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The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems
By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the conceptual tools needed to operationalise AI governance in practice.
A Blueprint for Auditing Generative AI
We show how audits on these three levels, when conducted in a structured and coordinated manner, can be a feasible and effective mechanism for identifying and managing some of the ethical and social risks posed by generative AI systems.
Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimization
In this article, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well known tensions at the heart of Weberian rationalization.
Auditing of AI: Legal, Ethical and Technical Approaches
Specifically, a distinction can be made between technology-oriented audits, which focus on the properties and capabilities of AI systems, and process oriented audits, which focus on technology providers governance structures and quality management systems.
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other?
On the whole, the U. S. Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems.
AI and Social Theory
In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI based models can help synthesize knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way.
Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation
First, by describing the enforcement mechanisms included in the AIA in terminology borrowed from existing literature on AI auditing, we help providers of AI systems understand how they can prove adherence to the requirements set out in the AIA in practice.
On the Limits of Design: What Are the Conceptual Constraints on Designing Artificial Intelligence for Social Good?
The main argument of this chapter is that while design is a useful conceptual tool to shape technologies and societies, collective efforts towards designing future societies are constrained by both internal and external factors.
Ethics-Based Auditing to Develop Trustworthy AI
A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics.
