Publicaties

The combination of "Big Data " and Artificial Intelligence (AI) is frequently promoted as having the potential to deliver valuable health benefits when applied to medical decision-making. However, the responsible adoption of AI-based clinical decision support systems faces several challenges at both the individual and societal level. One of the features that has given rise to particular concern is the issue of explainability, since, if the way ...

Can satisfactory explanations for complex machine learning models be achieved in high-risk automated decision-making? How can such explanations be integrated into a data protection framework safeguarding a right to explanation? This article explores from an interdisciplinary point of view the connection between existing legal requirements for the explainability of AI systems set out in the General Data Protection Regulation (GDPR) and the ...

Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the ...

Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the ...

Artificial Intelligence (AI) systems involve diverse components, such as data, models, users and predicted outcomes. To elucidate these different aspects of AI systems, multifaceted explanations that combine diverse explainable AI (XAI) methods are beneficial. However, popularly adopted user-centric XAI evaluation methods do not measure these explanations across the different components of the system. In this position paper, we advocate for an ...

Artificial Intelligence (AI) has been at the forefront of recent technological advancements. Various sectors have started integrating AI into their workflows and this transformation is expected to increase at an accelerating pace. However, certain industries have been struggling to adopt AI despite its promising potential documented in the literature. Especially industrial processes with strict constraints and infrastructural inertia show a ...

The attempt to concretely define the concept of explainability in terms of other vaguely described notions, is doomed to fail. We argue that a rigorous numeric measure is needed to take a significant step forward. Such a measure would allow the objective comparison of machine learning models that are explainable to varying degrees. In the longer term, a numeric measure might make it possible to compare humans and machines in terms of explanatory ...

The adoption of new data-driven methods has been transforming both public and private sectors. Within the context of law enforcement, the utilisation of big data and big data analytics is fundamentally altering decision-making processes at operational and strategic levels. Specifically, place-based big data policing, i.e. using big data sources to predict when and where specific crime types are likely to occur, has broadened discretionary powers ...

Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when (1) they have no, partial, and extensive informational inputs for ...