Towards Equitable Agile Research and Development of AI and Robotics
Andrew Hundt Julia Schuller Severin Kacianka
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.