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DTSTART:20231105T020000
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DTSTART:20240310T020000
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UID:calendar.3079.events_uoft_date.0@www.statistics.utoronto.ca
CREATED:20240305T133214Z
DESCRIPTION:\nWhen and Where: \nFriday, March 08, 2024 11:00 am to 12:00 
 pm \n 10 Floor \n Ontario Power Building \n 700 University Avenue Toronto\
 , ON M5G 1Z5 \n\nSpeakers \nRicardo Silva \n\nDescription: \nUsing data-dr
 iven and automated approaches to aid decision making has the merits of sca
 ling services up and allowing for standardized policies to treat people on
  a seemingly objective way. However, it is known that algorithms are ofte
 n not capable to be fair, according to a variety of value judgements, du
 e to reasons such as strong biases in the datasets used to build such algo
 rithms. This means some demographic groups are put at disadvantage, now a
 t an automated scale that can bring new potentially harmful consequences. 
 The notion of algorithmic fairness is however not straightforward, as the
 re are several roles algorithms play (from passive information retrieval t
 o high-stakes resource allocation problems) and several aspects of what fa
 irness means. In this talk, I will discuss how causal reasoning and infer
 ence can help some aspects of algorithmic fairness. Causality plays a role
  via the formulation of what-if questions that illuminate how an individua
 l’s history could have been different given alternative exposures, and ho
 w hypothetical policies not previously considered in the data could change
  the balance of outcomes across different demographic groups.Please join t
 he event.About Ricardo SilvaRicardo Silva is a Professor of Statistical Ma
 chine Learning and Data Science at the Department of Statistical Science,
  UCL, a Faculty Fellow at the Alan Turing Institute, and a recipient of 
 a EPSRC Open Fellowship (2023-2027). Ricardo obtained a PhD in Machine Lea
 rning from Carnegie Mellon University, 2005, followed by postdoctoral po
 sitions at the Gatsby Computational Neuroscience Unit (UCL) and at the Sta
 tistical Laboratory (University of Cambridge). His main interests are on c
 ausal inference, latent variable models, and probabilistic machine learn
 ing. His work has received funding from organisations such as the UK Engin
 eering and Physical Sciences Research Council, I\novate UK, the Office o
 f Naval Research, Winton Research and Adobe Research, among others. He i
 s currently Deputy Head of Department and a co-investigator in the UK AI H
 ub on Causality in Healthcare AI with Real Data. \n\nContact Information: 
 \n Data Sciences Institute \n700 University Avenue Toronto, ON M5G 1Z5 \n
 \nCategories \n Other Seminars \n\nAudiences \n FacultyGraduate Students
DTSTART;TZID=America/New_York:20240308T110000
DTEND;TZID=America/New_York:20240308T120000
LAST-MODIFIED:20250401T200700Z
LOCATION:700 University Avenue Toronto, ON M5G 1Z5
SUMMARY:Some Thoughts on the Use of Causal Modelling in Algorithmic Fairnes
 s
URL;TYPE=URI:https://www.statistics.utoronto.ca/events/some-thoughts-use-ca
 usal-modelling-algorithmic-fairness
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