The Problematization of Moral Responsibility in Artificial Intelligence: Intentionality, Agency, and the Boundaries of Moral Luck

Project Abstract

The emergence of artificial intelligence systems capable of autonomous decision-making challenges traditional conceptions of moral responsibility, agency, and accountability. This abstract synthesizes a comprehensive inquiry into how intentionality, ascriptions of agency, and the boundaries of moral luck reconfigure responsibility in AI. The study situates AI within a normative framework that distinguishes between engineering responsibility, operator accountability, and outcomes-driven moral praise or blame, while interrogating whether AI can or should bear moral responsibility in parallel with human agents. It analyzes the nature of intentionality in machine cognition, distinguishing between programmed directives, emergent goal-pursuit, and interpretive ascriptions by human evaluators, to assess the extent to which AI can possess or simulate intentional states that are relevant to moral evaluation. The concept of agency is examined through a triadic lens instrumental agency, where systems execute instructions; causal agency, where outcomes traceable to computational processes; and normative agency, wherein evaluators ascribe intent and justificatory reasons. The study interrogates the boundaries imposed by moral luck—specifically, how factors outside the direct control of AI design or operator influence judgments of responsibility for outcomes, including unforeseen emergent behaviors, data biases, and iterated decision dynamics. Methodologically, the research deploys a multi-method approach combining analytic philosophical scrutiny, case-based moral diagnostics drawn from contemporary AI deployments (such as healthcare, criminal justice, and autonomous transport), and legal-ethical discourse analysis to illuminate gaps between doctrine and practice. Theoretical arguments engage with compatibility and incompatibility theses regarding moral responsibility, exploring compatibilist and non-cognitivist perspectives, while considering the imputability of blame under different levels of autonomy and control. The project also examines institutional mechanisms—codes of ethics, governance frameworks, liability schemas, and design principles like transparency, explicability, and auditability—that mediate accountability without conceding undirected blame to machines. A central aim is to delineate a refined applicability condition for moral responsibility that accounts for the unique epistemic and agency-related constraints of AI systems, proposing criteria for when responsibility should be attributed to developers, operators, organizations, or the systems themselves. By interrogating the asymmetries between predicted, intended, and actual outcomes, the research contributes to a more nuanced normative map of AI accountability that accommodates imperfect predictability and the distributed nature of modern AI stewardship. The study culminates in a set of actionable recommendations for policymakers, engineers, and ethicists aimed at establishing robust moral accountability infrastructures that respect both the complexity of AI systems and the ethical duty to prevent harm.

Project Overview

What This Project Is About

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A plain-language overview of the topic and what the project investigates.

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The Problem It Addresses

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What problem or gap this project tackles and why it matters to the field or society.

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Objectives of the Project

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1. Clarify what we mean by moral responsibility when AI systems can act autonomously.
2. Explore how intentionality, agency, and luck influence responsibility judgments.
3. Assess current legal and ethical frameworks for AI accountability.
4. Propose a practical framework for assigning responsibility in AI-related cases.

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What You Will Do Step by Step

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1. Review key philosophical ideas about responsibility, intention, and luck.
2. Analyze case studies of AI decisions in real-world contexts (e.g., medicine, driving, finance).
3. Discuss how concepts of agency apply to artificial systems.
4. Evaluate existing accountability mechanisms and identify gaps.
5. Develop a simple framework for responsibility attribution in AI scenarios.

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Expected Outcome

A clear, accessible framework for understanding when AI agents can bear responsibility, plus recommendations for policy and practice to improve accountability and fairness.