Implementing AI Ethics: Making Sense of the Ethical Requirements
Mamia Agbese, Rahul Mohanani, Arif Ali Khan, and Pekka Abrahamsson

TL;DR
This paper explores how middle and higher-level AI management incorporate ethical requirements, highlighting a focus on privacy and data governance, and proposing a practical implementation approach using Agile portfolio management.
Contribution
It provides empirical insights into AI ethics implementation at management levels and introduces a practical approach using the ethical risk requirements stack with Agile frameworks.
Findings
Privacy and data governance are mainly seen as legal requirements.
Ethical considerations include technical robustness, safety, societal, and environmental well-being.
Implementation often focuses on risk and sustainability requirements.
Abstract
Society's increasing dependence on Artificial Intelligence (AI) and AI-enabled systems require a more practical approach from software engineering (SE) executives in middle and higher-level management to improve their involvement in implementing AI ethics by making ethical requirements part of their management practices. However, research indicates that most work on implementing ethical requirements in SE management primarily focuses on technical development, with scarce findings for middle and higher-level management. We investigate this by interviewing ten Finnish SE executives in middle and higher-level management to examine how they consider and implement ethical requirements. We use ethical requirements from the European Union (EU) Trustworthy Ethics guidelines for Trustworthy AI as our reference for ethical requirements and an Agile portfolio management framework to analyze…
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Taxonomy
TopicsEthics and Social Impacts of AI · Adversarial Robustness in Machine Learning · Artificial Intelligence in Healthcare and Education
