aiqbench analysis
This position paper argues that optimizing AI systems for individual trustworthiness metrics like fairness or privacy in isolation is insufficient due to significant negative trade-offs between them. The authors advocate for a holistic research approach that systematically accounts for interactions across all aspects of trustworthy AI simultaneously. This work provides critical guidance for researchers aiming to develop integrated trust mechanisms without inadvertently compromising other essential values.
Trustworthy AI Must Account for Interactions
arXiv:2504.07170v3 Announce Type: replace Abstract: Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainability, and uncertainty quantification. The ultimate goal of Trustworthy AI research is to achieve all aspects simultaneously. However, efforts to enhance one aspect often introduce unintended trade-offs that negatively impact others. In this position paper, we review notable approaches to five aspects and systematically consider every pair, detailing the negative interactions that can arise. For example, applying differential privacy to model training can amplify biases, undermining fairness. Drawing on these findings, we take the position that current research practices of improving one or two aspects in isolation are insufficient. Instead, research on Trustworthy AI must account for interactions between aspects and adopt a holistic view across all relevant axes at once. We offer recommendations for how researchers can work towards integrated trust, and provide guidance for practitioners to manage interactions today.
Abstract:Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainability, and uncertainty quantification. The ultimate goal of Trustworthy AI research is to achieve all aspects simultaneously. However, efforts to enhance one aspect often introduce unintended trade-offs that negatively impact others. In this position paper, we review notable approaches to five aspects and systematically consider every pair, detailing the negative interactions that can arise. For example, applying differential privacy to model training can amplify biases, undermining fairness. Drawing on these findings, we take the position that current research practices of improving one or two aspects in isolation are insufficient. Instead, research on Trustworthy AI must account for interactions between aspects and adopt a holistic view across all relevant axes at once. We offer recommendations for how researchers can work towards integrated trust, and provide guidance for practitioners to manage interactions today.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2504.07170 [cs.LG]
(or arXiv:2504.07170v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2504.07170 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 243 words
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