News and Research articles on Explainable AI

Automated decision-making

Why AI's lack of motivation doesn't preclude legal explainability

Jacob Livingston Slosser, University of Copenhagen
Henrik Palmer Olsen, University of Copenhagen

PUBLISHED ON: 24 Jul 2025

This op-ed defends the Universal Inscrutability Argument by clarifying what legal explainability actually requires: justifying reasons for institutional decisions, not access to individual motivations. The argument holds that legal standards for explainability should be based on the latter, not the former.

Volume 10, Issue 2

Black box algorithms and the rights of individuals: no easy solution to the “explainability” problem

Jarek Gryz, York University
Marcin Rojszczak, Warsaw University of Technology
PUBLISHED ON: 30 Jun 2021 DOI: 10.14763/2021.2.1564

The design of modern machine learning systems should take into account not only their effectiveness in solving a given problem, but also their impact on the rights of individuals. Implementing this goal may involve applying technical solutions proven in the IT industry, such as event logs or certification frameworks.

KEYWORDS: Right to explanation, Explainable AI, Algorithmic transparency, Certification framework
Volume 9, Issue 2

Transparency in artificial intelligence

Stefan Larsson, Lund University
Fredrik Heintz, Linköping University
PUBLISHED ON: 5 May 2020 DOI: 10.14763/2020.2.1469

Introduction: transparency in AI Transparency is indeed a multifaceted concept used by various disciplines (Margetts, 2011; Hood, 2006). Recently, it has gone through a resurgence with regards to contemporary discourses around artificial intelligence (AI). For example, the ethical guidelines published by the EU Commission’s High-Level Expert Group on AI (AI HLEG) in April 2019 states transparency as one of seven key requirements for the realisation of ‘trustworthy AI’, which also has made its clear mark in the Commission’s white paper on AI, published in February 2020. In fact, “transparency” is the single most common, and one of the key five principles emphasised in the vast number – a …

KEYWORDS: Transparency in AI, Algorithmic transparency, Explainable AI, AI governance