mlsanityData to 7 Oct 2026

Algorithmic Monoculture and Social Welfare

arXiv 2101.05853 ↗First posted 14 Jan 2021cs.GT cs.CY cs.LGcited by 98

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Abstract (arXiv)

As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under "normal" operations, and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions, and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.

Key facts. Cited by 98 papers in the map (data through 7 Oct 2026). Builds on 1 of its 27 references that are in the map. First posted on arXiv 14 Jan 2021.

Builds on

1 of 27 references.

References · 1

  1. 2016Pairwise Choice Markov Chainscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 12; 2024: 15; 2025: 20; 2026: 45.

98 papers.

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  1. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  2. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  3. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
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  6. 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 144
  7. 2024Homogenization Effects of Large Language Models on Human Creative Ideationcited by 127
  8. 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?cited by 89
  9. 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 81
  10. 2023EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteriacited by 73
  11. 2024On the Societal Impact of Open Foundation Modelscited by 64
  12. 2024Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Editscited by 49
38 more
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  4. 2025The Homogenizing Effect of Large Language Models on Human Expression and Thoughtcited by 35
  5. 2023Ecosystem Graphs: The Social Footprint of Foundation Modelscited by 31
  6. 2024Generative Monoculture in Large Language Modelscited by 21
  7. 2026Brittlebench: Quantifying LLM robustness via prompt sensitivitycited by 19
  8. 2023The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictionscited by 17
  9. 2023Improved Bayes Risk Can Yield Reduced Social Welfare Under Competitioncited by 14
  10. 2023Improving Interpersonal Communication by Simulating Audiences with Language Modelscited by 14
  11. 2024Human Expertise in Algorithmic Predictioncited by 13
  12. 2026Stop Automating Peer Review Without Rigorous Evaluationcited by 12
  13. 2025AI Safety Should Prioritize the Future of Workcited by 11
  14. 2022Making Intelligence: Ethical Values in IQ and ML Benchmarkscited by 10
  15. 2023Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomescited by 10
  16. 2026When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Modelscited by 10
  17. 2023On the Richness of Calibrationcited by 9
  18. 2024Growing a Tail: Increasing Output Diversity in Large Language Modelscited by 9
  19. 2023Semantic and Expressive Variation in Image Captions Across Languagescited by 8
  20. 2025AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Servicescited by 7
  21. 2025Benchmarking Overton Pluralism in LLMscited by 7
  22. 2025Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essayscited by 6
  23. 2025Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variabilitycited by 6
  24. 2026One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributionscited by 6
  25. 2022Should attention be all we need? The epistemic and ethical implications of unification in machine learningcited by 4
  26. 2023When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-makingcited by 4
  27. 2025Evaluating multiple models using labeled and unlabeled datacited by 4
  28. 2025Value Alignment of Social Media Ranking Algorithmscited by 4
  29. 2024Statistical Models of Top-k Partial Orderscited by 3
  30. 2024Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimizationcited by 3
  31. 2024Sound Check: Auditing Audio Datasetscited by 3
  32. 2025Generating the Modal Worker: A Cross-Model Audit of Race and Gender in LLM-Generated Personas Across 41 Occupationscited by 3
  33. 2026Belief Offloading in Human-AI Interactioncited by 3
  34. 2026The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmissioncited by 3
  35. 2024Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generationcited by 2
  36. 2025Revisiting Rogers' Paradox in the Context of Human-AI Interactioncited by 2
  37. 2025Limits to AI Growth: The Ecological and Social Consequences of Scalingcited by 2
  38. 2025Epistemic diversity across language models mitigates knowledge collapsecited by 2

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