Embracing Contradiction: Theoretical Inconsistency Will Not Impede the Road of Building Responsible AI Systems

• URL: http://arxiv.org/abs/2505.18139v2
• Date: Tue, 27 May 2025 20:19:42 GMT
• Title: Embracing Contradiction: Theoretical Inconsistency Will Not Impede the Road of Building Responsible AI Systems
• Authors: Gordon Dai, Yunze Xiao,
• Abstract summary: This position paper argues that the theoretical inconsistency often observed among Responsible AI (RAI) metrics should be embraced as a valuable feature rather than a flaw to be eliminated.<n>We contend that navigating these inconsistencies, by treating metrics as divergent objectives, yields three key benefits.
• Score: 0.6906005491572401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This position paper argues that the theoretical inconsistency often observed among Responsible AI (RAI) metrics, such as differing fairness definitions or tradeoffs between accuracy and privacy, should be embraced as a valuable feature rather than a flaw to be eliminated. We contend that navigating these inconsistencies, by treating metrics as divergent objectives, yields three key benefits: (1) Normative Pluralism: Maintaining a full suite of potentially contradictory metrics ensures that the diverse moral stances and stakeholder values inherent in RAI are adequately represented. (2) Epistemological Completeness: The use of multiple, sometimes conflicting, metrics allows for a more comprehensive capture of multifaceted ethical concepts, thereby preserving greater informational fidelity about these concepts than any single, simplified definition. (3) Implicit Regularization: Jointly optimizing for theoretically conflicting objectives discourages overfitting to one specific metric, steering models towards solutions with enhanced generalization and robustness under real-world complexities. In contrast, efforts to enforce theoretical consistency by simplifying or pruning metrics risk narrowing this value diversity, losing conceptual depth, and degrading model performance. We therefore advocate for a shift in RAI theory and practice: from getting trapped in inconsistency to characterizing acceptable inconsistency thresholds and elucidating the mechanisms that permit robust, approximated consistency in practice.

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