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:20240328T112108Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nSince it is increasingly h arder to opt out from interacting with AI technology\, people demand that AI is capable of maintaining contracts such that it supports agency and ov ersight of people who are required to use it or who are affected by it. To help those people create a mental model about how to interact with AI sys tems\, I extend the underlying models to self-explain—predict the label/an swer and explain this prediction. In this talk\, I will present how to gen erate (1) free-text explanations given in plain English that immediately t ell users the gist of the reasoning\, and (2) contrastive explanations tha t help users understand how they could change the text to get another labe l.
\nBiography
\nAna Marasović is a postdocto ral researcher at the Allen Institute for AI (AI2) and the Paul G. Allen S chool of Computer Science & Engineering at University of Washington. Her r esearch interests broadly lie in the fields of natural language processing \, explainable AI\, and vision-and-language learning. Her projects are mot ivated by a unified goal: improve interaction and control of the NLP syste ms to help people make these systems do what they want with the confidence that they’re getting exactly what they need. Prior to joining AI2\, Ana o btained her PhD from Heidelberg University.
\nHow to pronounce 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 wilso n + “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-TAGS;LANGUAGE=en-US:2022\,February\,Marasovic END:VEVENT BEGIN:VEVENT UID:ai1ec-22394@www.clsp.jhu.edu DTSTAMP:20240328T112108Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nModel robustness and spurious correlations have received increasing atten tion in the NLP community\, both in methods and evaluation. The term “spur ious correlation” is overloaded though and can refer to any undesirable sh ortcuts learned by the model\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we oft en (implicitly) assume that a spurious feature is irrelevant for predictio n. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying obj ects in an image. In contrast\, they carry important information that’s ne eded to make predictions by humans. In this talk\, we argue that it is mor e productive to characterize features in terms of their necessity and suff iciency for prediction. We then discuss the implications of this categoriz ation in representation\, learning\, and evaluation.
\nBiogr aphy
\nHe He is an Assistant Professor in the Department of Computer Science and the Center for Data Science at New York University. She obtained her PhD in Computer Science at the University of Maryland\, C ollege Park. Before joining NYU\, she spent a year at AWS AI and was a pos t-doc at Stanford University before that. She is interested in building ro bust and trustworthy NLP systems in human-centered settings. Her recent re search focus includes robust language understanding\, collaborative text g eneration\, and understanding capabilities and issues of large language mo dels.
\n 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-TAGS;LANGUAGE=en-US:2022\,He\,October END:VEVENT END:VCALENDAR