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PhD Projects

Mario Verdicchio: Explainable Artificial Intelligence and Scientific Explanation

Neural networks present an inherent component of unknowability that makes them critical from an epistemological perspective especially when they are used in connection with scientific research. Indeed, the language of a scientific field is often, if not almost always, different from the language of neural networks, which are currently viewed as “black boxes”, providing useful output but without an explanation that traces such output back to the input. The endeavour of providing an explanation of why the network has provided a certain result is called “Explainable AI (XAI)”. The general question that this work aims to tackle is whether the explanations Explainable AI is aiming for are scientific. The investigation is based on the construction of a conceptual framework that is as general as possible regarding how explanations are created in AI. It aims to verify whether scientific explanations are created at all with Explainable AI and, if so, whether such creation can be described in traditional terms from Philosophy of Science or whether new concepts are called for.

Judith Marlen Dobler: Drawing together. Manual Drawing as Collaborative Knowledge Practice

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Taking manual drawing in the natural sciences as its point of departure, DRAWING TOGETHER investigates drawing as a collaborative practice of knowledge. The PhD project examines the complexity of media arrangements in an experimental physics laboratory by means of ethnographic and drawing research.

The project questions the practice of scientific drawing in the present as pictorial practice, epistemic practice and collaborative practice. The research questions derive from the decidedly practice-led and interdisciplinary focus: What is the function of drawing in the laboratory? What is the epistemic status of drawing? How are the drawing practices in the laboratory formed concerning various media formats and materials, digital technologies and experimental constellations? These questions lead to the methodical question of how the contemporary and hitherto invisible (design) practices of drawing can be made visible in the sciences. Furthermore, what becomes visible when the gaze is directed from the design research perspective into scientific practice?

The dissertation is an extension of the existing knowledge and practice research of drawing. Using an innovative methodological approach from discourse analysis, ethnography and drawing practice, the work is intended to be applicable to both theoretical and applied discourses.

 

 

Jessica de Jesus de Pinho Pinhal: Hacking Biases: Intersectional AI and Metaethics

AI, particularly its subfield Machine Learning, learn and apprehend our world(s) through numerical representations of the latter. When applied in our societies, these algorithms mirror their biases and discriminations. Various strategies such as machine ethics or fairness of algorithms are employed to correct, or in computer science jargon, mitigate these "errors". In our contemporary societies where democracy is supposed to set all of us equal and free, and where justice is supposedly blind to our differences, these algorithmic biases embarrass. Yet, they seem to finally shed light on prejudices and unfairness which have long been denounced by oppressed groups. Building on feminist, post-colonialist, queer and disability studies, and the new challenges brought by intelligent machines, how can we deconstruct human morality, if the concept even exists?

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