Summary of Time Can Invalidate Algorithmic Recourse, by Giovanni De Toni et al.
Time Can Invalidate Algorithmic Recourse
by Giovanni De Toni, Stefano Teso, Bruno Lepri, Andrea Passerini
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed Algorithmic Recourse (AR) method aims to provide users with actionable steps to overturn unfavourable decisions made by machine learning predictors. The current approach, however, assumes a static environment, neglecting the impact of time on the effectiveness of these actions. This paper investigates the robustness of AR over time by applying causal analysis. Theoretical and empirical results demonstrate that even robust causal AR methods can fail unless the world is stationary. Moreover, counterfactual AR cannot be solved optimally unless the world is fully deterministic. To address this challenge, a simple yet effective algorithm for temporal AR is proposed, which explicitly accounts for time under the assumption of having access to an estimator approximating the stochastic process. Experimental results on synthetic and realistic datasets show that considering time yields more resilient solutions to potential trends in data distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Algorithmic Recourse (AR) helps people fix wrong decisions made by machine learning models. But, these fixes might not work well over time because the world keeps changing. This paper looks at how AR works when time is considered. The results show that even good AR methods can fail if the world isn’t static. Additionally, fixing past mistakes with counterfactual AR isn’t always possible unless everything is certain. To fix this problem, a new algorithm for temporal AR is proposed, which takes into account changes over time. This algorithm works better than previous ones and produces more reliable results. |
Keywords
* Artificial intelligence * Machine learning
