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

arXiv: https://arxiv.org/abs/2311.06537 · HTML: https://mlsanity.com/paper/2311.06537/

## 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.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond](https://mlsanity.com/paper/2103.10689/) (2021)
- [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Interpretability and Explainability: A Machine Learning Zoo Mini-tour](https://mlsanity.com/paper/2012.01805/) (2020)
- [The age of secrecy and unfairness in recidivism prediction](https://mlsanity.com/paper/1811.00731/) (2018)
- [Learning Certifiably Optimal Rule Lists for Categorical Data](https://mlsanity.com/paper/1704.01701/) (2017)
- [Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges](https://mlsanity.com/paper/1803.07517/) (2018)
- [In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction](https://mlsanity.com/paper/2005.04176/) (2020)
- [An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning](https://mlsanity.com/paper/2306.15786/) (2023)
- [Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability](https://mlsanity.com/paper/2206.10610/) (2022)
- [Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI](https://mlsanity.com/paper/2106.07483/) (2021)
- [Towards Quantification of Explainability in Explainable Artificial Intelligence Methods](https://mlsanity.com/paper/1911.10104/) (2019)
- [Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy](https://mlsanity.com/paper/2201.09932/) (2022)
- [Fair-by-design explainable models for prediction of recidivism](https://mlsanity.com/paper/1910.02043/) (2019)
- [Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models](https://mlsanity.com/paper/2203.16073/) (2022)

## 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.