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<p>This paper introduces the Capability-Sensitive Framework (CSF), a formal method for auditing AI systems through the lens of capability approach. CSF specifies two normative guardrails: a capability floor, which ensures no individual is pushed below thresholds for essential freedoms, and a life-plan ceiling, which guarantees that people retain viable paths toward their meaningful goals. These constraints are operationalized via two metrics, the Capability-Coverage Ratio (CCR) and Life-Plan Alignment Score (LAS), evaluated at both individual and subgroup levels. A typology of positive and negative modalities situates benefits and harms in capability terms, distinguishing ethical outcomes from systemic risks and clarifying category boundaries. CSF foregrounds human flourishing, ethical sufficiency and human-centric values, enabling actionable, model-agnostic guidance for high-stakes sociotechnical decisions. This work bridges normative political philosophy with formal auditing practices for AI systems, offering a rigorous foundation for capability-aware AI governance.</p>

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A capability-sensitive framework for assessing ethical assistance and harm in AI systems

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Sankarshan Saptasomabuddha

摘要

This paper introduces the Capability-Sensitive Framework (CSF), a formal method for auditing AI systems through the lens of capability approach. CSF specifies two normative guardrails: a capability floor, which ensures no individual is pushed below thresholds for essential freedoms, and a life-plan ceiling, which guarantees that people retain viable paths toward their meaningful goals. These constraints are operationalized via two metrics, the Capability-Coverage Ratio (CCR) and Life-Plan Alignment Score (LAS), evaluated at both individual and subgroup levels. A typology of positive and negative modalities situates benefits and harms in capability terms, distinguishing ethical outcomes from systemic risks and clarifying category boundaries. CSF foregrounds human flourishing, ethical sufficiency and human-centric values, enabling actionable, model-agnostic guidance for high-stakes sociotechnical decisions. This work bridges normative political philosophy with formal auditing practices for AI systems, offering a rigorous foundation for capability-aware AI governance.