# ECCOLA -- a Method for Implementing Ethically Aligned AI Systems

arXiv: https://arxiv.org/abs/2004.08377 · HTML: https://mlsanity.com/paper/2004.08377/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts](https://mlsanity.com/paper/2407.17374/) (2024)
- [Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges](https://mlsanity.com/paper/2307.10057/) (2023)
- [Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI](https://mlsanity.com/paper/2411.08881/) (2024)
- [A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance](https://mlsanity.com/paper/2503.04739/) (2025)
- [RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems](https://mlsanity.com/paper/2306.01774/) (2023)
- [Responsible Design Patterns for Machine Learning Pipelines](https://mlsanity.com/paper/2306.01788/) (2023)
- [Environmental and Social Sustainability of Creative-Ai](https://mlsanity.com/paper/2209.12879/) (2022)
- [Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice](https://mlsanity.com/paper/2503.04785/) (2025)
- [Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems](https://mlsanity.com/paper/2401.09473/) (2024)

## Abstract (arXiv)

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.