# A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems arXiv: https://arxiv.org/abs/2305.06055 ยท HTML: https://mlsanity.com/paper/2305.06055/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 85 references that are in the map. - First posted on arXiv 10 May 2023. ## Builds on (most cited first, 30 of 37) - [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017) - [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020) - [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018) - [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020) - [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017) - [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015) - [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019) - [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018) - [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020) - [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018) - [To be Robust or to be Fair: Towards Fairness in Adversarial Training](https://mlsanity.com/paper/2010.06121/) (2020) - [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017) - [The Social Cost of Strategic Classification](https://mlsanity.com/paper/1808.08460/) (2018) - [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017) - [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015) - [How Do Classifiers Induce Agents To Invest Effort Strategically?](https://mlsanity.com/paper/1807.05307/) (2018) - [The Disparate Effects of Strategic Manipulation](https://mlsanity.com/paper/1808.08646/) (2018) - [How Do Fair Decisions Fare in Long-term Qualification?](https://mlsanity.com/paper/2010.11300/) (2020) - [On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning](https://mlsanity.com/paper/1903.01209/) (2019) - [A Short-term Intervention for Long-term Fairness in the Labor Market](https://mlsanity.com/paper/1712.00064/) (2017) - [Performative Power](https://mlsanity.com/paper/2203.17232/) (2022) - [The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally](https://mlsanity.com/paper/1910.04123/) (2019) - [Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness](https://mlsanity.com/paper/1905.00569/) (2019) - [From Fair Decision Making to Social Equality](https://mlsanity.com/paper/1812.02952/) (2018) - [Equal Opportunity in Online Classification with Partial Feedback](https://mlsanity.com/paper/1902.02242/) (2019) - [Fairness in Learning-Based Sequential Decision Algorithms: A Survey](https://mlsanity.com/paper/2001.04861/) (2020) - [Algorithmic Collective Action in Machine Learning](https://mlsanity.com/paper/2302.04262/) (2023) - [On the Moral Justification of Statistical Parity](https://mlsanity.com/paper/2011.02079/) (2020) ## Built on by (most cited first, 13 of 13) - [Stop Automating Peer Review Without Rigorous Evaluation](https://mlsanity.com/paper/2605.03202/) (2026) - [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024) - [Robustness and Cybersecurity in the EU Artificial Intelligence Act](https://mlsanity.com/paper/2502.16184/) (2025) - [Towards a Systems Theory of Algorithms](https://mlsanity.com/paper/2401.14029/) (2024) - [Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists](https://mlsanity.com/paper/2404.04269/) (2024) - [Strategic Linear Contextual Bandits](https://mlsanity.com/paper/2406.00551/) (2024) - [Harm Mitigation in Recommender Systems under User Preference Dynamics](https://mlsanity.com/paper/2406.09882/) (2024) - [FairSense: Long-Term Fairness Analysis of ML-Enabled Systems](https://mlsanity.com/paper/2501.01665/) (2025) - [Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation](https://mlsanity.com/paper/2604.15937/) (2026) - [The Gender Code: Gendering the Global Governance of Artificial Intelligence](https://mlsanity.com/paper/2512.09570/) (2025) - [When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning](https://mlsanity.com/paper/2601.04447/) (2026) - [Actions Have Consequences: Detecting Outcome Performativity using Intervention Testing](https://mlsanity.com/paper/2607.26908/) (2026) - [Algorithmic Fairness Over Time: Advances & Prospects](https://mlsanity.com/paper/2610.15015/) (2026) ## Abstract (arXiv) Prediction-based decision-making systems are becoming increasingly prevalent in various domains. Previous studies have demonstrated that such systems are vulnerable to runaway feedback loops, e.g., when police are repeatedly sent back to the same neighborhoods regardless of the actual rate of criminal activity, which exacerbate existing biases. In practice, the automated decisions have dynamic feedback effects on the system itself that can perpetuate over time, making it difficult for short-sighted design choices to control the system's evolution. While researchers started proposing longer-term solutions to prevent adverse outcomes (such as bias towards certain groups), these interventions largely depend on ad hoc modeling assumptions and a rigorous theoretical understanding of the feedback dynamics in ML-based decision-making systems is currently missing. In this paper, we use the language of dynamical systems theory, a branch of applied mathematics that deals with the analysis of the interconnection of systems with dynamic behaviors, to rigorously classify the different types of feedback loops in the ML-based decision-making pipeline. By reviewing existing scholarly work, we show that this classification covers many examples discussed in the algorithmic fairness community, thereby providing a unifying and principled framework to study feedback loops. By qualitative analysis, and through a simulation example of recommender systems, we show which specific types of ML biases are affected by each type of feedback loop. We find that the existence of feedback loops in the ML-based decision-making pipeline can perpetuate, reinforce, or even reduce ML biases.