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AMIT GANDHI: Hi, my name is Amit Gandhi,
and I'm a graduate researcher at MIT.
Welcome to this course on exploring fairness
in machine learning for international development.
I'm going to present the motivation for this course, why
it is important to pay attention to ethics and appropriate use
in the topics we will be covering.
Let's start with an introduction to how
machine learning is being used in international development.
As a brief review, machine learning
is a branch of artificial intelligence
in which software learns how to perform a task based
on experience as opposed to being explicitly programmed.
As a result, this emerging technology
is widely applicable across many fields
and can leverage the power of data
in ways that were previously impossible.
Some application areas for machine learning
include medicine, workforce development,
and financial inclusion.
In the next few slides, I will talk about some
of the ways machine learning is being used,
and we will go more in depth about ethical usage of machine
learning in later modules.
In the health care sector, significant advances
in machine learning have allowed for rapid diagnostics
of medical conditions.
Several organizations are developing tools,
ranging from sensors to smartphone apps,
for community health workers to collect patient data,
get novel insights into patterns of disease spread
and occurrence, and perform diagnostics away
from hospitals and clinics, significantly increasing
the access and quality of medical care
available to individuals in remote areas.
In workforce development, machine learning
can reduce unemployment rates by pairing skilled individuals
with appropriate jobs.
For example, in India, hiring managers
pay more attention to credentials than skills,
making it difficult for uncredentialed people
to get hired or promoted.
Aspiring Minds is an Indian company
that has developed a computer test that
determines applicants' strengths and connects them
to better paying jobs.
In financial inclusion, several organizations
are using machine learning to determine
credit worthiness of individuals in areas where
other formalized credit systems may not
be available or accessible.
These companies are deploying pay-as-you-go services,
ranging from solar lighting systems to agricultural inputs,
and are generating and gathering non-traditional data on user
assets and repayments.
This data can be used to determine credit worthiness
using machine learning, enabling individuals to access loans
or financing that were otherwise inaccessible.
Machine learning techniques have been around for decades
but have increased in usage in recent years.
Over the past decade, this technology
has become more accessible.
Machine learning is being taught at many universities
across the world, and several data analysis platforms
have released machine learning libraries and toolboxes.
For example, with Scikit-learn, an open source Python library,
someone familiar with programming
can get started with training machine
learning models within a few days.
While the applications of machine learning are great,
it is also important to acknowledge its limitations.
The impacts of machine learning on society
are still not well understood.
Without careful attention to these issues,
we run the risk of applying systems
that are not only ineffective, but could also harm people
by reinforcing existing patterns of social inequity
and exclusion.
There is a large community of research
in this area in developed countries,
such as the FAT or AI Now groups.
A well-known example is about gender-differentiated credit
scoring.
Low-income women in developing countries
often lack credit histories and formal income.
Despite evidence showing that these women default
on loans less than men, the lack of data
makes it difficult for lending organizations
to provide these women loans.
As a result, taking predatory loans from informal lenders
is a common practice.
While many of these concepts translate to ethics and machine
learning, research about fairness
in machine learning specifically for issues
relevant to international development
is at an early stage.
Ongoing efforts to ensure ethical usage
of this technology will be discussed in the next video.
We will cover content from an appropriate usage framework
developed by the Center for digital development
at the US Agency for International Development.
In the second set of modules, we will
present case studies of organizations
that are currently using machine learning
in international development, and discuss
potential ethical issues that may arise in these examples.
At a high level, we will present approaches
on how to address these issues and what
the outcomes could mean.
In the third set of modules, we will present a framework
for addressing ethical challenges.
Content will include protected attributes, fairness
through unawareness, choices and fairness criteria,
and techniques for mitigating bias.
In the fourth set of modules, we will present case studies
from organizations with examples of how machine learning could
be implemented appropriately.
Thank you for taking the time to watch these videos,
and we hope that you will continue to watch
the rest of the series.
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