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AMIT GANDHI: Hi, my name is Amit Gandhi.
And I'm a graduate researcher at MIT.
Welcome to the series on exploring
fairness and machine learning for international development.
In this module, we will have an example
of how an organization would go about implementing machine
learning and what some of the ethical challenges that
may arise are.
This module primarily focuses on decisions
that are made at the organizational level.
But it is important for both organizational decision makers
and machine learning implementers
to consider these interactions.
In this case study, we will be taking
the role of a chief technology officer of a social enterprise
to provide solar lighting products in East Africa.
The mission of the company is to provide affordable lighting
solutions to people living in poverty.
And the company started off by providing
high-quality, inexpensive solar light, so a replacement
for kerosene lanterns.
Over time, the company has grown and increased its product
offering to include large solar home systems and along the way
has implemented pay-as-you-go models so that households can
afford to purchase these larger systems.
The way pay-as-you-go models work are that you provide
the solar lighting infrastructure as a loaned
asset to individuals.
And they pay you back over time through mobile money payments,
until the full value of the asset is recovered.
The company has been meticulous about keeping records
from transactions from their user base.
And as a result, you have access to both demographic information
and payment history for all of your clients.
The information you have from your users
includes age, gender, occupation, location,
and household income.
As you look at expanding the social impact
of your enterprise, you realize that this data
can be analyzed to determine a creditworthiness
metric for your customers.
Additionally, you could provide this information
to banks or microfinance institutions
so that they can give loans to your client base.
Machine learning is a powerful tool
that you can use to implement this credit scoring metric.
However, you do not have data scientists or machine
learning experts within your team that
can implement this solution.
You also do not know how accurate or powerful
an algorithm you developed could be.
So you do not want to spend the resources to build
a full team [INAUDIBLE] on a small pilot with some
of your users in Uganda.
As a resourceful company with engineering staff,
you could either have some of your engineers
implement a machine learning solution
using off-the-shelf products or work with a third party company
to implement the solution for credit scoring.
Let's pause this case study for a second
and examine the pros and cons of the decisions that
need to be made.
It is important to consider perspectives
from both the machine learning implementer as well
as the organizations to understand the thoughts
and complexities that go into developing a solution.
Doing it in-house without a trained data scientist
will likely involve implementation of a black box
solution.
While someone with no background in machine learning
could get a solution up and running fairly quickly,
there are several nuances in the design that may get overlooked.
Allowing a third party consultant
to implement your solution would solve many of these issues,
though you may lack both in-house capabilities
to understand how your model is being implemented
and maintain it moving forward.
Let's assume that one way or another, the credit scoring
algorithm gets built.
Without paying attention to fairness in this setup,
several issues may arise.
First, you may find that as you analyze your historical data,
that certain groups of people have different default
rates than others.
For example, women may have a lower default rate than men.
And you may decide that as an organization,
you want to be fair and gender blind in your loan
determination.
The slide shows an example of what different loan rates look
like for what men and women.
To implement fairness, a naive implementer
may first try to use fairness through unawareness, which
means that you simply hide gender information
while building your models.
Depending on correlations within your data
and how relevant gender is to default rates,
your models could still predict gender
and use that in the model.
Second, since your data shows a difference in default rates,
you have to actively decide how to correct for that.
In the case of loans, different approaches
to implement fairness may have a trade-off with the accuracy
of your algorithms.
Third, the type of algorithm the implementer uses
could have trade-offs as well.
Some algorithms may be faster at the cost of accuracy.
Others may be more accurate at the cost of explainability
or understandability.
I won't go more in-depth on these topics,
because we will discuss them more in future modules.
However, I do want to highlight a couple of important concepts.
First, implementing a machine learning algorithm
is not an objective process.
In your implementation, you are both designing a technology
and making decisions, both of which
introduce your biases into the system.
To think that outcomes from a computer are objective
is just a fantasy.
Second, open communication between you
and the implementer on your values as an organization.
And decisions that they are making are critical.
Third, you need a way to audit your data and your algorithms
if you want to have a fair system.
Let's move on, assuming you were able to work with a consultant
to build a satisfactory solution to your algorithm.
And you're able to demonstrate significant success
with your pilot in western Uganda.
You now want to scale your model to other parts of Uganda
and East Africa.
At this point, it is important to pay attention
to the representativeness of your data.
Are there large differences between the types of users
you have in western Uganda and eastern Uganda?
How about the users in Uganda and Tanzania?
You need to make sure that you are collecting representatives'
data as you scale your solution, which
involves significant testing and auditing.
Additionally, you want to make sure
that changes within your population
do not suddenly affect your results.
For example, if a kerosene tax were imposed by the government,
would your model no longer be accurate?
How could you build in support within your organization
to make sure you can react to changes?
Thank you for taking the time to take this course.
We hope that you'll continue to watch the rest
of the modules in the series.
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