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<h1 style="text-align: center; line-height: 1.2; margin: 0px; display: block; font-family: Arial, Helvetica, sans-serif; font-size: 18px; color: rgb(230, 70, 38);">
Illusions of Explanation in Deep Learning<br>
<span style="font-size: 14px; color: rgb(0, 0, 0);"><br>
</span><span style="font-size: 15px; color: rgb(0, 0, 0);">Raphaël Millière (Macquarie University )</span><br>
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<b>Dates: </b>Monday, 09/09/2024<br>
<b>Time: </b>5:30pm<br>
<b>Venue: </b>F09.331. Madsen Building. Madsen Seminar Room 331<br>
<b>How to register: </b>Free, no registration required<br>
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<b>Abstract: </b>Recent advancements in artificial intelligence have been largely driven by deep learning. However, deep neural networks (DNNs) are often characterized as inscrutable "black boxes": while we can study their performance on various tasks, we struggle
to understand the internal mechanisms that drive it. Mechanistic interpretability has emerged as a promising approach to unveil the inner workings of DNNs by decoding the computations and representations underlying their behavior. While preliminary results
in toy models show potential, scaling these techniques to large-scale DNNs remains a challenge. Here, I investigate a serious concern about the viability of this project: the possibility of illusory explanations that appear to reveal how DNN process information
but are, in fact, misleading. I present a novel typology of such interpretability illusions, and explore potential strategies to mitigate their occurrence and impact on explanations.<br>
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<b>Bio: </b>Raphaël Millière is a Lecturer (Assistant Professor) in Philosophy of Artificial Intelligence at Macquarie University in Sydney, Australia. Prior to joining Macquarie in 2023, he was the Robert A. Burt Presidential Scholar in Society and Neuroscience
at Columbia University. His research interests lie at the intersection of philosophy, cognitive science, and artificial intelligence. His current work focuses on assessing the linguistic and reasoning abilities of large language models, drawing on philosophy
and computer science to shed light on the potential of these models to advance our understanding of human cognition.</div>
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