Towards Equitable Agile Research and Development of AI and Robotics
Andrew Hundt, Julia Schuller, Severin Kacianka
TL;DR
This paper tackles the persistent problem of bias and inequity in AI, ML, and robotics by proposing an actionable framework that infuses equity, ethics, and human-centered governance into Agile R&D. It extends Scrum with identity-sensitive gating, participant-led governance, and measurable artifacts to detect, mitigate, and adapt to harms early in the development lifecycle. Key contributions include an Equity Context framework, governance mechanisms, scorecards, and education blocks designed to build organizational capability for equitable outcomes. The approach seeks to bridge academia and industry while enabling iterative improvement, with explicit attention to marginalized populations and real-world deployment impacts. The work aims to reduce harms, improve accountability, and provide a scalable toolkit adaptable to various project sizes and domains in AI and Robotics.
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.