inieval

Inieval is a neologism used to describe a methodological stance in which evaluation foregrounds initial conditions, priors, or starting states when interpreting processes, events, or data. The term blends "initial" and "evaluation" and is mainly found in speculative discourse, philosophical thought experiments, and fringe discussions in data science and AI ethics.

Origins and usage: The word appears in online discussions and occasionally in conference abstracts from the late 2010s onward. It lacks a formal, widely adopted definition, and its precise meaning varies among authors, contributing to a diffuse and context-dependent usage.

Concepts and practice: An inieval approach emphasizes identifying the starting data or assumptions as the primary basis for interpretation, then testing how subsequent information would alter conclusions. It often involves making priors explicit and examining the sensitivity of results to those priors. In AI ethics, proponents may invoke inieval to demand transparent accounting of initial design choices.

Criticism and limitations: Critics argue that overemphasizing initial states can sow anchoring bias and reduce responsiveness to new evidence. Others note that in many domains, starting conditions matter but do not alone determine outcomes, making inieval a potentially misleading lens if used in isolation.

See also: cognitive bias, prior probability, hypothesis testing, historical revisionism.