Summary of Engineering Trustworthy Ai: a Developer Guide For Empirical Risk Minimization, by Diana Pfau and Alexander Jung
Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization
by Diana Pfau, Alexander Jung
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate how empirical risk minimization (ERM) can be modified to prioritize trustworthiness over accuracy in artificial intelligence (AI) systems. As AI increasingly influences critical decisions across various domains, ensuring the transparency and fairness of these systems is crucial. The authors propose design choices for ERM components that align with emerging standards for trustworthy AI, providing actionable guidance for building reliable AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence systems are making important decisions in our personal and public lives. While they’re often very good at getting things right, they can also make mistakes because of biases or be too hard to understand. This paper looks at how we can change the way AI works so it prioritizes being trustworthy over just being correct. By doing this, we can make sure AI systems are fair and transparent. |
