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Publications
  • 2023
  • Article
  • Proceedings of the International Conference on Machine Learning (ICML)

On the Impact of Actionable Explanations on Social Segregation

By: Ruijiang Gao and Himabindu Lakkaraju
  • Format:Electronic
  • | Pages:17
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Abstract

As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research on algorithmic recourse in recent years. While recourses can be extremely beneficial to affected individuals, their implementation at a large scale can lead to potential data distribution shifts and other unintended consequences. However, there is little to no research on understanding the impact of algorithmic recourse after implementation. In this work, we address the aforementioned gaps by making one of the first attempts at analyzing the delayed societal impact of algorithmic recourse. To this end, we theoretically and empirically analyze the recourses output by state-of-the-art algorithms. Our analysis demonstrates that large-scale implementation of recourses by end users may exacerbate social segregation. To address this problem, we propose novel algorithms which leverage implicit and explicit conditional generative models to not only minimize the chance of segregation but also provide realistic recourses. Extensive experimentation with real-world datasets demonstrates the efficacy of the proposed approaches.

Keywords

Forecasting and Prediction; AI and Machine Learning; Outcome or Result

Citation

Gao, Ruijiang, and Himabindu Lakkaraju. "On the Impact of Actionable Explanations on Social Segregation." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 10727–10743.
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About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

More from the Authors

    • 2024
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    Fair Machine Unlearning: Data Removal while Mitigating Disparities

    By: Himabindu Lakkaraju, Flavio Calmon, Jiaqi Ma and Alex Oesterling
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    Quantifying Uncertainty in Natural Language Explanations of Large Language Models

    By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
    • 2023
    • Advances in Neural Information Processing Systems (NeurIPS)

    Post Hoc Explanations of Language Models Can Improve Language Models

    By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
More from the Authors
  • Fair Machine Unlearning: Data Removal while Mitigating Disparities By: Himabindu Lakkaraju, Flavio Calmon, Jiaqi Ma and Alex Oesterling
  • Quantifying Uncertainty in Natural Language Explanations of Large Language Models By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
  • Post Hoc Explanations of Language Models Can Improve Language Models By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
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