# Fairness via Explanation Quality

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/fairness-via-explanation-quality/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jessica Dai,Sohini Upadhyay,Ulrich Aïvodji,Stephen H. Bach,Himabindu Lakkaraju |
| Citations | 52 |
| DOI | 10.1145/3514094.3534159 |
| Fields | Computer Science,Social Sciences |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2205.07277 |
| OpenAlex ID | https://openalex.org/W4280578116 |
| Type | conference-paper |
| Year | 2022 |

## Paper authors

- [Jessica Dai](https://scholariq.org/researchers/jessica-dai/)

## Paper primary topic

- [Explainable Artificial Intelligence (XAI)](https://scholariq.org/topics/explainable-artificial-intelligence-xai/)

## Paper topics

- [Explainable Artificial Intelligence (XAI)](https://scholariq.org/topics/explainable-artificial-intelligence-xai/)
- [Ethics and Social Impacts of AI](https://scholariq.org/topics/ethics-and-social-impacts-of-ai/)
- [Bayesian Modeling and Causal Inference](https://scholariq.org/topics/bayesian-modeling-and-causal-inference/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.