3 research outputs found
Quantitative study about the estimated impact of the AI Act
With the Proposal for a Regulation laying down harmonised rules on Artificial
Intelligence (AI Act) the European Union provides the first regulatory document
that applies to the entire complex of AI systems. While some fear that the
regulation leaves too much room for interpretation and thus bring little
benefit to society, others expect that the regulation is too restrictive and,
thus, blocks progress and innovation, as well as hinders the economic success
of companies within the EU. Without a systematic approach, it is difficult to
assess how it will actually impact the AI landscape. In this paper, we suggest
a systematic approach that we applied on the initial draft of the AI Act that
has been released in April 2021. We went through several iterations of
compiling the list of AI products and projects in and from Germany, which the
Lernende Systeme platform lists, and then classified them according to the AI
Act together with experts from the fields of computer science and law. Our
study shows a need for more concrete formulation, since for some provisions it
is often unclear whether they are applicable in a specific case or not. Apart
from that, it turns out that only about 30\% of the AI systems considered would
be regulated by the AI Act, the rest would be classified as low-risk. However,
as the database is not representative, the results only provide a first
assessment. The process presented can be applied to any collections, and also
repeated when regulations are about to change. This allows fears of over- or
under-regulation to be investigated before the regulations comes into effect.Comment: The raw data and the various categorizations (including the
preprocessing steps) are submitted as wel
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
Previous research in Explainable Artificial Intelligence (XAI) suggests that
a main aim of explainability approaches is to satisfy specific interests,
goals, expectations, needs, and demands regarding artificial systems (we call
these stakeholders' desiderata) in a variety of contexts. However, the
literature on XAI is vast, spreads out across multiple largely disconnected
disciplines, and it often remains unclear how explainability approaches are
supposed to achieve the goal of satisfying stakeholders' desiderata. This paper
discusses the main classes of stakeholders calling for explainability of
artificial systems and reviews their desiderata. We provide a model that
explicitly spells out the main concepts and relations necessary to consider and
investigate when evaluating, adjusting, choosing, and developing explainability
approaches that aim to satisfy stakeholders' desiderata. This model can serve
researchers from the variety of different disciplines involved in XAI as a
common ground. It emphasizes where there is interdisciplinary potential in the
evaluation and the development of explainability approaches.Comment: 57 pages, 2 figures, 1 table, to be published in Artificial
Intelligence, Markus Langer, Daniel Oster and Timo Speith share
first-authorship of this pape