# Power and accountability in reinforcement learning applications to environmental policy

arXiv: https://arxiv.org/abs/2205.10911 · HTML: https://mlsanity.com/paper/2205.10911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 22 May 2022.

## Builds on (most cited first, 7 of 7)

- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning](https://mlsanity.com/paper/1912.05743/) (2019)
- [Transparency and Explanation in Deep Reinforcement Learning Neural Networks](https://mlsanity.com/paper/1809.06061/) (2018)
- [CropGym: a Reinforcement Learning Environment for Crop Management](https://mlsanity.com/paper/2104.04326/) (2021)
- [Deep Reinforcement Learning for Green Security Games with Real-Time Information](https://mlsanity.com/paper/1811.02483/) (2018)
- [Deep reinforcement learning in World-Earth system models to discover sustainable management strategies](https://mlsanity.com/paper/1908.05567/) (2019)
- [Deep Reinforcement Learning for Conservation Decisions](https://mlsanity.com/paper/2106.08272/) (2021)

## Built on by (most cited first, 1 of 1)

- [Do You Trust the Process?: Modeling Institutional Trust for Community Adoption of Reinforcement Learning Policies](https://mlsanity.com/paper/2510.22017/) (2025)

## Abstract (arXiv)

Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We leverage examples from RL applications to climate change mitigation and fisheries management to explore how RL technologies shift the distribution of power between resource users, governing bodies, and private industry.