Practice inclusive problem identification and impact assessment
November 24, 2023
A project owner (individual or organisation) with suitable expertise and resources to manage an AI system project should be identified, ensuring that accountability mechanisms to counter potential harm are built in. It should be decided which other stakeholders will be involved in the system’s development and regulation. Both intended and unintended impacts that the AI […]
Establish inclusive and informed product development, training, evaluation, and sign-off
November 24, 2023
New stakeholders for iterative rounds of product development, training, and testing should be brought in, and beta groups for test deployments should be recruited. User groups should reflect different needs and abilities. Fresh perspectives contribute to the evaluation of both the AI system’s functionality and, importantly, its level and quality of inclusivity. New or emergent […]
Establish policies for how biometric data is collected and used
November 24, 2023
Establishing policies (either at the organizational or industry level), for how biometric data and face and body images are collected and used may be the most effective way of mitigating harm to trans people—and also people of marginalized races, ethnicities, and sexualities.
Reflect collectively on key questions – AI why, for whom, and by whom?
November 24, 2023
Key questions about why an AI project should happen, for who is the project for, and by whom should it be developed should be asked, answered, and revisited collectively using a diversity and inclusion lens during the AI-LC. Views from stakeholders and representatives of impacted communities should be sought. Although it might be advantageous that […]
Identify stakeholder knowledge and needs
November 24, 2023
Stakeholders generally hold specific knowledge, expertise, concerns, and objectives that can contribute to effective AI system design. Stakeholder expectations, needs and feedback throughout the AI-LC should be considered. Cohorts include government regulatory bodies, and civil society organizations monitoring AI impact and advocating users’ rights, industry, and people affected by AI systems. There are groups whose knowledge or expertise is valuable for AI system design, but they do not necessarily have needs or requirements for the system because they will not be users or consumers. Both groups need to be involved.
Integrate diversity and inclusion principles and practice throughout the AI lifecycle
November 24, 2023
Integrating diversity and inclusion principles and practices throughout the lifecycle of AI has an important role in achieving equity for all stakeholders. In particular, the integration of diversity and inclusion principles and practices through the engagement of diverse stakeholders is important. The composition of different levels of stakeholder cohorts should maintain diversity along social lines (race, gender identification, age, ability, and viewpoints) where bias is a concern. End-users, AI practitioners, subject matter experts, and interdisciplinary professionals including those from the law, social sciences and community development should be involved to identify downstream impacts comprehensively.