Voting Theory: Fairness Criterion
This video define 4 Fairness Criterion for determining the winner of an election. Site: http://mathispower4u.com
From playlist Voting Theory
This video introduced fair division. Site: http://mathispower4u.com
From playlist Fair Division
Percentiles, Deciles, Quartiles
Understanding percentiles, quartiles, and deciles through definitions and examples
From playlist Unit 1: Descriptive Statistics
This video is about the measures of center, including the mean, median, and mode.
From playlist Statistical Measures
F-measure is a harmonic mean of recall and precision. Think of it as accuracy, but without the effect of true negatives (which made accuracy meaningless for evaluating search algorithms). F-measure can also be interpreted as the Dice coefficient between the relevant set and the retrieved s
From playlist IR13 Evaluating Search Engines
Measure Theory 1.1 : Definition and Introduction
In this video, I discuss the intuition behind measures, and the definition of a general measure. I also introduce the Lebesgue Measure, without proving that it is indeed a measure. Email : fematikaqna@gmail.com Code : https://github.com/Fematika/Animations Notes : None yet
From playlist Measure Theory
In Class Example Difference of Sample Means
A beneficial in class example of difference of sample means
From playlist Unit 7 Probability C: Sampling Distributions & Simulation
Guy Rothblum - Individual Fairness - IPAM at UCLA
Recorded 11 July 2022. Guy Rothblum of Apple Inc. presents "Individual Fairness" at IPAM's Graduate Summer School on Algorithmic Fairness. Abstract: This session will focus on the techniques for achieving individual fairness. Learn more online at: http://www.ipam.ucla.edu/programs/summer-s
From playlist 2022 Graduate Summer School on Algorithmic Fairness
Stanford Seminar: A Computational Approach to Criminal Justice
Sharad Goel Stanford University Statistical and algorithmic methods are increasingly used throughout the criminal justice system, from predictive policing to sentencing. I'll discuss two recent applications of this approach: (1) real-time risk assessments for stop-and-frisk and for bail d
From playlist Stanford Seminars
In How Many Ways Can an Algorithm be Fair? - Suchana Seth
Recent research in machine learning has thrown up some interesting measures of algorithmic fairness – the different ways that a predictive algorithm can be fair in its outcome. In this talk, Suchana Seth will explore what these measures of fairness imply for technology policy and regulat
From playlist Turing Seminars
RailsConf 2017: Bayes is BAE by Richard Schneeman
RailsConf 2017: Bayes is BAE by Richard Schneeman Before programming, before formal probability there was Bayes. He introduced the notion that multiple uncertain estimates which are related could be combined to form a more certain estimate. It turns out that this extremely simple idea has
From playlist RailsConf 2017
Oktay Günlük: "Fair and Interpretable Decision Rules for Binary Classification"
Deep Learning and Combinatorial Optimization 2021 "Fair and Interpretable Decision Rules for Binary Classification" Oktay Günlük - Cornell University Abstract: In this talk we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for
From playlist Deep Learning and Combinatorial Optimization 2021
The Mathematics of Bias by Nisheeth Vishnoi
ICTS at Ten ORGANIZERS: Rajesh Gopakumar and Spenta R. Wadia DATE: 04 January 2018 to 06 January 2018 VENUE: International Centre for Theoretical Sciences, Bengaluru This is the tenth year of ICTS-TIFR since it came into existence on 2nd August 2007. ICTS has now grown to have more tha
From playlist ICTS at Ten
Just machine learning In this talk, I will address some concerns about the use of machine learning in situations where the stakes are high (such as criminal justice, law enforcement, employment decisions, credit scoring, health care, public eligibility assessment, and school assignments).
From playlist DSI Virtual Seminar Series
AI Fariness and Adversarial Debiasing
Speaker(s): David Van Bruwaene Facilitator(s): Find the recording, slides, and more info at https://ai.science/e/how-the-board-of-directors-got-their-start-with-adversarial-debiasing--4tJcydU0OxWYpORF6cPl Motivation / Abstract Designing governance systems for AI is challenging on mult
From playlist AI Products
MIT 6.02 Introduction to EECS II: Digital Communication Systems, Fall 2012 View the complete course: http://ocw.mit.edu/6-02F12 Instructor: Hari Balakrishnan This lecture focuses on shared media networks and shared communications channels. Measures for optimization such as utilization, fa
From playlist MIT 6.02 Introduction to EECS II: Digital Communication Systems, Fall 2012
Bias in Machine Learning Literature Review
If you're short on time here's a deck we made to summarize the main ideas from this talk https://drive.google.com/file/d/1sLKk5fOrViOhmxc0ZLuTnHAtWrXD5hj9/view?usp=sharing
From playlist Machine Learning Streams
Cynthia Dwork - Group Fairness and Individual Fairness Pt. 1/2 - IPAM at UCLA
Recorded 11 July 2022. Cynthia Dwork of Harvard University SEAS presents "Group Fairness and Individual Fairness" at IPAM's Graduate Summer School on Algorithmic Fairness. Abstract: The early literature on the theory of algorithmic fairness identified two categories of fairness notions: gr
From playlist 2022 Graduate Summer School on Algorithmic Fairness