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.

机器学习和人工智能方法倾向于复制和放大现有的偏见和偏见，噢录能通过面部识别的机器人未能将黑人妇女视为人类，而其他机器人则仅基于外貌将人们如黑人男性归类为罪犯。我们提出一个框架，将广泛应用的研发项目管理方法调整为建立组织公平能力并更好地整合已知的基于证据的最佳实践。我们描述了如何组织和运作最有前景的实践、技能集、组织文化和方法，以尽早检测和解决基于权利的公平、公正、问责和道德问题。我们的主要示例是基于 Scrum 的敏捷开发过程，这是一种广泛采用的组织研发团队方法。我们还讨论了我们提出的框架的局限性和未来的研究方向。