December 22, 2023

During model training and implementation, the effectiveness of bias mitigation should be evaluated and adjusted. Periodically assess bias identification processes and address any gaps. The model specification should include how and what sources of bias were identified, mitigation techniques used, and how successful mitigation was. A related performance assessment should be undertaken before model deployment.

December 22, 2023

Diverse values and cultural perspectives from multiple stakeholders and populations should be codified in mathematical models and AI system design. Basic steps should include incorporating input from diverse stakeholder cohorts, ensuring the development team embodies different kinds of diversity, establishing and reviewing metrics to capture diversity and inclusion elements throughout the AI-LC, and ensuring well-documented […]

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 […]

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 ̶ […]

December 22, 2023

Dataset suitability factors should be assessed. This includes statistical methods for mitigating representation issues, the socio-technical context of deployment, and interaction of human factors with the AI system. The question of whether suitable datasets exist that fit the purpose of the various applications, domains, and tasks for the planned AI system should be asked.

December 22, 2023

The concept of, and practices supporting, data sovereignty is a critical element in the AI ecosystem. It covers considerations of the “use, management and ownership of AI to house, analyze and disseminate valuable or sensitive data”. Although definitions are context-dependent, operationally data sovereignty refers to stakeholders within an AI ecosystem, ad other relevant representatives from outside stakeholder cohorts to be included as partners throughout the AI-LC. Data sovereignty should be explored from and with the perspectives of those whose data is being used. These alternative and diverse perspectives can be captured and fed back into AI Literacy programs, exemplifying how people can affect and enrich AI both conceptually and materially. Various Indigenous technologists, researchers, artists, and activists have progressed the concept of, and protocols for, Indigenous data sovereignty in AI. This involves “Indigenous control over the protection and use of data that is collected from our communities, including statistics, cultural knowledge and even user data,” and moving beyond the representation of impacted users to “maximising the generative capacity of truly diverse groups.”

December 18, 2023

Users should have accessible mechanisms to identify and report harmful or concerning AI system incidents and impacts, with such warnings shareable among relevant stakeholders. Feedback should be continuously incorporated into system updates and communicated to relevant stakeholders.

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 […]

November 24, 2023

New or emergent stakeholder cohorts should participate in system monitoring and retraining. Stakeholders should be involved in a final review and sign-off, particularly if their input propelled significant changes in design or development processes. After validation, teams should obtain informed consent on the developed product features from impacted stakeholders, to track and respond to the […]

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.