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SUMMARY:UiB AI #2 But\, why? - make AI answer!
DESCRIPTION:Welcome to the second seminar in the UiB AI seminar series.  The event is open to all employees and students at UiB. Prior registration is required.  \nTITLE: UiB AI #2 But\, why?  – make AI answer! \nWHEN: Friday 8 April\, 10:00-12:00WHERE: Universitetsaulaen\, Muséplassen 3\, Bergen \nSeminar registration \nBackground: \nArtificial intelligence (AI) is increasingly involved in decision making. Decision-making can be a monotonous task that involves a lot of routine operations and the processing of vast amounts of information. Involving artificial intelligence is a possibility to spare human time and allow for it to be used in a more meaningful way\, both for society and for the individuals involved. \nDecisions by AI can mean many different things. On one hand\, machine learning is used to identify who is most likely to pass the exam\, how much your house will be appraised for\, or how likely malignant that spot is on your mammography image.  On the other hand\, automated decision-makers are built to follow a specific set of rules. When a decision affects our life\, we would like to know how and why that decision was made. Knowing why helps scientists and engineers improve the automated decision-making tools. It also helps individuals to retain their autonomy. If you do not know why\, you cannot possibly do anything to change a decision. Not knowing why makes the personal experience the same as being subjected to a roll of a dice deciding the value of your property and the quality of your life. \nTo explain means to provide information about a process that is meaningful\, useful and understandable to the person for whom it is intended. Not all AI methods `shed’ enough information for a meaningful explanation to be feasible. It is not that the why exists somewhere and the AI method would not admit to it. Machine learning algorithms produce models of correlations in the data. The data is a numerical representation of the real world. There might be a reason in the real world why two phenomena are related. A machine learning model can correctly identify that relation without having access to\, or making use of\, the reasons for it. AI methods that rely on symbolic representations by design produce `reason based’ decisions. However\, those reasons are not explanations\, just the material from which explanations are built. \nHow do we build AI that explains its decisions? There are numerous challenges to be addressed both in providing material for explanations and constructing explanations. Ultimately\, some AI approaches would always be more explanation friendly than others. One can break a walnut with a sledgehammer\, but we do not use sledgehammers for this purpose because they tend to destroy the walnut. Analogously\, an AI approach can be used for a decision-making purpose\, but its explainability should be matched with a consideration of what impact do the produced decisions have. Otherwise\, we risk breaking something we cherish. \nIn this seminar we will describe how AI – sees the world and makes decisions. We will elucidate what happens when we say the AI reasons and the AI learns. We will discuss how researchers are trying to change different AI methods to gain more explainability from AI. \nBIO: \nSamia Touileb is a researcher at MediaFutures working on Norwegian Language Technologies. Her main research interests are information extraction\, sentiment analysis\, bias and fairness in NLP\, and applications of NLP and machine learning methods to tasks within social science research. She holds a PhD in Information Science with a focus on Natural Language Processing (NLP) from the University of Bergen\, was a Postdoc at the Language Technology Group at the University of Oslo\, and has been working within research in and applications of AI and NLP for almost a decade. \nGhazaal Sheikhi is a Postdoctoral Fellow at MediaFutures. Her research interests revolve around machine learning\, natural language processing and textual content analysis. She holds a PhD in Computer engineering (Machine Learning) from Eastern Mediterranean University\, North Cyprus and a master’s degree in Biomedical Engineering from Amirkabir University of Technology\, Teheran\, Iran.
URL:https://mediafutures.no/event/uib-ai-2-but-why-make-ai-answer/
CATEGORIES:Events,Seminar
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SUMMARY:MediaFutures Seminar: Fairness—Are algorithms a burden or a solution? Dr. Christine Bauer\, Assistant Professor at Utrecht University
DESCRIPTION:Dr. Christine Bauer\, Assistant Professor at Utrecht University\, will give a seminar on 21 April\, at 13:00. \nTITLE:  Fairness—Are algorithms a burden or a solution?WHEN: Thursday 21 April\, 13:00-14:00WHERE: Zoom – \nhttps://uib.zoom.us/j/66369080035?pwd=MDFzdmV6TUdCVVZlZnhsNWc1eHlMUT09  \nMeeting ID: 663 6908 0035\n\nPassword: F9fN181n\n \nABSTRACT: \nRecommender systems play an important role in everyday life. These systems assist users in choosing products to buy\, movies to watch\, or news articles to read. With their wide usage\, there is an increasing pressure that such systems are fair. Besides serving diverse groups of users\, recommenders need to represent and serve item providers in a fair manner\, too. But what is fair? In this talk\, I will present research on fairness in music recommender systems taking the artists’ perspective. What do artists consider fair? Are algorithms a burden or a solution? In particular\, I will zoom in on recent research on gender bias in music recommenders and how we can address this issue. \nBIO: \nDr. Christine Bauer is an Assistant Professor at Utrecht University\, The Netherlands. She is an experienced teacher in a wide spectrum of topics in computing and information systems—ranging from algorithms to adaptive interactive systems to research methods. Her research activities center on interactive intelligent systems. Thereby\, she takes a human-centered computing approach\, where technology follows humans’ and society’s needs. Central themes in her research are context and context-adaptivity.  In the recent years\, she worked on context-aware recommender systems. Core interest in her current research activities are fairness and multi-method evaluations. Further information can be found at https://christinebauer.eu.
URL:https://mediafutures.no/event/mediafutures-seminar-fairness-are-algorithms-a-burden-or-a-solution-dr-christine-bauer-assistant-professor-at-utrecht-university/
CATEGORIES:Events,Seminar,WP2 User Modeling, Personalisation & Engagement
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