# Value-laden Disciplinary Shifts in Machine Learning

arXiv: https://arxiv.org/abs/1912.01172 · HTML: https://mlsanity.com/paper/1912.01172/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 25 of 25)

- [AI and the Everything in the Whole Wide World Benchmark](https://mlsanity.com/paper/2111.15366/) (2021)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Measuring what Matters: Construct Validity in Large Language Model Benchmarks](https://mlsanity.com/paper/2511.04703/) (2025)
- [Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research](https://mlsanity.com/paper/2112.01716/) (2021)
- [Better than classical? The subtle art of benchmarking quantum machine learning models](https://mlsanity.com/paper/2403.07059/) (2024)
- [Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking](https://mlsanity.com/paper/2106.06052/) (2021)
- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [Ecosystem Graphs: The Social Footprint of Foundation Models](https://mlsanity.com/paper/2303.15772/) (2023)
- [Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data](https://mlsanity.com/paper/2103.03399/) (2021)
- [Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency](https://mlsanity.com/paper/2105.08667/) (2021)
- [Data Representativity for Machine Learning and AI Systems](https://mlsanity.com/paper/2203.04706/) (2022)
- [Metaethical Perspectives on 'Benchmarking' AI Ethics](https://mlsanity.com/paper/2204.05151/) (2022)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [Making Intelligence: Ethical Values in IQ and ML Benchmarks](https://mlsanity.com/paper/2209.00692/) (2022)
- [Benchmark Data Repositories for Better Benchmarking](https://mlsanity.com/paper/2410.24100/) (2024)
- [Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks](https://mlsanity.com/paper/2107.10302/) (2021)
- [Perceptions and Realities of Text-to-Image Generation](https://mlsanity.com/paper/2306.08363/) (2023)
- [Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT for Mining Insights at Scale](https://mlsanity.com/paper/2306.05036/) (2023)
- [A Taxonomy of Challenges to Curating Fair Datasets](https://mlsanity.com/paper/2406.06407/) (2024)
- [Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarks](https://mlsanity.com/paper/2210.05038/) (2022)
- [Big AI is accelerating the metacrisis: What can we do?](https://mlsanity.com/paper/2512.24863/) (2025)
- [Demanding and Designing Aligned Cognitive Architectures](https://mlsanity.com/paper/2112.10190/) (2021)
- [Evaluation for Change](https://mlsanity.com/paper/2212.11670/) (2022)
- [Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem](https://mlsanity.com/paper/2604.20805/) (2026)
- [Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics](https://mlsanity.com/paper/2608.05656/) (2026)

## Abstract (arXiv)

As machine learning models are increasingly used for high-stakes decision making, scholars have sought to intervene to ensure that such models do not encode undesirable social and political values. However, little attention thus far has been given to how values influence the machine learning discipline as a whole. How do values influence what the discipline focuses on and the way it develops? If undesirable values are at play at the level of the discipline, then intervening on particular models will not suffice to address the problem. Instead, interventions at the disciplinary-level are required. This paper analyzes the discipline of machine learning through the lens of philosophy of science. We develop a conceptual framework to evaluate the process through which types of machine learning models (e.g. neural networks, support vector machines, graphical models) become predominant. The rise and fall of model-types is often framed as objective progress. However, such disciplinary shifts are more nuanced. First, we argue that the rise of a model-type is self-reinforcing--it influences the way model-types are evaluated. For example, the rise of deep learning was entangled with a greater focus on evaluations in compute-rich and data-rich environments. Second, the way model-types are evaluated encodes loaded social and political values. For example, a greater focus on evaluations in compute-rich and data-rich environments encodes values about centralization of power, privacy, and environmental concerns.