mlsanityData to 7 Oct 2026

Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization

arXiv 2410.19361 ↗First posted 25 Oct 2024cs.AIcited by 3

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Abstract (arXiv)

AI systems increasingly shape critical decisions across personal and societal domains. While empirical risk minimization (ERM) drives much of the AI success, it typically prioritizes accuracy over trustworthiness, often resulting in biases, opacity, and other adverse effects. This paper discusses how key requirements for trustworthy AI can be translated into design choices for the components of ERM. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 47 of its 162 references that are in the map. First posted on arXiv 25 Oct 2024.

Builds on

47 of 162 references.

References · 47

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35 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Beyond Explainability: The Case for AI Validationcited by 1
  2. 2025Privacy-Preserving Empathy Detection in Video Interactionscited by 0
  3. 2026Certified Uncertainty Propagation in One-Shot Federated Bayesian Models via Posterior Event Transportcited by 0