I return to one question across AI, institutions and education: what becomes invisible when a complex decision is compressed into a score? I set out my positions here, connect them to work that can be examined, and make clear what would change my mind.
I argue that equivalent reported performance does not establish equivalent action. I want evaluation to follow the score into the selection, evidence or decision it produces.
I believe the number of opportunities a system can allocate belongs in the explanation of its decisions. A model cannot take responsibility for a limit the institution chose.
I investigate why equivalence on declared performance measures need not imply equivalence in downstream action. I use RIDI to make that question inspectable, including how selection identity can change under a stated constraint. The public demonstration replays one SciFact case: the displayed retrieval metrics remain unchanged while the recorded answer changes. I use that case to examine the limits of the reported audit; I would need separate evidence to estimate the frequency or consequences of such changes in a deployment.
I want a learner to connect a claim to evidence, explain the limits of that evidence and revise a choice when a consequential constraint changes. I bring this position into iSCARB, with IMAM as a contextual design layer for language, local examples and educational purpose. I believe that context should help a student reason within the discipline while the university retains responsibility for curriculum, assessment and the meaning of readiness.
Each essay displays its first recorded date on this site, with the date of its current edition shown in the article. First-recorded dates come from the public repository history. I aim to publish one substantial essay every two weeks, alternating Arabic and English, and explain substantive revisions.