Technology Considered Harmful? Case Study: Facial Recognition and - - PowerPoint PPT Presentation

CMSC 20370/30370 Winter 2020 Technology Considered Harmful? Case Study: Facial Recognition and Bias

Mar 6, 2020

Quiz Time (5-7 minutes).

Quiz on Facial recognition and bias

Principles of Good Design

Administrivia

• GP4 video due on Monday for video

screening

• Next week video showcases will be in:

– Monday: Room 390 – Wednesday: Room 298

• Schedule of groups is online
• Please send us links to your videos ahead
• f the class session so we can load them

all on one laptop

Today’s Agenda

• Is technology considered harmful?
• Case Study: Facial recognition and Bias

Case Study: Facial Recognition and Bias

• Looked at existing face data sets to see

composition of male vs female faces, light skinned vs dark skinned

• Evaluation 3 commercial classifiers and

found they all perform worse on darker skinned females

• Provide implications for fairness in

machine learning and assessing algorithms accuracy

Fairness and Recidivism

• ProPublica 2016 study:
• Rode unlocked bike and scooter with a friend down street - $80

worth

• Prater $83 dollar shoplifting from Walmart
• Rode misdemeanors as juvenile
• Prater already convicted of armed robbery and attempted armed

robbery

Bail based on risk score…

• 2016 ProPublica study
• COMPAS, an algorithm used for recidivism prediction
• Produces much higher false positive for black people than white people
• Recidivism = likelihood of a criminal to re-offend
• Examined 7000 risk scores in Florida in 2013/2014

Hiring and fairness

• 2018 study found that Xing, similar to LinkedIn, algorithm exhibits bias
• Ranked more qualified female candidates lower than qualified male candidates

Case Study: Facial Recognition and Bias – Skin Tone Map

Created a data set of parliamentarians

• Evaluated 3 commercial gender classifiers

from Microsoft, IBM, and Face++

• Looked at overall accuracy for gender

classification

• Then broke it out by skin tone
• Found that all three perform worse for darker

skinned females

• Also perform worse on darker skin than

lighter skin

Gender classification Performance

What if this is due to image quality?

• They also wanted to see if this was just

because images from European countries were higher res + better pose

• Did another analysis on South African data

since skin tone range is high

• Also there is more balanced set of darker

skin tones in South African subset

• Found the trends remain…

Why does this bias happen?

• Could be the training data used for the

algorithms

• Could be fewer instances of darker skinned

people in training set

• Darker skin could be correlated with facial

characteristics not well represented in data set for instance

Fairness in Machine Learning

• Lots of definitions of fairness

– Such as being blind to “protected” attributes such as race, gender – Equalizing odds – Individual fairness etc

• Watch 21 definitions of fairness in ML by

Arvind Narayanan @ Princeton

• https://www.youtube.com/watch?v=wqam

rPkF5kk

Other sources of bias

• Skewed sample, confirmation bias over time
• Uneven data set
• Limited features for minority groups
• Proxies

– Don’t use race but other demographic information can be correlated with race

• Tainted examples

– E.g. Word2Vec word embeddings trained on Google news – Associates words “computer programmer” with “man” and “homemaker” with women

Case Study Discussion Points

• Can’t rely on one metric for accuracy
• Have to break out performance metrics by

subsets of classification

• Facial recognition algorithms give confidence

scores in classification

– Need to provide other metrics such as true positive rate and false positive rate

• Image quality such as pose and illumination

can confound results (and algorithms)

HCI uses ML

But how else can HCI help to make inclusive technology?

• Its Machine Learning but HCI has a part to

play

• How to visualize different forms of bias in

ML

• Understand how to help overcome bias in

different parts of the ML pipeline

HCI, Ethics, and ML

• How are data sets used over time?
• Where are images gathered from?
• How can we help people identify biases?
• How can we help people document how

they create a ML pipeline?

Let’s consider if technology is harmful or not

• Break up into groups of 4-5
• Discuss the following questions (10 minutes)
• Do you think we can ever achieve fairness?
• What do you think about technology and

amplification of human intent?

• How can you balance using technology and

achieving inclusivity in design?

• What are the top three things you learned in

this class?

• Let’s share a few instances next

Summary

• Increasing reliance on algorithms for decisions that

impact humans in some way

– Hiring, bail, criminal investigations, facial recognition

• Have to think of ways to incorporate fairness into

machine learning problems

• HCI has a part to play to make ML more fair and

inclusive

• Technology is useful but can also be

unintentionally harmful

• Remember that at the end of the day technology
• nly amplifies what is already there

Coming up…

• GP 4 video showcase on Monday and

Wednesday

• GP 4 reports due in 2 weeks

Get in touch:

Office hours: Fridays 2-4pm (Sign up in advance) or by appointment JCL 355 Email: marshini@uchicago.edu