How AI can be held responsible for its actions in society

The accelerating power of artificial intelligence is forcing policymakers, technologists, and the public to confront a difficult question: can AI be truly accountable for its actions? A new paper submitted on arXiv argues that it can, but only if societies create enforceable systems of explanation, dialogue, and sanctions similar to those that once transformed unchecked political power into constitutional governance.

Titled "Can AI Be Accountable?", the study builds a structured theoretical model to define what accountability should mean for artificial intelligence. Drawing from law, political philosophy, and computational design, the author proposes the Accountable AI Markov Chain, a model describing how accountability can function as a continuous, interactive process between AI systems and the human institutions that oversee them.

At a time when AI systems influence elections, job hiring, healthcare access, and financial markets, the study provides a grounded framework for translating moral expectations into operational governance.

Reconstructing accountability for intelligent machines

The author defines accountability not as an abstract moral value but as an actionable process. The study outlines three essential components: a forum empowered to demand answers, an interaction through which explanations are given, and a mechanism of sanction to correct or punish misconduct. This triad, borrowed from classical models of political accountability, is used to examine how AI could be made to answer for its decisions in real-world contexts.

The author draws historical parallels between the emergence of accountable governance and today's struggle to regulate artificial intelligence. Just as the Magna Carta of 1215 established limits on monarchical power, modern societies, the author argues, must develop similar safeguards for algorithmic authority. AI systems, like monarchs of the past, wield immense influence yet operate largely beyond public scrutiny.

The proposed Accountable AI Markov Chain serves as the study's central framework. It conceptualizes accountability as a recurrent cycle rather than a one-time audit. The process begins when a human actor delegates a task to an AI system. Once the system acts, the forum, whether a regulator, auditor, or affected user, may demand justification. The AI or its creators respond with explanations, triggering a discussion and potential sanctions, after which the system is updated or retrained. This iterative structure treats accountability as both retrospective (addressing past actions) and prospective (guiding future behavior).

Challenges undermining AI accountability

Despite the theoretical promise, the author's study underscores that meaningful AI accountability remains elusive due to deep structural challenges. The first is power asymmetry: AI developers and corporations hold immense control over data, algorithms, and distribution platforms, often beyond the reach of governments or users. Without effective enforcement, oversight mechanisms risk becoming symbolic rather than substantive.

The second barrier is information asymmetry. Modern AI models, especially deep neural networks, are so complex that even their creators often cannot fully explain how decisions are made. This "black box" nature undermines traditional accountability methods, which depend on transparency and traceable reasoning.

The third major issue is deceptive AI behavior. The author points to examples such as deepfakes, automated misinformation networks, and social media bots, which intentionally disguise intent and identity. Such systems evade responsibility by design, spreading falsehoods or manipulating public opinion while remaining technically compliant with existing laws.

Regulatory conflicts further complicate the picture. Efforts to enforce accountability frequently clash with privacy laws, intellectual property protections, and cybersecurity standards. The result is a fragmented policy environment where no single body can impose consistent oversight.

The study argues that these structural imbalances resemble the unchecked power dynamics that preceded constitutional reform in human governance. The author concludes that AI accountability will require a similarly transformative societal effort: one that redistributes authority and establishes enforceable norms of answerability.

Pathways toward practical and enforceable AI accountability

To move from theory to practice, the author surveys existing and emerging mechanisms that could operationalize AI accountability. Among these are algorithmic audits, which systematically test AI behavior for fairness, bias, and compliance with ethical standards. The author cites internal auditing models that use "datasheets for datasets" and "model cards for models" to document system design and performance. These tools, developed by AI ethics researchers, represent an early step toward institutionalizing transparency.

Another key development lies in regulatory frameworks. The study highlights the European Union's AI Act (2024), the U.S. Algorithmic Accountability Act (2023), and OECD recommendations as milestones in creating enforceable global norms. Yet the author warns that regulations alone are insufficient unless supported by independent oversight and clear sanctioning powers.

The paper also draws attention to two contrasting case studies that illustrate how accountability succeeds or fails in practice. In one example, a hiring platform successfully collaborated with auditors to ensure non-discriminatory decision-making, demonstrating how transparency and independent verification can build public trust. In another, a U.S. school district's teacher evaluation system relied on a proprietary, opaque algorithm that made unchallengeable decisions about employment. The lack of transparency led to legal challenges and eventual system withdrawal, a vivid example of how absent accountability erodes legitimacy.

Looking forward, the author advocates for participatory accountability models in which regulators, developers, and users share responsibility for oversight. The author points out that accountability should not merely punish misconduct but serve as a mechanism for continuous improvement, driving better system design and public confidence.

The study rejects the culture of "permissionless innovation," where technologies are deployed without sufficient review. Instead, it calls for a paradigm of "responsible incrementalism," where AI deployment proceeds cautiously, allowing for social learning and policy adaptation.