Employ model design techniques attuned to diversity and inclusion considerations
December 22, 2023
Diverse values and cultural perspectives from multiple stakeholders and populations should be codified in mathematical models and AI system design. Model design techniques are necessarily contextual, related to the type of AI technology, the purpose and scope of the system, how users will be impacted, and so forth. However, basic steps should include incorporating input […]
Consider specific categories relevant to the AI system
December 22, 2023
For example, before embedding gender classification into a facial analysis service or incorporating gender into image labelling, it is important to consider what purpose gender is serving. Furthermore, it is important to consider how gender will be defined, and whether that perspective is unnecessarily exclusionary (for example, non-binary). Therefore, stakeholders involved in the development of […]
Identify possible systemic problems of bias and appoint a steward
December 22, 2023
At the start of the Pre-Design stage, stakeholders should identify possible systemic problems of bias such as racism, sexism, or ageism that have implications for diversity and inclusion. Main decision-makers and power holders should be identified, as this can reflect systemic biases and limited viewpoints within the organisation. A sole person responsible for algorithmic bias ̶ […]
Establish a clear rationale for data collection
November 24, 2023
For data collection involving human subjects, why, how and by whom data is being collected should be established in the Pre-Design stage. Potential data challenges or data bias issues that have implications for diversity and inclusion should be identified by key stakeholders and data scientists. For example, in the health application domain, diverse data sources […]
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.