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MIKE TEODORESCU: Hello, and welcome to this module
on protected attributes and fairness through unawareness.
My name is Mike Teodorescu.
I'm an assistant professor of information systems
at Boston College, as well as a visiting scholar at MIT D-Lab.
What this module will cover will be
examples of laws that codify protected attributes, as well
as the base case scenario for fairness
in machine learning, which is called fairness
through unawareness.
The use of machine learning presents both risks
and opportunities.
Machine learning can reduce costs
by automating repetitive tasks, but could also increase biases.
Certain individual attributes are commonly
labeled as protected attributes, as they
can be sources of social bias.
These are race, religion, national origin, gender,
marital status, age, and socioeconomic status.
In the United States, discrimination
based on these protected attributes in housing, lending,
and employment is illegal.
Some of the laws are listed here for your reference.
However, regardless of the legal framework,
machine learning still has the potential
to unintentionally embed bias.
In this lecture, we look at a few examples.
The next lecture will explore some approaches
to mitigate unintentional bias.
Even large companies could unintentionally discriminate.
For example, Amazon used a machine
learning algorithm to screen resumes,
and later found out that the algorithm was discriminating
against female applicants.
In another example, this time from the criminal justice
system, machine learning is used to determine
the risk of recidivism.
This system has been questioned in a variety of studies,
in particular, with reference to the protected
attribute of race and gender.
In another example of a large organization
employing machine learning, this time, to display ads,
Facebook has been named in a suit over alleged violations
of the Fair Housing Act.
Therefore, even large organizations,
like Amazon an Facebook, find machine
learning fairness challenging.
We hope this course will be helpful in preparing
software engineers to better address machine learning
fairness.
Generally speaking, before you implement a machine learning
algorithm, you need to collect data to train that algorithm.
This data set would have your outcome variable,
in this figure, Y, for example, the decision
to hire or not to hire, as well as all of the predictors,
for example, features collected from resumes.
This complete data set would then
have to be split in a training set on which the model would
learn and a test set on which we would determine the model
performance.
There are other details, like cross-validation,
which we'll not cover here, but I
encourage you to read more on.
Fairness starts with a good quality training set.
Low quality data, generally speaking,
leads to bad predictions.
The individuals labeling the data,
for example, managers labeling resume as a higher or no hire,
may carry biases, which are then picked up
by the machine learning algorithm.
Training data may not be representative of all groups,
which could lead to bias.
And there may be hidden correlations in input data,
for example, between a protected attribute and the predictor.
That can also lead to bias.
Individuals labeling the training data
may misremember past situations, a phenomenon known
as selective perception, which may in itself become
a source of bias.
The default fairness method in machine learning
is fairness through unawareness.
Fairness through unawareness refers
to leaving out protected attributes such as gender,
race, and other characteristics deemed sensitive.
And, while it was thought to erase inequality,
it was found, actually, to perpetuate it.
It may do so by, for example, having other attributes that
are correlated with the protected
attributes in the data, which, by ignoring the protected
attributes, we could just still include in our model.
This could actually perpetuate inequality.
When race, gender, and other sensitive variables
are treated as protected, other variables, such as college
attended, hometown, or other resume indicators
that remain unprotected, may still
be highly correlated with these protected attributes.
Thus, ignoring the protected attributes altogether
may not reveal these hidden correlations in data.
This is called redundant encoding.
In one example, researchers at Carnegie Mellon University
found that gender caused an unintentional change
in Google's advertising system such an ad listings targeted
for user's seeking high-income jobs
were presented to men at nearly six times the rate they
were presented to women.
This may be an example of fairness through unawareness.
Here are some review questions about this material.
What are the sensitive attributes
in the context in which you work?
Do you think that the current list of protected attributes
is exhaustive?
What is fairness through unawareness?
What variables might lead to biased predictions
for a machine learning hiring system in your country?
What are some risks to an organization choosing
unawareness?
I'd like to thank my co-authors Lily Morse, Gerald Kane,
and Yazeed Awwad for a study that
helped me put together also these slides,
as well as USAID for a grant on appropriate use of machine
learning in developing countries and the Carroll
School of Management at Boston College for research funding.
I'd also like to thank everyone who helped contribute
with feedback to these videos, as well as
the accompanying manuscripts.
I'd like to share with you some references I found helpful
[INAUDIBLE] this material.
I hope you will read more about this,
and I'd like to thank you for your attention
in watching this video.
Thank you so much for watching this video.
We hope you find it useful and you'll continue
watching the rest of the class.
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