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-23316@www.clsp.jhu.edu DTSTAMP:20240329T101051Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nUnderstanding the implications underlying a text is c ritical to assessing its impact\, in particular the social dynamics that m ay result from a reading of the text. This requires endowing artificial in telligence (AI) systems with pragmatic reasoning\, for example to correctl y conclude that the statement “Epidemics and cases of disease in the 21st century are “staged”” relates to unfounded conspiracy theories. In this ta lk\, I discuss how shortcomings in the ability of current AI systems to re ason about pragmatics present challenges to equitable detection of false o r harmful language. I demonstrate how these shortcomings can be addressed by imposing human-interpretable structure on deep learning architectures u sing insights from linguistics.In the first part of the talk\, I describe how adversarial text generation algorithms can be used to improve robustne ss of content moderation systems. I then introduce a pragmatic formalism f or reasoning about harmful implications conveyed by social media text. I s how how this pragmatic approach can be combined with generative neural lan guage models to uncover implications of news headlines. I also address the bottleneck to progress in text generation posed by gaps in evaluation of factuality. I conclude by showing how context-aware content moderation can be used to ensure safe interactions with conversational agents.\n \nBiogr aphy\nSaadia Gabriel is a PhD candidate in the Paul G. Allen School of Com puter Science & Engineering at the University of Washington\, advised by P rof. Yejin Choi and Prof. Franziska Roesner. Her researchrevolves around n atural language processing and machine learning\, with a particular focus on building systems for understanding how social commonsense manifests in text (i.e. how do people typically behave in social scenarios)\, as well a s mitigating spread of false or harmful text (e.g. Covid-19 misinformation ). Her work has been covered by a wide range of media outlets like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nominatio n\, a 2019 IROS RoboCup best paper nomination and won a best paper award a t the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cu m laude from Mount Holyoke College in Computer Science and Mathematics.\n DTSTART;TZID=America/New_York:20230227T120000 DTEND;TZID=America/New_York:20230227T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Saadia Gabriel (University of Washington) “Socially Responsible and Factual Reasoning for Equitable AI Systems” URL:https://www.clsp.jhu.edu/events/saadia-gabriel-university-of-washington -socially-responsible-and-factual-reasoning-for-equitable-ai-systems/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nUnderstanding the implications underlying a text is c
ritical to assessing its impact\, in particular the social dynamics that m
ay result from a reading of the text. This requires endowing artificial in
telligence (AI) systems with pragmatic reasoning\, for example to correctl
y conclude that the statement “Epidemics and cases of disease in the 21st
century are “staged”” relates to unfounded conspiracy theories. In this ta
lk\, I discuss how shortcomings in the ability of current AI systems to re
ason about pragmatics present challenges to equitable detection of false o
r harmful language. I demonstrate how these shortcomings can be addressed
by imposing human-interpretable structure on deep learning architectures u
sing insights from linguistics.
In the first part of the talk\, I describe how adversarial text gen
eration algorithms can be used to improve robustness of content moderation
systems. I then introduce a pragmatic formalism for reasoning about harmf
ul implications conveyed by social media text. I show how this pragmatic a
pproach can be combined with generative neural language models to uncover
implications of news headlines. I also address the bottleneck to progress
in text generation posed by gaps in evaluation of factuality. I conclude b
y showing how context-aware content moderation can be used to ensure safe
interactions with conversational agents.
\n
Biography
\nSaadia Gabr iel is a PhD candidate in the Paul G. Allen School of Computer Scie nce & Engineering at the University of Washington\, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research re volves around natural language processing and machine learning\, with a pa rticular focus on building systems for understanding how social commonsens e manifests in text (i.e. how do people typically behave in social scenari os)\, as well as mitigating spread of false or harmful text (e.g. Covid-19 misinformation). Her work has been covered by a wide range of media outle ts like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nomination\, a 2019 IROS RoboCup best paper nomination and won a bes t paper award at the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cum laude from Mount Holyoke College in Computer Sc ience and Mathematics.
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