Inclusion and intersectionality in AI-enabled evaluations

This session, organized by the UN Network for Evaluation Systems Strengthening in Africa (UNNESSA), an interagency network, examines inclusion and intersectionality in evaluation as a question of power, not only participation. It begins by clarifying what “inclusion” and “intersectionality” mean in evaluative practice, then explores how exclusion can be (re)produced at key points in the evaluation cycle: who sets questions and priorities, whose knowledge is treated as credible, which methods and evidence hierarchies are labelled “rigorous,” who validates findings, and how results are communicated and used in this AI era.

Grounded in practical examples of “invisible” exclusion points, panelists apply intersectionality as a working lens to map who is excluded, how, and why across overlapping axes of identity and power (e.g., gender, race, class, disability, geography, language, migration status, colonial histories). Aligning with gLOCAL 2026’s theme “Evaluation, Evidence, and Trust in the Age of AI”, the session frames exclusion as an ethical challenge that can be intensified or mitigated by AI-enabled tools and workflows, raising critical questions about ethics, accountability, and credibility.
The session closes with a short diagnostic exercise to help participants identify exclusion risks (including AI-related risks), pinpoint remedies, and define concrete redesign actions that shift voice, influence, and decision-making towards those most affected.