As artificial intelligence continues to reshape how evidence is generated, analysed, and used, the question of trust in evaluation is becoming increasingly central and more urgent in contexts where the consequences of weak or unreliable evidence are most significant. This session brings together complementary perspectives from the EvalforEarth Community of Practice to examine how evaluation can remain credible, ethical, and fit for purpose in AI-supported environments. The discussion highlights two interrelated dimensions. The first focuses on the design of trustworthy evaluation systems, emphasizing that credibility cannot be retrofitted but must be embedded from the outset. This includes clearly defining decision needs, calibrating appropriate levels of rigor in increasingly data-rich contexts, and preserving the collective and interpretive dimensions of evidence use that remain essential to meaningful evaluation practice. The second dimension examines the expanding role of AI across the evaluation cycle, identifying ethical and methodological risks related to transparency, bias, data protection, and the potential erosion of professional judgement particularly in contexts where unequal access to technology and external control of digital systems may pose additional challenges.
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