As artificial intelligence continued to reshape how evidence was generated, analysed, and used, the question of trust in evaluation became increasingly central and more urgent, particularly in contexts where the consequences of weak or unreliable evidence were most significant. This session brought together complementary perspectives from the EvalforEarth Community of Practice to examine how evaluation could remain credible, ethical, and fit for purpose in AI-supported environments.
The discussion highlighted two interrelated dimensions. The first focused on the design of trustworthy evaluation systems, emphasizing that credibility could not be retrofitted but had to be embedded from the outset. This included 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 remained essential to meaningful evaluation practice.
The second dimension examined 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 could pose additional challenges.
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