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<p class="MsoNormal"><span style="font-size:10.5pt">***apologies if you receive this more than once***<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:10.5pt">Please see details below of a seminar on Explainable AI & ML taking place tomorrow, Wednesday 20 October, from 14:00-15:45.
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<p class="MsoNormal"><span style="font-size:10.5pt;color:black">We are delighted to announce our Autumn Seminar Series! </span><b><span style="color:black"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ai3sd.org%2Fai3sd-online-seminar-series%2Fautumn-seminar-series-2021%2F&data=04%7C01%7Csdd1%40soton.ac.uk%7C66459109557e4ea9d13608d9924739f0%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637701656867387850%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=ijZwey%2FR3Zz8zN93UQIHYTO0pTu3SrG1bYcjWSbdfNE%3D&reserved=0"><span style="font-size:10.5pt">https://www.ai3sd.org/ai3sd-online-seminar-series/autumn-seminar-series-2021/</span></a></span></b><b><span style="font-size:10.5pt;color:black"> </span></b><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:10.5pt;color:black">You can sign up for this event here: </span></b><strong><span style="font-family:"Calibri",sans-serif;color:black"><a href="https://ai3sd-autumn-series-201021.eventbrite.co.uk" target="_blank"><span style="font-size:10.5pt">https://ai3sd-autumn-series-201021.eventbrite.co.uk</span></a></span></strong><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="color:black">This event consists of two talks: <o:p></o:p></span></p>
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<p class="MsoNormal"><strong><span style="font-family:"Calibri",sans-serif;color:black">How can Explainable AI help scientific exploration? – </span></strong><b><span style="color:black"><a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fexplanations.ai%2F&data=04%7C01%7Csdd1%40soton.ac.uk%7C66459109557e4ea9d13608d9924739f0%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637701656867397845%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=Z3FI0FS4mprxalxKFwniQyw3%2BaTyarTzM2EW0wEE0ic%3D&reserved=0">Professor
Carlos Zednik</a> (Eindhoven University of Technology)</span></b><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="color:black">Abstract: </span></b><span style="color:black">Although models developed using machine learning are increasingly prevalent in scientific research, their opacity poses a threat to their utility. Explainable AI
(XAI) aims to diminish this threat by rendering opaque models transparent. But, XAI is more than just the solution to a problem--it can also play an invaluable role in scientific exploration. In this talk, I will consider different techniques from Explainable
AI to demonstrate their potential contribution to different kinds of exploratory activities. In particular, I argue that XAI tools can be used (1) to better understand what a "big data" model is a model of, (2) to engage in causal inference over high-dimensional
nonlinear systems, and (3) to generate algorithmic-level hypotheses in cognitive science and neuroscience. <o:p></o:p></span></p>
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<p class="MsoNormal"><strong><i><span style="font-family:"Calibri",sans-serif;color:black">Bio:</span></i></strong><em><span style="font-family:"Calibri",sans-serif;color:black"> My research centers on the explanation of natural and artificial cognitive systems.
Many of my articles specify norms and best-practice methods for cognitive psychology, neuroscience, and explainable AI. Others develop philosophical concepts and arguments with which to better understand scientific and engineering practice. I am the PI of
the DFG-funded project on Generalizability and Simplicity of Mechanistic Explanations in Neuroscience. In addition to my regular research and teaching, I do consulting work on the methodological, normative, and ethical constraints on artificial intelligence,
my primary expertise being transparency in machine learning. In this context I have an ongoing relationship with the research team at neurocat GmbH, and have contributed to AI standardization efforts at the German Institute for Standardization (DIN). Before
arriving in Eindhoven I was based at the Philosophy-Neuroscience-Cognition program at the University of Magdeburg, and prior to that, at the Institute of Cognitive Science at the University of Osnabrück. I received my PhD from the Indiana University Cognitive
Science Program, after receiving a Master's degree in Philosophy of Mind from the University of Warwick and a Bachelor's degree in Computer Science and Philosophy from Cornell University. You can find out more about me on Google Scholar, PhilPapers, Publons,
and Twitter.</span></em><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><i><span style="color:black">_______________________________________________________________________________________</span></i><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><strong><span style="font-family:"Calibri",sans-serif;color:black">Explainable Machine Learning for Trustworthy AI – </span></strong><span style="color:black"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fkdd.isti.cnr.it%2Fpeople%2Fgiannotti-fosca&data=04%7C01%7Csdd1%40soton.ac.uk%7C66459109557e4ea9d13608d9924739f0%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637701656867397845%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=0fWVCz1JMJ1%2BSf3k8f82oYpxl4U%2B1swJoZd2yTobiXM%3D&reserved=0"><b>Dr
Fosca Giannotti</b></a><strong><span style="font-family:"Calibri",sans-serif"> (University of Pisa)</span></strong><o:p></o:p></span></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="color:black">Abstract:</span></b><span style="color:black"> Black box AI systems for automated decision making, often based on machine learning over (big) data,
map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for the lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the
training data, which may lead to unfair or wrong decisions. The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity and understanding.
Explainable AI addresses such challenges and for years different AI communities have studied such topic, leading to different definitions, evaluation protocols, motivations, and results. This lecture provides a reasoned introduction to the work of Explainable
AI (XAI) to date, and surveys the literature with a focus on machine learning and symbolic AI related approaches. We motivate the needs of XAI in real-world and large-scale application, while presenting state-of-the-art techniques and best practices, as well
as discussing the many open challenges.<o:p></o:p></span></p>
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<p class="MsoNormal"><strong><i><span style="font-family:"Calibri",sans-serif;color:black">Bio: </span></i></strong><em><span style="font-family:"Calibri",sans-serif;color:black">Fosca Giannotti is a director of research of computer science at the Information
Science and Technology Institute “A. Faedo” of the National Research Council, Pisa, Italy. Fosca Giannotti is a pioneering scientist in mobility data mining, social network analysis and privacy-preserving data mining. Fosca leads the Pisa KDD Lab - Knowledge
Discovery and Data Mining Laboratory <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fkdd.isti.cnr.it%2F&data=04%7C01%7Csdd1%40soton.ac.uk%7C66459109557e4ea9d13608d9924739f0%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637701656867407843%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=UqS5CMa1YFCu92lWEui13FWwU0oNB3xuGUjCcWnDcZk%3D&reserved=0">http://kdd.isti.cnr.it</a>,
a joint research initiative of the University of Pisa and ISTI-CNR, founded in 1994 as one of the earliest research lab centered on data mining. Fosca's research focus is on social mining from big data: smart cities, human dynamics, social and economic networks,
ethics and trust, diffusion of innovations. She has coordinated tens of European projects and industrial collaborations. Fosca is currently the coordinator of SoBigData, the European research infrastructure on Big Data Analytics and Social Mining <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.sobigdata.eu%2F&data=04%7C01%7Csdd1%40soton.ac.uk%7C66459109557e4ea9d13608d9924739f0%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637701656867417838%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=zRyGhTX5M2GdRJKcBEg%2FJc3s1SCPjHK6DCLQ0T%2F9HI8%3D&reserved=0">http://www.sobigdata.eu</a>,
an ecosystem of ten cutting edge European research centres providing an open platform for interdisciplinary data science and data-driven innovation. Recently she is the PI of ERC Advanced Grant entitled XAI – Science and technology for the explanation of AI
decision making. She is member of the steering board of CINI-AIIS lab. On March 8, 2019 she has been features as one of the 19 Inspiring women in AI, BigData, Data Science, Machine Learning by <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fkdnuggets.com%2F&data=04%7C01%7Csdd1%40soton.ac.uk%7C66459109557e4ea9d13608d9924739f0%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637701656867417838%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=aPJV1s0ze2rEjCSBPxeO%2Ff3ko8sDVV37foJqSj2LvyE%3D&reserved=0">KDnuggets.com</a>,
the leading site on AI, Data Mining and Machine Learning <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.kdnuggets.com%2F2019%2F03%2Fwomen-ai-big-data-science-machine-learning.html&data=04%7C01%7Csdd1%40soton.ac.uk%7C66459109557e4ea9d13608d9924739f0%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637701656867427830%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=YahML%2FM1bFVvYuyDG8uGca2I1tjgQX4FydDyHS1FXtE%3D&reserved=0">https://www.kdnuggets.com/2019/03/women-ai-big-data-science-machine-learning.html</a>. </span></em><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="color:black">Best Wishes,<o:p></o:p></span></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><span style="color:black">Dr Samantha Kanza<br>
Enterprise & Research Fellow<br>
Coordinator of the AI3 Science Discovery Network+ & The Future Blood Testing Network+<br>
Faculty of Engineering and Physical Sciences<br>
University of Southampton<br>
ORCID: 0000-0002-4831-9489<o:p></o:p></span></p>
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