Source: Thomson Reuters

AI governance is accelerating across global, national, and local levels as public investment and new oversight bodies expand, yet practical enforcement of newly drafted guardrails remains underdeveloped. Legislators have shifted from ethical principles to binding and risk‑based frameworks, but courts and dispute resolution mechanisms will determine whether those expectations translate into enforceable accountability, test rights, evaluate harm, and assign responsibility. If individuals lack the ability to access these mechanisms, governance frameworks risk remaining theoretical or being ignored, a risk underscored by structural constraints in justice systems; in the United States, 92% of people do not receive needed help to access their rights, which could widen rather than narrow the justice gap if AI is layered onto an already strained system. Leaders from government, the legal sector, international organizations, industry, and civil society convened in early February for an expert discussion that informed the reflections presented.

Justice systems provide the operational infrastructure for AI governance by interpreting regulatory provisions in concrete cases and offering accessible pathways for redress, while disputes involving AI operate as early signals exposing gaps in transparency, fairness, and accountability. Existing human rights standards—legality, non‑discrimination, due process, and access to remedy—apply directly to AI‑enabled decision‑making and can anchor a people‑centered justice approach; the discussion emphasized that AI can assist judges but must never replace human judgment, accountability, or due process. Governance design will determine whether AI‑enabled tools broaden access to justice or introduce new barriers, and enforceability together with equal access to remedies underpin public trust and the successful adoption of AI in public and private spheres. Without systems that are understandable, affordable, and responsive, governance frameworks risk entrenching inequities, particularly where biased data can replicate disparities across employment, financial services, healthcare, and criminal justice.