Under Co-construction: Toward the Social Design of Explainable AI Systems<\/h3>\n\n\n\n
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ABSTRACT<\/strong><\/p>\n\n\n\n
A new co-constructive view on the process of explanation is required to push forward the development of explainable AI systems. Technological advancements in machine learning affecting humans\u2019 lives on the one hand and also regulatory initiatives fostering transparency in algorithmic decision making on the other hand drive a recent surge of interest in explainable AI (XAI).Explainability is discussed as a solution to socio-technical challenges such as intelligent software providing incomprehensible decisions or big data enabling fast learning but becoming too complex to fully comprehend and judge its achievements.With explainable AI, more insights into the functions, decisions, and usefulness of algorithms are expected.If an explanation is successful, it results in an understanding.Current XAI research is centering around one-way interaction from which solutions to achieve understanding are derived. In the presentation, I will point to an important resource for achieving understanding that has been overlooked so far: the interaction with the addressee. Whereas in current XAI research, the addressee (explainee) is mostly seen as a passive receiver, I argue that the explainee can provide an active and crucial contribution to the process of understanding resulting in the explanation being tailored to a particular form of understanding and thus gaining on relevance. Within the co-constructive view[1], both partners scaffold and monitor each other\u2014perpetuating, thus, the process of explaining as a joint endeavor toward a goal.I argue that such an endeavor can be implemented in explainable and interactive AI.<\/p>\n<\/div>\n\n\n\n
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BIOGRAPHY<\/strong><\/p>\n\n\n\n
Katharina J. Rohlfing received her Master\u2019s in Linguistics, Philosophy, and Media Studies from Paderborn University,Germany, in 1997. Asa member of the interdisciplinary Graduate Program \u201cTask-Oriented Communication,\u201dshe received her PhD in Linguistics from Bielefeld University in 2002.In 2006, she became a Dilthey Fellow (Volkswagen Foundation) and Head of the Emergentist Semantics Group at Bielefeld University\u2019s Cluster of Excellence Cognitive Interaction Technology (CITEC). Currently, she is professor of psycholinguistics at Paderborn University. Her work is on multimodal dialogical coordination and learning with a strong interest in cognitive modeling, developmental robotics, and HRI.<\/p>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n
Angela Sasse (Bochum, Germany)<\/h2>\n\n\n\n
Learning about Security – Who, What, When, Why and How?<\/h3>\n\n\n\n
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Security Awareness is big business – virtually every organisation in the Western world provides some form of awareness or training, mostly bought from external vendors. In the perspective that underlies the provision of this material, the \u201cWHO\u201d are non-security specialists who need to understand threats and know countermeasures (WHAT) by reading through lots of information (HOW) to deadlines mandated by others (WHEN) because security is important (WHY). But academic reviews and industry reports show that these programs have little to no effect in changing security behaviour of employees or the general public. This talk presents an alternative perspective that identifies the security experts \u201cWHO\u201d see non-security specialists who don\u2019t follow their rules as \u201cdefective\u201d and try to \u201cfix\u201d them by repeated presentation of information (HOW) that due to lack of quality control and evaluation is often ambiguous, contradictory, outdated or downright wrong (WHAT). Most organisations do not present a convincing narrative \u201cWHY\u201d security is important, and don\u2019t budget time \u201cWHEN\u201d employees can learn, nor support people the stages of behaviour change. This perspective creates us to a much broader set of actors WHO have to learn about security: WHAT it is, and HOW to work with others to achieve it. WHY? Because not doing so makes the attackers job much easier. WHEN? Arguably, we should have started some time ago.<\/p>\n<\/div>\n\n\n\n
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Prof. Dr. Angela Sasse studied psychology at the University of Wuppertal in the 1980s and continued her studies in Great Britain. She received her master’s degree in industrial psychology from the University of Sheffield and her doctorate from the University of Birmingham.<\/p>\n\n\n\n
In 1990, she began working as a lecturer in Computer Science at University College London. There she was a professor of human-centered technology from 2003. From 2012 to 2017, she directed the British Research Institute for Empirical Security Research and was inducted into the Royal Academy of Engineering in 2015.<\/p>\n\n\n\n
On May 1, 2018, she took over the chair of Human-Centred Security at the Horst G\u00f6rtz Institute for IT Security at Ruhr University Bochum.<\/p>\n<\/div>\n<\/div>\n\n\n\n
Catherine D’Ignazio (MIT, USA)<\/h2>\n\n\n\n
Data Feminism: Teaching and Learning for Justice<\/h3>\n\n\n\n
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ABSTRACT<\/strong><\/p>\n\n\n\n
As data are increasingly mobilized in the service of governments and corporations, their unequal conditions of production, their asymmetrical methods of application, and their unequal effects on both individuals and groups have become increasingly difficult for data scientists–and others who rely on data in their work–to ignore. But it is precisely this power that makes it worth asking: “Data science by whom? Data science for whom? Data science with whose interests in mind? These are some of the questions that emerge from Data Feminism(D\u2019Ignazio & Klein, MIT Press, 2020), a way of thinking about data science and its communication that is informed by the past several decades of intersectional feminist activism and critical thought. Illustrating data feminism in action, this talk will show how challenges to the male\/female binary can help to challenge other hierarchical (and empirically wrong) classification systems; it will explain how an understanding of emotion can expand our ideas about effective data visualization; how the concept of invisible labor can expose the significant human efforts required by our automated systems; and why the data never, ever \u201cspeak for themselves.\u201d Drawing from data feminism, this talk will focus on implications for teaching and learning. It features examples from the book which connect intersectional feminist theory to data literacy and innovative pedagogy for diverse audiences. This goal of this talk, as with the project of data feminism, is to model how scholarship can be transformed into action: how feminist thinking can be operationalized in order to imagine more ethical and equitable data education practices.<\/p>\n<\/div>\n\n\n\n
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BIOGRAPHY<\/strong><\/p>\n\n\n\n
Catherine D\u2019Ignazio is an Assistant Professor of Urban Science and Planning in the Department of Urban Studies and Planning at MIT. She is also Director of the Data + Feminism Lab which uses data and computational methods to work towards gender and racial equity. D\u2019Ignazio is a scholar, artist\/designer and hacker mama who focuses on feminist technology, data literacy and civic engagement. She has run reproductive justice hackathons, designed global news recommendation systems, created talking and tweeting water quality sculptures, and led walking data visualizations to envision the future of sea level rise. With Rahul Bhargava, she built the platform Databasic.io, a suite of tools and activities to introduce newcomers to data science. Her 2020 book from MIT Press, Data Feminism, co-authored with Lauren Klein, charts a course for more ethical and empowering data science practices. Her research at the intersection of technology, design & social justice has been published in the Journal of Peer Production, the Journal of CommunityInformatics, and the proceedings of Human Factors in Computing Systems (ACM SIGCHI). Her art and design projects have won awards from the Tanne Foundation, Turbulence.org and the Knight Foundation and exhibited at the Venice Biennial and the ICA Boston.<\/p>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
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