Explizitere

Explizitere is a neologism used in discussions around transparency in automated decision-making. It describes the deliberate design principle of making internal states and decision rationales of software systems explicit to observers, rather than hidden.

Origin and usage: The word appears in niche forums and some academic papers since around the early 2020s; it is not widely standardized and is mainly used in privacy, governance, and accountability contexts.

Concept and characteristics: Explizitere emphasizes auditability and reproducibility. It often involves instrumentation, comprehensive logging, model cards, and clearly defined decision pipelines. The aim is to enable external audits and to support accountability for automated outcomes.

Relation to other concepts: It is similar to explainable AI, interpretable machine learning, and broader algorithmic transparency. A key distinction is that explizitere focuses on the explicit exposure of internal states and decision processes, rather than solely on post-hoc explanations.

Limitations and concerns: Implementing explizitere can raise security and proprietary information concerns, introduce performance and design overhead, and risk data leakage. Effective use requires balancing the benefits of transparency with the need to protect sensitive data and system integrity.

See also: Explainable AI, model cards, transparency, audit trails.