Towards Equitable Agile Research and Development of AI and Robotics

TL;DR

This paper proposes an adapted Agile R&D framework to improve fairness, equity, and ethical practices in AI and robotics development, addressing biases and harms early in the process.

Contribution

It introduces a novel framework integrating equity-focused practices into Agile R&D, specifically adapting Scrum to better address bias and fairness issues in AI and robotics.

Findings

Framework enables early detection of bias and fairness issues.

Organizational practices improve accountability and ethical awareness.

Potential for reducing incidents of bias in AI and robotics development.

Abstract

Machine Learning (ML) and 'Artificial Intelligence' ('AI') methods tend to replicate and amplify existing biases and prejudices, as do Robots with AI. For example, robots with facial recognition have failed to identify Black Women as human, while others have categorized people, such as Black Men, as criminals based on appearance alone. A 'culture of modularity' means harms are perceived as 'out of scope', or someone else's responsibility, throughout employment positions in the 'AI supply chain'. Incidents are routine enough (incidentdatabase.ai lists over 2000 examples) to indicate that few organizations are capable of completely respecting peoples' rights; meeting claimed equity, diversity, and inclusion (EDI or DEI) goals; or recognizing and then addressing such failures in their organizations and artifacts. We propose a framework for adapting widely practiced Research and Development (R&D) project management methodologies to build organizational equity capabilities and better integrate known evidence-based best practices. We describe how project teams can organize and operationalize the most promising practices, skill sets, organizational cultures, and methods to detect and address rights-based fairness, equity, accountability, and ethical problems as early as possible when they are often less harmful and easier to mitigate; then monitor for unforeseen incidents to adaptively and constructively address them. Our primary example adapts an Agile development process based on Scrum, one of the most widely adopted approaches to organizing R&D teams. We also discuss limitations of our proposed framework and future research directions.