Data Stories

156  |  Visualizing Fairness in Machine Learning with Yongsu Ahn and Alex Cabrera

Mar 5, 2020
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1
Introduction
00:00 • 2min
2
A Young Si, Welcome to the Show!
02:26 • 2min
3
Machine Learning Is Increasingly Used in Important Decisions
04:25 • 2min
4
Machine Learning
06:22 • 2min
5
Is There a Difference Between the Output and the Input?
07:53 • 2min
6
Is There a Difference Between Black and White in Face Recognition?
09:26 • 2min
7
Is There a Problem With Thes Systems?
11:34 • 2min
8
Machine Learning and Fairness in Machine Learning Problems
13:07 • 2min
9
The Importance of Ranking Decisions, Right?
14:55 • 2min
10
Defining Fairness in Machine Learning
17:00 • 4min
11
Is There a Difference Between Machine Learning and Human Computer Decisions?
20:33 • 2min
12
Machine Learning
23:02 • 2min
13
Is There a Tension Between Being Fair and Making the Most Optima Qodent Quot Optimal Decision?
25:22 • 2min
14
The Machine Learning System Can Help Decision Makers With Locally Optimal Decisions
26:59 • 2min
15
Are the Credit Scores Fairer Than Average Neural Net?
29:05 • 2min
16
Can We Develop Better Methods of Understanding How Algorithms Can Be Problematic?
31:12 • 2min
17
Machine Learning and Machine Learning Methods
32:51 • 4min
18
Participatory Design
36:46 • 2min
19
Is It Possible to Try Out Your Tools?
38:33 • 2min
20
Data Stories Podcast - Thank You So Much!
40:44 • 2min