# Axes for Sociotechnical Inquiry in AI Research

arXiv: https://arxiv.org/abs/2105.06551 · HTML: https://mlsanity.com/paper/2105.06551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 89 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 27 of 27)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances](https://mlsanity.com/paper/1709.07523/) (2017)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems](https://mlsanity.com/paper/1705.01292/) (2017)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [AI Safety Gridworlds](https://mlsanity.com/paper/1711.09883/) (2017)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [The Off-Switch Game](https://mlsanity.com/paper/1611.08219/) (2016)
- [Research Priorities for Robust and Beneficial Artificial Intelligence](https://mlsanity.com/paper/1602.03506/) (2016)
- [When Will AI Exceed Human Performance? Evidence from AI Experts](https://mlsanity.com/paper/1705.08807/) (2017)
- [Machine Learning Explainability for External Stakeholders](https://mlsanity.com/paper/2007.05408/) (2020)
- [A Mulching Proposal](https://mlsanity.com/paper/1908.06166/) (2019)
- [A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance](https://mlsanity.com/paper/1811.05929/) (2018)
- [Incorrigibility in the CIRL Framework](https://mlsanity.com/paper/1709.06275/) (2017)
- [Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning](https://mlsanity.com/paper/1806.06790/) (2018)
- [What is the Point of Fairness? Disability, AI and The Complexity of Justice](https://mlsanity.com/paper/1908.01024/) (2019)
- [AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks](https://mlsanity.com/paper/2102.04255/) (2021)
- [Personal Universes: A Solution to the Multi-Agent Value Alignment Problem](https://mlsanity.com/paper/1901.01851/) (2019)

## Built on by (most cited first, 5 of 5)

- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Reward Reports for Reinforcement Learning](https://mlsanity.com/paper/2204.10817/) (2022)
- [Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses](https://mlsanity.com/paper/2410.22349/) (2024)
- [Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems](https://mlsanity.com/paper/2402.12907/) (2024)
- [Lost in Translation: Reimagining the Machine Learning Life Cycle in Education](https://mlsanity.com/paper/2209.03929/) (2022)

## Abstract (arXiv)

The development of artificial intelligence (AI) technologies has far exceeded the investigation of their relationship with society. Sociotechnical inquiry is needed to mitigate the harms of new technologies whose potential impacts remain poorly understood. To date, subfields of AI research develop primarily individual views on their relationship with sociotechnics, while tools for external investigation, comparison, and cross-pollination are lacking. In this paper, we propose four directions for inquiry into new and evolving areas of technological development: value--what progress and direction does a field promote, optimization--how the defined system within a problem formulation relates to broader dynamics, consensus--how agreement is achieved and who is included in building it, and failure--what methods are pursued when the problem specification is found wanting. The paper provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology.