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

2025-05-23

AI Summary

This paper addresses pervasive theoretical inconsistencies in Responsible AI (RAI)—such as conflicting fairness definitions and trade-offs between accuracy and privacy—not as flaws to be eliminated, but as inherent features requiring principled management. Method: Drawing on multi-objective optimization, ethical value modeling, robustness analysis, and formal characterization of metric conflicts, the authors define quantifiable “acceptability thresholds” for inconsistency, grounded in normative pluralism, epistemic completeness, and implicit regularization. Contribution/Results: The work introduces the novel paradigm of “acceptable inconsistency,” reframing inconsistency as a design resource rather than a defect. It shifts RAI evaluation from enforcing metric consistency toward embracing value tensions, thereby enhancing practical adaptability, ethical inclusivity, and systemic robustness of AI systems. This represents the first systematic effort to operationalize inconsistency as a constructive element in RAI development and assessment.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: Learning Human Values and Preferences

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Embrace theoretical inconsistency in Responsible AI metrics
• Navigate contradictions to represent diverse moral stances
• Optimize conflicting objectives for robust AI solutions

Innovation

• Embrace theoretical inconsistency in RAI metrics
• Optimize for conflicting objectives to prevent overfitting
• Characterize acceptable inconsistency thresholds in practice