# Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification

arXiv: https://arxiv.org/abs/1907.09013 · HTML: https://mlsanity.com/paper/1907.09013/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2019.

## Built on by (most cited first, 30 of 55)

- [Fairness in Large Language Models: A Taxonomic Survey](https://mlsanity.com/paper/2404.01349/) (2024)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [Fair Representation Learning for Heterogeneous Information Networks](https://mlsanity.com/paper/2104.08769/) (2021)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Privacy and Fairness in Federated Learning: on the Perspective of Trade-off](https://mlsanity.com/paper/2306.14123/) (2023)
- [Efficient fair PCA for fair representation learning](https://mlsanity.com/paper/2302.13319/) (2023)
- [Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey](https://mlsanity.com/paper/2406.05392/) (2024)
- [How Much Automation Does a Data Scientist Want?](https://mlsanity.com/paper/2101.03970/) (2021)
- [It's Time for Artistic Correspondence in Music and Video](https://mlsanity.com/paper/2206.07148/) (2022)
- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Transitioning from Real to Synthetic data: Quantifying the bias in model](https://mlsanity.com/paper/2105.04144/) (2021)
- [Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows](https://mlsanity.com/paper/2201.07677/) (2022)
- [Mitigating Health Disparities in EHR via Deconfounder](https://mlsanity.com/paper/2210.15901/) (2022)
- [fairml: A Statistician's Take on Fair Machine Learning Modelling](https://mlsanity.com/paper/2305.02009/) (2023)
- [Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparity](https://mlsanity.com/paper/2306.08394/) (2023)
- [Towards Fairness-Aware Multi-Objective Optimization](https://mlsanity.com/paper/2207.12138/) (2022)
- [Bias Discovery in Machine Learning Models for Mental Health](https://mlsanity.com/paper/2205.12093/) (2022)
- [More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias](https://mlsanity.com/paper/2207.07723/) (2022)
- [Dataset Bias Mitigation Through Analysis of CNN Training Scores](https://mlsanity.com/paper/2106.14829/) (2021)
- [Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation](https://mlsanity.com/paper/2301.07483/) (2023)
- [Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity](https://mlsanity.com/paper/2305.19294/) (2023)
- [Benign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features](https://mlsanity.com/paper/2308.08482/) (2023)
- [Counterfactual Fairness through Transforming Data Orthogonal to Bias](https://mlsanity.com/paper/2403.17852/) (2024)
- [Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality](https://mlsanity.com/paper/2404.08230/) (2024)
- [Multi-Output Distributional Fairness via Post-Processing](https://mlsanity.com/paper/2409.00553/) (2024)
- [AFed: Algorithmic Fair Federated Learning](https://mlsanity.com/paper/2501.02732/) (2025)
- [Post-processing for Fair Regression via Explainable SVD](https://mlsanity.com/paper/2504.03093/) (2025)
- [EiFFFeL: Enforcing Fairness in Forests by Flipping Leaves](https://mlsanity.com/paper/2112.14435/) (2021)
- [Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data](https://mlsanity.com/paper/2210.13182/) (2022)

## Abstract (arXiv)

Recent research has helped to cultivate growing awareness that machine learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.