, and regulators face complex challenges as they seek to establish coherent frameworks for the development and responsible use of AI. Understanding these policy areas is essential not only for governments and private sector stakeholders, but also for anyone concerned with how AI intersects with public policy issues, rights, and societal values.

Key AI Policy Areas

AI policy encompasses a set of core domains regarding use of artificial intelligence, each with significant implications for law, governance, and public well-being:

  • Data Privacy and Security: Safeguarding personal and sensitive data used in AI systems.
  • Algorithmic Transparency and Explainability: Ensuring decisions made by AI are understandable and open to scrutiny.
  • Accountability and Liability: Establishing responsibility when AI models or tools cause harm or malfunction.
  • Fairness, Bias, and Anti-Discrimination: Preventing unjust outcomes and reinforcing civil rights.
  • Intellectual Property and Innovation: Balancing protection of AI-driven creations with support for open innovation.
  • National Security: Regulating dual-use technologies and safeguarding critical infrastructure.

1. Data Privacy and Security

AI systems depend on large datasets, including personal and sensitive information. This dependence raises critical questions about data governance, access, and ethical use. AI regulations such as the GDPR and privacy protection measures like HIPAA define how data must be collected, protected, and managed, yet emerging technologies’ increasing complexity intensifies these challenges.[1]

Concerns around surveillance, unauthorized data use, and breaches underscore the need for evolving privacy safeguards. Policymakers aim to balance individual rights with the responsible use of data that supports development of AI. International initiatives like the OECD’s AI Policy Observatory, for example, offer insights into how nations manage these issues.[2]

2. Algorithmic Transparency and Explainability

Many AI applications function as opaque systems, making decisions without clear rationale. This “black box” problem undermines trustworthy AI and raises both technical and legal concerns. In response, regulators are advancing requirements for explainability and auditability, ensuring that individuals affected by AI-based decisions can understand and contest them.

Explainable AI supports informed decision-making and is often linked to due process rights. Policies promoting transparency aim to enhance accountability and foster public trust in the use of AI tools across sectors.

3. Accountability and Liability

Determining legal responsibility for harms caused by AI remains a pressing issue. Existing tort and product liability frameworks are often ill-equipped to address the distributed nature of AI development and deployment. Some propose joint liability among developers, deployers, and end users, while others advocate for new legal standards tailored to high-risk AI applications.

The development of fair and enforceable liability rules is central to mitigating AI risk while preserving innovation. Legal debates focus on creating consistent mechanisms for redress when AI systems malfunction or cause harm.

4. Fairness, Bias, and Anti-Discrimination

Bias in AI systems, often stemming from unrepresentative or flawed datasets, can perpetuate discrimination across domains such as employment, finance, healthcare, and law enforcement. Policymakers are increasingly requiring regular audits, dataset transparency, and inclusive design practices to mitigate these harms.[3]

Efforts to prevent algorithmic discrimination include both legislative proposals and enforcement mechanisms grounded in civil rights law. Regulatory bodies also emphasize the need for trustworthy AI that upholds principles of equality and fairness.

5. Intellectual Property and Innovation

The rise of AI-generated content challenges conventional intellectual property (IP) norms. Key questions include whether AI can be considered an inventor and who holds rights to AI-generated outputs. As generative AI systems become more common, stakeholders must navigate the balance between protecting innovation and preserving open access to AI tools.

Current legal discussions focus on adapting IP laws (copyright, patent, and trade secrets) to accommodate the collaborative and data-driven nature of AI. Ongoing AI technology policy initiatives reflect this tension between protection and progress.

There are many cases of litigation over copyright and the use of copyrighted works in AI training datasets. These disputes, involving both individual creators and large content owners, are shaping the legal boundaries of permissible data use for AI model development and could significantly influence the future availability and scope of generative AI technologies.

6. AI and National Security

AI’s integration into defense, cybersecurity, and critical infrastructure creates unique national security concerns. Dual-use technologies can serve both civilian and military purposes, complicating efforts to regulate their development and export. Other threats include AI-driven cyberattacks and disinformation campaigns.

Policymakers are responding with stricter export controls, risk assessments for high-impact AI systems, and international cooperation on shared security standards. Global forums such as the United Nations continue to explore common norms to ensure responsible AI deployment in national security contexts.[4]

Current AI Governance and Emerging Debates

AI-related laws and regulatory initiatives vary widely across jurisdictions. In the United States, a fragmented approach spans federal and state agencies. The Federal Trade Commission, Equal Employment Opportunity Commission, and Department of Transportation are among those addressing different dimensions of AI oversight.

Some states are implementing their own regulations concerning facial recognition, algorithmic accountability, and AI ethics. These efforts highlight the challenges of regulating a fast-evolving technology with widespread applications.

National AI Initiative Act

The National AI Initiative Act, passed in the United States in 2021, sets a federal government-wide approach for AI research and development as well as federal, state and local government policy. The law works to balance support for innovation with public trust and national interest.[5]

It does so by creating channels for federal agencies to:

  • Coordinate AI efforts and fund programs for research, education, and workforce training.
  • Create standards in ethics, safety, and data sharing.
  • Encourage American innovation in developing and implementing machine learning technologies.
  • Support US economic growth and prepare workers for shifts in the job market to position the country as a global leader in developing and deploying AI.
  • Shape how AI is used in areas like health care and medical research, science, and national defense.

AI Bill of Rights

The Blueprint for an AI Bill of Rights, or AI Bill of Rights, set out principles to protect people as companies and governments use AI. Importantly, it served only as a non-enforceable policy statement, rather than an actual bill.[6]

It suggested five main rights:

  • The right to safe and effective systems.
  • Protection against discrimination.
  • Data privacy.
  • Notice and explanation about how AI is used.
  • Human alternatives or fallback when needed.

It aimed to prevent harm and build trust as AI becomes more common. However, it largely became irrelevant when President Biden issued Executive Order 14110 on AI.[7] Unlike the AI Bill of Rights, it had concrete directives for federal agencies. EO 14110 has since been rescinded under President Trump, whose administration has issued its own AI executive orders, culminating in the recently announced National AI Action Plan.

Comparative International Perspectives

Across the world, governments are advancing a wide array of AI strategies to stay ahead of new technologies. The European Union’s AI Act, for example, exemplifies a risk-based regulatory model that imposes stricter requirements on high-risk AI systems.[8] In contrast, countries like Japan and Canada emphasize innovation-friendly guidelines and voluntary compliance frameworks.[9],[10]

International organizations such as the OECD are working to harmonize principles around trustworthy AI, promoting values like human rights, transparency, and accountability. These cross-border efforts are crucial for managing global AI deployment and risks.[2]

Ongoing Debates and Future Directions

Despite regulatory progress, many issues remain unresolved. Key debates involve the ethical limits of AI, enforcement of cross-border rules, and how to govern systems that evolve over time. As powerful AI tools spread globally, calls for multilateral governance and shared technical standards are intensifying.

Future AI strategy development must be inclusive, interdisciplinary, and iterative, drawing on input from technologists, legal experts, civil society, and the public. Proposals such as the AI Bill of Rights in the U.S. exemplify early-stage efforts to define the boundaries of ethical AI use.

Conclusion

Understanding AI policy is essential for anyone engaged in technology, law, or public governance. Legal frameworks for privacy, accountability, fairness, and national security shape how AI is adopted and how its risks are managed. As AI technologies evolve, so too must the regulatory approaches that guide their safe, ethical, and effective use. Continued dialogue, research, and global cooperation will be critical to ensuring that AI serves the public good.

WashU Law is the leading law school in legal technology and innovation.

[1] General Data Protection Regulation (GDPR) (2024), https://gdpr-info.eu/ (last visited Jul 30, 2025).

[2] Artificial Intelligence | OECD. Available at: https://www.oecd.org/en/topics/policy-issues/artificial-intelligence.html (Accessed: 30 July 2025).

[3] Black, Emily and Koepke, John Logan and Kim, Pauline and Barocas, Solon and Hsu, Mingwei, Less Discriminatory Algorithms (October 2, 2023). Georgetown Law Journal, Vol. 113, No. 1, 2024, Washington University in St. Louis Legal Studies Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=4590481 or http://dx.doi.org/10.2139/ssrn.4590481

[4] Seizing the opportunities of safe, secure and trustworthy artificial intelligence systems for sustainable development (March 11, 2024) United Nations. Available at: https://docs.un.org/en/A/78/L.49 (Accessed: 30 July 2025).

[5] H.R.6216 – 116th Congress (2019-2020): National Artificial Intelligence Initiative Act of 2020 | Congress.gov. Available at: https://www.congress.gov/bill/116th-congress/house-bill/6216 (Accessed: 30 July 2025).

[6] Blueprint for an AI Bill of Rights | OSTP | The White House. National Archives and Records Administration. Available at: https://bidenwhitehouse.archives.gov/ostp/ai-bill-of-rights/ (Accessed: 30 July 2025).

[7] Biden, Joseph, Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, FederalRegister.gov (2023), https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence (Accessed: 15 August 2025).

[8] The EU Artificial Intelligence Act, EU Artificial Intelligence Act, https://artificialintelligenceact.eu/ (last visited Jul 30, 2025).

[9] Andrews, C. (2025) Japan passes innovation-focused AI governance bill, IAPP. Available at: https://iapp.org/news/a/japan-passes-innovation-focused-ai-governance-bill (Accessed: 30 July 2025).

[10] Government of Canada (2025) Artificial Intelligence and Data Act (AIDA) Companion Document. Available at: https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act-aida-companion-document (Accessed: 30 July 2025).

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