ECCOLA -- a Method for Implementing Ethically Aligned AI Systems
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2020.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 3; 2025: 2.
9 papers.
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Expertscited by 10
- 2023Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challengescited by 6
- 2024Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AIcited by 6
- 2025A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governancecited by 5
- 2023RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systemscited by 4
- 2023Responsible Design Patterns for Machine Learning Pipelinescited by 4
- 2022Environmental and Social Sustainability of Creative-Aicited by 3
- 2025Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practicecited by 1
- 2024Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systemscited by 0
- 2025Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice27 Feb 2025
- 2025A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance4 Feb 2025
- 2024Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI25 Oct 2024
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts24 Jul 2024
- 2024Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems12 Jan 2024
- 2023Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges14 Jul 2023
- 2023Responsible Design Patterns for Machine Learning Pipelines31 May 2023
- 2023RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems29 May 2023
- 2022Environmental and Social Sustainability of Creative-Ai26 Sep 2022