BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Fairness for Sequential Decision Making Algorithms - Hoda Heidari
DTSTART:20190117T110000Z
DTEND:20190117T120000Z
UID:TALK114025@talks.cam.ac.uk
CONTACT:Adrian Weller
DESCRIPTION:Fairness considerations in settings where decisions are made b
 y supervised learning algorithms (e.g. criminal risk assessment) has recei
 ved considerable attention\, recently. As the fairness literature continue
 s to expand mostly around this canonical learning task\, it is important t
 o recognize that many real-world applications of ML fall outside the categ
 ory of supervised\, one-shot learning. In this presentation\, I will talk 
 about two scenarios in which algorithmic decisions are made in a sequentia
 l manner and over time. I will argue that in such settings\, being fair---
 at a minimum---requires decisions to be "consistent" across individuals wh
 o arrive at different time steps\, that is\, similar individuals must be t
 reated similarly. I will then talk about how such consistency constraints 
 affect learning. \n\nIn the first part of the talk\, I will introduce a ge
 neric sequential decision making framework\, in which at each time step th
 e learning algorithm receives data corresponding to a new individual (e.g.
  a new job application) and must make an irrevocable decision about him/he
 r (e.g. whether to hire the applicant) based on observations it has made s
 o far. I propose a general framework for post-processing predictions made 
 by a black-box learning model\, so that the resulting sequence of outcomes
  is guaranteed to be consistent. I show\, both theoretically and via simul
 ations\, that imposing consistency constraints will not significantly slow
  down learning.\n\nIn the second part of the talk\, I will focus on fairne
 ss considerations in a particular type of market---namely\, combinatorial 
 prediction markets---where traders can submit limit orders on various secu
 rity bundles\, and the market maker is tasked with executing these orders 
 in a fair manner. The main challenge in running such market is that execut
 ing one order can potentially change the price of every other order in the
  book. I define the notion of a "fair trading path"\, which at a high leve
 l guarantees similar orders are executed similarly: no order executes at a
  price above its limit\, and every order executes when its market price fa
 lls below its limit price. I present a market algorithm that respects thes
 e fairness conditions\, and evaluate it using real combinatorial predictio
 ns made during the 2008 U.S. Presidential election. 
LOCATION:Engineering Department\, CBL Room BE-438.
END:VEVENT
END:VCALENDAR