# Explainability for identification of vulnerable groups in machine learning models

arXiv: https://arxiv.org/abs/2203.00317 · HTML: https://mlsanity.com/paper/2203.00317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [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)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Explainable Artificial Intelligence Approaches: A Survey](https://mlsanity.com/paper/2101.09429/) (2021)
- [Fairness in representation: quantifying stereotyping as a representational harm](https://mlsanity.com/paper/1901.09565/) (2019)
- [Artificial Intelligence and Legal Liability](https://mlsanity.com/paper/1802.07782/) (2018)
- [Digital Voodoo Dolls](https://mlsanity.com/paper/2105.02738/) (2021)

## Built on by (most cited first, 1 of 1)

- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)

## Abstract (arXiv)

If a prediction model identifies vulnerable individuals or groups, the use of that model may become an ethical issue. But can we know that this is what a model does? Machine learning fairness as a field is focused on the just treatment of individuals and groups under information processing with machine learning methods. While considerable attention has been given to mitigating discrimination of protected groups, vulnerable groups have not received the same attention. Unlike protected groups, which can be regarded as always vulnerable, a vulnerable group may be vulnerable in one context but not in another. This raises new challenges on how and when to protect vulnerable individuals and groups under machine learning. Methods from explainable artificial intelligence (XAI), in contrast, do consider more contextual issues and are concerned with answering the question "why was this decision made?". Neither existing fairness nor existing explainability methods allow us to ascertain if a prediction model identifies vulnerability. We discuss this problem and propose approaches for analysing prediction models in this respect.