# Algorithmic Fairness in Education

arXiv: https://arxiv.org/abs/2007.05443 · HTML: https://mlsanity.com/paper/2007.05443/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 30 of 44)

- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Large Language Models in Education: Vision and Opportunities](https://mlsanity.com/paper/2311.13160/) (2023)
- [What's in a Name? Auditing Large Language Models for Race and Gender Bias](https://mlsanity.com/paper/2402.14875/) (2024)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023)
- [DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students' Hand-Drawn Math Images](https://mlsanity.com/paper/2501.14877/) (2025)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning](https://mlsanity.com/paper/2208.01182/) (2022)
- [Beyond "Fairness:" Structural (In)justice Lenses on AI for Education](https://mlsanity.com/paper/2105.08847/) (2021)
- [Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach](https://mlsanity.com/paper/2303.03300/) (2023)
- [Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?](https://mlsanity.com/paper/2104.12920/) (2021)
- [Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity](https://mlsanity.com/paper/2305.00927/) (2023)
- [Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models](https://mlsanity.com/paper/2305.15342/) (2023)
- [Survey of Computerized Adaptive Testing: A Machine Learning Perspective](https://mlsanity.com/paper/2404.00712/) (2024)
- [Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics](https://mlsanity.com/paper/2212.02985/) (2022)
- [Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint](https://mlsanity.com/paper/2310.18593/) (2023)
- [Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War](https://mlsanity.com/paper/2307.08840/) (2023)
- [Bias Discovery in Machine Learning Models for Mental Health](https://mlsanity.com/paper/2205.12093/) (2022)
- [A review of clustering models in educational data science towards fairness-aware learning](https://mlsanity.com/paper/2301.03421/) (2023)
- [The Life Cycle of Large Language Models: A Review of Biases in Education](https://mlsanity.com/paper/2407.11203/) (2024)
- [Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling](https://mlsanity.com/paper/2411.02066/) (2024)
- [Predicting Early Dropout: Calibration and Algorithmic Fairness Considerations](https://mlsanity.com/paper/2103.09068/) (2021)
- [Equity and Fairness of Bayesian Knowledge Tracing](https://mlsanity.com/paper/2205.02333/) (2022)
- [Education in the age of Generative AI: Context and Recent Developments](https://mlsanity.com/paper/2309.12332/) (2023)
- [Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters](https://mlsanity.com/paper/2306.17575/) (2023)
- [Fairness Hub Technical Briefs: AUC Gap](https://mlsanity.com/paper/2309.12371/) (2023)
- [Deep Learning for Educational Data Science](https://mlsanity.com/paper/2404.19675/) (2024)
- [Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students](https://mlsanity.com/paper/2405.09821/) (2024)
- [Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA](https://mlsanity.com/paper/2501.04683/) (2025)
- [Optimizing LLM Annotation of Classroom Discourse through Multi-Agent Orchestration](https://mlsanity.com/paper/2603.13353/) (2026)

## Abstract (arXiv)

Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predictions and uses of these algorithmic systems. In this introduction to algorithmic fairness in education, we draw parallels to prior literature on educational access, bias, and discrimination, and we examine core components of algorithmic systems (measurement, model learning, and action) to identify sources of bias and discrimination in the process of developing and deploying these systems. Statistical, similarity-based, and causal notions of fairness are reviewed and contrasted in the way they apply in educational contexts. Recommendations for policy makers and developers of educational technology offer guidance for how to promote algorithmic fairness in education.