BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21489@www.clsp.jhu.edu DTSTAMP:20240329T010441Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSince it is increasingly harder to opt out from inter acting with AI technology\, people demand that AI is capable of maintainin g contracts such that it supports agency and oversight of people who are r equired to use it or who are affected by it. To help those people create a mental model about how to interact with AI systems\, I extend the underly ing models to self-explain—predict the label/answer and explain this predi ction. In this talk\, I will present how to generate (1) free-text explana tions given in plain English that immediately tell users the gist of the r easoning\, and (2) contrastive explanations that help users understand how they could change the text to get another label.\nBiography\nAna Marasovi ć is a postdoctoral researcher at the Allen Institute for AI (AI2) and the Paul G. Allen School of Computer Science & Engineering at University of W ashington. Her research interests broadly lie in the fields of natural lan guage processing\, explainable AI\, and vision-and-language learning. Her projects are motivated by a unified goal: improve interaction and control of the NLP systems to help people make these systems do what they want wit h the confidence that they’re getting exactly what they need. Prior to joi ning AI2\, Ana obtained her PhD from Heidelberg University.\nHow to pronou nce my name: the first name is Ana like in Spanish\, i.e.\, with a long “a ” like in “water”\; regarding the last name: “mara” as in actress mara wil son + “so” + “veetch”. DTSTART;TZID=America/New_York:20220228T120000 DTEND;TZID=America/New_York:20220228T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Ana Marasović (Allen Institute for AI & University of Washington) “ Self-Explaining for Intuitive Interaction with AI” URL:https://www.clsp.jhu.edu/events/ana-marasovic-allen-institute-for-ai-un iversity-of-washington-self-explaining-for-intuitive-interaction-with-ai/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nSince it is increasingly harder to opt out from inter acting with AI technology\, people demand that AI is capable of maintainin g contracts such that it supports agency and oversight of people who are r equired to use it or who are affected by it. To help those people create a mental model about how to interact with AI systems\, I extend the underly ing models to self-explain—predict the label/answer and explain this predi ction. In this talk\, I will present how to generate (1) free-text explana tions given in plain English that immediately tell users the gist of the r easoning\, and (2) contrastive explanations that help users understand how they could change the text to get another label.
\nBiograph y
\nAna Marasović is a postdoctoral researcher at the Allen Institute for AI (AI2) and the Paul G. Allen School of Computer Science & Engineering at University of Washington. Her research interests broadly l ie in the fields of natural language processing\, explainable AI\, and vis ion-and-language learning. Her projects are motivated by a unified goal: i mprove interaction and control of the NLP systems to help people make thes e systems do what they want with the confidence that they’re getting exact ly what they need. Prior to joining AI2\, Ana obtained her PhD from Heidel berg University.
\nHow to pronounce my name: the first name i s Ana like in Spanish\, i.e.\, with a long “a” like in “water”\; regarding the last name: “mara” as in actress mara wilson + “so” + “veetch”.
\n< /BODY> X-TAGS;LANGUAGE=en-US:2022\,February\,Marasovic END:VEVENT BEGIN:VEVENT UID:ai1ec-22394@www.clsp.jhu.edu DTSTAMP:20240329T010441Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\n\nModel robustness and spurious correlations have rec eived increasing attention in the NLP community\, both in methods and eval uation. The term “spurious correlation” is overloaded though and can refer to any undesirable shortcuts learned by the model\, as judged by domain e xperts.\n\n\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, man y features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image . In contrast\, they carry important information that’s needed to make pre dictions by humans. In this talk\, we argue that it is more productive to characterize features in terms of their necessity and sufficiency for pred iction. We then discuss the implications of this categorization in represe ntation\, learning\, and evaluation.\nBiography\nHe He is an Assistant Pro fessor in the Department of Computer Science and the Center for Data Scien ce at New York University. She obtained her PhD in Computer Science at the University of Maryland\, College Park. Before joining NYU\, she spent a y ear at AWS AI and was a post-doc at Stanford University before that. She i s interested in building robust and trustworthy NLP systems in human-cente red settings. Her recent research focus includes robust language understan ding\, collaborative text generation\, and understanding capabilities and issues of large language models. DTSTART;TZID=America/New_York:20221014T120000 DTEND;TZID=America/New_York:20221014T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:He He (New York University) “What We Talk about When We Talk about Spurious Correlations in NLP” URL:https://www.clsp.jhu.edu/events/he-he-new-york-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nModel robustness and spuri ous correlations have received increasing attention in the NLP community\, both in methods and evaluation. The term “spurious correlation” is overlo aded though and can refer to any undesirable shortcuts learned by the mode l\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image. In contra st\, they carry important information that’s needed to make predictions by humans. In this talk\, we argue that it is more productive to characteriz e features in terms of their necessity and sufficiency for prediction. We then discuss the implications of this categorization in representation\, l earning\, and evaluation.
\nBiography
\nHe He is an Assistant Professor in the Department of Computer Science and the C enter for Data Science at New York University. She obtained her PhD in Com puter Science at the University of Maryland\, College Park. Before joining NYU\, she spent a year at AWS AI and was a post-doc at Stanford Universit y before that. She is interested in building robust and trustworthy NLP sy stems in human-centered settings. Her recent research focus includes robus t language understanding\, collaborative text generation\, and understandi ng capabilities and issues of large language models.
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