EthicsTrustworthinessAccountabilitycs.CY
Ethical AI Governance: Methods for Evaluating Trustworthy AI
Louise McCormack, Malika Bendechache
August 28, 2024
arXiv.org
7 citations
Key Takeaways
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Self-assessment is the primary method for evaluating trustworthy AI, meaning organizations developing AI systems are largely responsible for checking their own ethical standards and safety measures, rather than relying on external oversight.
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Trustworthy AI requires alignment with human values, indicating that ethical AI governance must actively consider how AI systems' behavior and decision-making reflect the values of the people they affect.
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Current evaluation methods lack standardization, as the paper reviews and classifies existing TAI evaluation approaches, suggesting the field still needs to develop more consistent frameworks for assessing whether AI systems are truly trustworthy.
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Ethics and safety are interconnected in AI development, with the paper treating ethical standards and safety considerations as integrated components rather than separate concerns when evaluating AI systems.
Abstract
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.