# Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagement

arXiv: https://arxiv.org/abs/2409.10520 · HTML: https://mlsanity.com/paper/2409.10520/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability](https://mlsanity.com/paper/2311.13158/) (2023)
- [AI and Ethics -- Operationalising Responsible AI](https://mlsanity.com/paper/2105.08867/) (2021)
- [Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development](https://mlsanity.com/paper/1910.03515/) (2019)
- [Integrating ESG and AI: A Comprehensive Responsible AI Assessment Framework](https://mlsanity.com/paper/2408.00965/) (2024)

## Abstract (arXiv)

As Artificial Intelligence (AI) becomes integral to business operations, integrating Responsible AI (RAI) within Environmental, Social, and Governance (ESG) frameworks is essential for ethical and sustainable AI deployment. This study examines how leading companies align RAI with their ESG goals. Through interviews with 28 industry leaders, we identified a strong link between RAI and ESG practices. However, a significant gap exists between internal RAI policies and public disclosures, highlighting the need for greater board-level expertise, robust governance, and employee engagement. We provide key recommendations to strengthen RAI strategies, focusing on transparency, cross-functional collaboration, and seamless integration into existing ESG frameworks.