Summary of Cost-adaptive Recourse Recommendation by Adaptive Preference Elicitation, By Duy Nguyen et al.
Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation
by Duy Nguyen, Bao Nguyen, Viet Anh Nguyen
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 A novel approach to algorithmic recourse is proposed, which recommends a cost-efficient action to reverse an unfavorable machine learning classification decision. Unlike existing methods, this paper assumes incomplete information about the underlying cost function due to distinct subject preferences. A two-step approach is designed: first, a question-answering framework refines the confidence set of the Mahalanobis matrix cost; then, gradient-based and graph-based cost-adaptive recourse methods are used to generate valid recommendations considering the whole confidence set. Numerical evaluations show the benefits of this approach over state-of-the-art baselines in delivering cost-efficient recourse recommendations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for machines to give people helpful actions when they disagree with a machine learning decision. Right now, most methods assume that people have complete information about what’s important to them. But in real life, people might have different priorities and preferences. The authors of this paper developed a two-part plan: first, ask the person questions to get more accurate information about what matters to them; then, use that information to create good recommendations that consider all their priorities. |
Keywords
* Artificial intelligence * Classification * Machine learning * Question answering
