Decision making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a demographically and disciplinarily diverse team including internal and external personnel. Specifically, teams that are directly engaged with identifying design considerations and risks include a diversity of experience, expertise, and backgrounds to ensure AI systems meet requirements beyond a narrow subset of users.
About
To enhance organizational capacity and capability for anticipating risks, AI actors should reflect a diversity of experience, expertise and backgrounds. Consultation with external personnel may be necessary when internal teams lack a diverse range of lived experiences or disciplinary expertise.
To extend the benefits of diversity, equity, and inclusion to both the users and AI actors, it is recommended that teams are composed of a diverse group of individuals who reflect a range of backgrounds, perspectives and expertise.
Without commitment from senior leadership, beneficial aspects of team diversity and inclusion can be overridden by unstated organizational incentives that inadvertently conflict with the broader values of a diverse workforce.
Actions
Organizational management can:
Define policies and hiring practices at the outset that promote interdisciplinary roles, competencies, skills, and capacity for AI efforts.
Define policies and hiring practices that lead to demographic and domain expertise diversity; empower staff with necessary resources and support, and facilitate the contribution of staff feedback and concerns without fear of reprisal.
Establish policies that facilitate inclusivity and the integration of new insights into existing practice.
Seek external expertise to supplement organizational diversity, equity, inclusion, and accessibility where internal expertise is lacking.
Transparency and Documentation
Organizations can document the following:
Are the relevant staff dealing with AI systems properly trained to interpret AI model output and decisions as well as to detect and manage bias in data?
Entities should include diverse perspectives from technical and non-technical communities throughout the AI life cycle to anticipate and mitigate unintended consequences including potential bias and discrimination.
Stakeholder involvement: Include diverse perspectives from a community of stakeholders throughout the AI life cycle to mitigate risks.
Strategies to incorporate diverse perspectives include establishing collaborative processes and multidisciplinary teams that involve subject matter experts in data science, software development, civil liberties, privacy and security, legal counsel, and risk management.
To what extent are the established procedures effective in mitigating bias, inequity, and other concerns resulting from the system?
