The Language Labyrinth: Constructive Critique on the Terminology Used in the AI Discourse

TL;DR

This paper critically examines the ambiguous terminology in AI discourse, arguing that metaphors like 'training' and 'learning' distort understanding and decision-making, and proposes clearer terminology to improve debate quality.

Contribution

It offers a constructive critique of AI terminology, highlighting the need for more precise language to foster better understanding and responsible use.

Findings

Current AI metaphors distort understanding of AI capabilities

Ambiguous terminology impacts decision-making in sensitive areas

Proposes alternative terminology for clearer AI discourse

Abstract

In the interdisciplinary field of artificial intelligence (AI) the problem of clear terminology is especially momentous. This paper claims, that AI debates are still characterised by a lack of critical distance to metaphors like 'training', 'learning' or 'deciding'. As consequence, reflections regarding responsibility or potential use-cases are greatly distorted. Yet, if relevant decision-makers are convinced that AI can develop an 'understanding' or properly 'interpret' issues, its regular use for sensitive tasks like deciding about social benefits or judging court cases looms. The chapter argues its claim by analysing central notions of the AI debate and tries to contribute by proposing more fitting terminology and hereby enabling more fruitful debates. It is a conceptual work at the intersection of critical computer science and philosophy of language.