Summary of Beyond One-size-fits-all: Adapting Counterfactual Explanations to User Objectives, by Orfeas Menis Mastromichalakis et al.
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
by Orfeas Menis Mastromichalakis, Jason Liartis, Giorgos Stamou
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 contributes to Explainable Artificial Intelligence (XAI) by investigating Counterfactual Explanations (CFEs), which provide valuable insights into machine learning algorithms’ decision-making processes. Despite CFEs’ growing popularity in XAI, existing literature overlooks the diverse needs and objectives of users across different applications and domains, leading to a lack of tailored explanations. The paper advocates for a nuanced understanding of CFEs, recognizing the variability in desired properties based on user objectives and target applications. It identifies three primary user objectives and explores the desired characteristics of CFEs in each case. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CFEs can provide valuable insights into AI systems’ decision-making processes by exploring alternative scenarios where certain factors differ. This paper aims to design more effective and tailored explanations that meet specific needs of users, enhancing collaboration with AI systems. By identifying primary user objectives and desired characteristics of CFEs in each case, this research contributes to Explainable Artificial Intelligence. |
Keywords
* Artificial intelligence * Machine learning
