Summary of Learning Actionable Counterfactual Explanations in Large State Spaces, by Keziah Naggita and Matthew R. Walter and Avrim Blum
Learning Actionable Counterfactual Explanations in Large State Spaces
by Keziah Naggita, Matthew R. Walter, Avrim Blum
First submitted to arxiv on: 25 Apr 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 This paper tackles counterfactual explanations (CFEs), which are crucial for consequential decisions like loan applications, hiring, and admissions. The authors explore settings where optimal CFEs correspond to weighted set cover problems. They consider a scenario where an agent wants to perform the cheapest actions that provide all needed capabilities to achieve a positive classification. Since this is an NP-hard optimization problem, they aim to learn a CFE generator from training data (instances of agents and their optimal CFEs) that can quickly provide optimal sets of actions for new agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines can explain why they made certain decisions. For example, when a bank denies a loan application, it could tell the customer what changes they would need to make to get approved. The authors want to find a way to teach machines to generate these explanations quickly and accurately. They’re trying to solve an optimization problem where the machine finds the cheapest combination of actions that will achieve a desired outcome. |
Keywords
» Artificial intelligence » Classification » Optimization
