Summary of Privacy Implications Of Explainable Ai in Data-driven Systems, by Fatima Ezzeddine
Privacy Implications of Explainable AI in Data-Driven Systems
by Fatima Ezzeddine
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 The paper addresses the lack of interpretability in machine learning (ML) models, which is a major limitation for building trust in these complex systems. The authors propose Explainable AI (XAI) techniques to provide transparency into the internal workings of ML models, using methods such as Counterfactual Explanations and Feature Importance. However, they also highlight the challenge of balancing XAI with privacy concerns, particularly when sensitive information is involved. To mitigate this issue, the paper explores the use of privacy-preserving techniques like differential privacy to protect sensitive data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are very good at doing things, but we don’t always know how or why they’re making decisions. This lack of understanding can be a problem because it makes us question whether these models are truly trustworthy. One way to address this issue is by using “explainable AI” techniques that help us understand how the model is working. These techniques include methods like counterfactual explanations and feature importance, which provide insights into the internal decision-making processes of the model. However, there’s a challenge when it comes to balancing these explainability techniques with privacy concerns, especially when sensitive information is involved. |
Keywords
» Artificial intelligence » Machine learning
