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Ethical AI Governance: Methods for Evaluating Trustworthy AI

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arxiv 2409.07473 v1 pith:IYE5RVJU submitted 2024-08-28 cs.CY cs.AI

Louise McCormack , Malika Bendechache This is my paper · ORCID
classification cs.CYcs.AI
keywords methodsethicalevaluationself-assessmenttrustworthyaimsalignartificial
verification ladder T0 review T1 audit T2 compute T3 formal
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Trustworthy Artificial Intelligence (TAI) integrates ethics that align with human values, looking at their influence on AI behaviour and decision-making. Primarily dependent on self-assessment, TAI evaluation aims to ensure ethical standards and safety in AI development and usage. This paper reviews the current TAI evaluation methods in the literature and offers a classification, contributing to understanding self-assessment methods in this field.

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Cited by 1 Pith paper

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  1. Practitioner Insights on Fairness Requirements in the AI Development Life Cycle: An Interview Study

    cs.SE 2025-12 conditional novelty 5.0 of 10

    Practitioners recognize AI fairness but apply it unevenly, rarely document it formally, and frequently deprioritize it against accuracy, deadlines, and features.

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