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Is Machine Learning Unsafe and Irresponsible in Social Sciences? Paradoxes and Reconsidering from Recidivism Prediction Tasks

arXiv 2311.06537 ↗First posted 11 Nov 2023cs.CY cs.AI stat.APcited by 0

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Abstract (arXiv)

The paper addresses some fundamental and hotly debated issues for high-stakes event predictions underpinning the computational approach to social sciences. We question several prevalent views against machine learning and outline a new paradigm that highlights the promises and promotes the infusion of computational methods and conventional social science approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 124 references that are in the map. First posted on arXiv 11 Nov 2023.

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23 of 124 references.

References · 23

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  4. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
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  11. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 85
  12. 2020Interpretability and Explainability: A Machine Learning Zoo Mini-tourcited by 55
11 more
  1. 2018The age of secrecy and unfairness in recidivism predictioncited by 53
  2. 2017Learning Certifiably Optimal Rule Lists for Categorical Datacited by 50
  3. 2018Explanation Methods in Deep Learning: Users, Values, Concerns and Challengescited by 49
  4. 2020In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Predictioncited by 42
  5. 2023An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learningcited by 25
  6. 2022Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainabilitycited by 21
  7. 2021Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AIcited by 16
  8. 2019Towards Quantification of Explainability in Explainable Artificial Intelligence Methodscited by 15
  9. 2022Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracycited by 15
  10. 2019Fair-by-design explainable models for prediction of recidivismcited by 8
  11. 2022Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Modelscited by 5

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