Summary of Hacking a Surrogate Model Approach to Xai, by Alexander Wilhelm and Katharina A. Zweig

Main: Artificial Intelligence (cs.AI)
Secondary: None

Hacking a surrogate model approach to XAI

by Alexander Wilhelm, Katharina A. Zweig

First submitted to arxiv on: 24 Jun 2024

GrooveSquid.com Paper Summaries

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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 explores explainable AI (XAI) techniques to ensure fairness and transparency in algorithmic decision-making systems (ADMs). Specifically, it examines surrogate models as a means of making complex AI systems more interpretable. Surrogate models are simpler machine learning models that approximate the behavior of a black box model, allowing for human intuition-based understanding. The paper aims to investigate how well these surrogate models can approximate the original black box model’s behavior.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The researchers are looking at ways to make artificial intelligence (AI) more understandable. They’re doing this by creating simpler AI models that mimic the way a complex AI system makes decisions. This is important because people need to trust the decisions made by these systems, and right now, we don’t really know how they work.