Etanalet

Etanalet is a neologism used in discussions of artificial intelligence and ethics to denote a theoretical construct that integrates ethical evaluation into the core analytics of an automated system. It is not a standardized term and has no single agreed definition.

Conceptually, an etanalet comprises an ethical evaluation module that assesses decisions against an evolving normative framework, a decision engine that incorporates ethical signals into action selection, and a knowledge base that can be updated to reflect policy changes or social values.

The term appears in late 2010s and early 2020s techno-ethics literature, where authors use it to discuss how to embed moral considerations into automation without unduly sacrificing performance. Some sources treat the etanalet as a conceptual ideal rather than a concrete technology.

In practical terms, an etanalet architecture would typically include data inputs, an ethical evaluation module that analyzes potential outcomes, harms, and equity implications, and a scoring or flagging mechanism. The decision engine would weigh these signals alongside conventional metrics to guide action.

Applications discussed in scholarly and industry contexts include healthcare AI for triage or treatment recommendations, automated hiring or lending with bias checks, and policy simulations that test normative impacts. Proponents argue the approach improves transparency and governance.

Critics warn that ethical scoring can be inherently subjective, culturally contingent, and difficult to operationalize. There are concerns about norm drift, impact on innovation, and the risk of over-constraining systems if the ethical framework is incomplete or biased.

See also: explainable AI, ethical AI, value alignment.