Towards Implementing Responsible AI

TL;DR

This paper explores practical ways to implement responsible AI by interviewing practitioners and analyzing ethical considerations across AI system development stages, aiming to bridge high-level principles with tangible practices.

Contribution

It provides empirical insights into how AI ethics principles can be integrated into actual AI development processes through interviews with practitioners.

Findings

Identifies key ethical considerations at each development stage.

Highlights the importance of requirements engineering for responsible AI.

Suggests adapting software engineering practices for ethical AI implementation.

Abstract

As the deployment of artificial intelligence (AI) is changing many fields and industries, there are concerns about AI systems making decisions and recommendations without adequately considering various ethical aspects, such as accountability, reliability, transparency, explainability, contestability, privacy, and fairness. While many sets of AI ethics principles have been recently proposed that acknowledge these concerns, such principles are high-level and do not provide tangible advice on how to develop ethical and responsible AI systems. To gain insight on the possible implementation of the principles, we conducted an empirical investigation involving semi-structured interviews with a cohort of AI practitioners. The salient findings cover four aspects of AI system design and development, adapting processes used in software engineering: (i) high-level view, (ii) requirements engineering, (iii) design and implementation, (iv) deployment and operation.