24 september 2025 – aktualizované pred 9 mesiacov
Business leaders and policymakers are considering the role AI assessments can play in supporting their AI governance objectives. While the current AI assessment landscape presents some clear challenges, a diverse group of stakeholders are working to address these issues. The development of this ecosystem is important. If properly designed and conducted in a careful and objective manner, AI assessments can help businesses assess the reliability of their AI systems and promote the trust and confidence in AI needed to realize its potential.
AI assessments: enhancing confidence in AI
Year of publication
2025
External URL
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Thank you for sharing this – I really appreciate how clearly you frame AI assessments as a way to earn confidence rather than just declare it.
One extra dimension I’ve been working on that might complement this is ontological honesty: not just how a system behaves (accuracy, robustness, bias, etc.), but what it presents itself as to end-users, especially in high-affect contexts (companions, tutors, “therapy-like” systems). In practice there can be a big gap between a model’s nature (statistics over text, product incentives, safety limits) and its representation (friendly persona, caring language, “always here for you”), and that gap strongly affects trust, attachment and over-reliance.
I’ve been developing a framework called Reality-Aligned Intelligence (RAI) that proposes simple, auditable metrics for this “N/R gap” and for relational drift risk, particularly for minors and vulnerable adults. The idea is not to replace existing assessment regimes, but to add a semantic/relational layer on top of them:
– RAI metaframework:https://doi.org/10.5281/zenodo.17686975
– RAI for Minors (misrepresentation & artificial intimacy):https://doi.org/10.5281/zenodo.17735576
– Reality-Aligned Auditing (RAA) – governance stack:https://doi.org/10.5281/zenodo.17814922
Would be very interested to explore how an “ontological honesty / artificial intimacy” module could plug into the kind of assessment ecosystem you describe, especially for AI systems that users experience less as tools and more as quasi-relationships.