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[MUSIC PLAYING]

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AMIT GANDHI: Hi, my
name is Amit Gandhi,

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and I'm a graduate
researcher at MIT.

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Welcome to this course
on exploring fairness

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in machine learning for
international development.

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I'm going to present the
motivation for this course, why

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it is important to pay attention
to ethics and appropriate use

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in the topics we
will be covering.

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Let's start with an
introduction to how

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machine learning is being used
in international development.

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As a brief review,
machine learning

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is a branch of
artificial intelligence

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in which software learns
how to perform a task based

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on experience as opposed to
being explicitly programmed.

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As a result, this
emerging technology

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is widely applicable
across many fields

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and can leverage
the power of data

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in ways that were
previously impossible.

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Some application areas
for machine learning

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include medicine,
workforce development,

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and financial inclusion.

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In the next few slides,
I will talk about some

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of the ways machine
learning is being used,

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and we will go more in depth
about ethical usage of machine

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learning in later modules.

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In the health care sector,
significant advances

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in machine learning have
allowed for rapid diagnostics

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of medical conditions.

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Several organizations
are developing tools,

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ranging from sensors
to smartphone apps,

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for community health workers
to collect patient data,

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get novel insights into
patterns of disease spread

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and occurrence, and
perform diagnostics away

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from hospitals and clinics,
significantly increasing

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the access and quality
of medical care

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available to individuals
in remote areas.

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In workforce development,
machine learning

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can reduce unemployment rates
by pairing skilled individuals

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with appropriate jobs.

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For example, in
India, hiring managers

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pay more attention to
credentials than skills,

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making it difficult for
uncredentialed people

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to get hired or promoted.

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Aspiring Minds is
an Indian company

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that has developed
a computer test that

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determines applicants'
strengths and connects them

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to better paying jobs.

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In financial inclusion,
several organizations

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are using machine
learning to determine

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credit worthiness of
individuals in areas where

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other formalized
credit systems may not

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be available or accessible.

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These companies are deploying
pay-as-you-go services,

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ranging from solar lighting
systems to agricultural inputs,

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and are generating and gathering
non-traditional data on user

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assets and repayments.

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This data can be used to
determine credit worthiness

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using machine learning, enabling
individuals to access loans

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or financing that were
otherwise inaccessible.

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Machine learning techniques
have been around for decades

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but have increased in
usage in recent years.

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Over the past decade,
this technology

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has become more accessible.

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Machine learning is being
taught at many universities

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across the world, and several
data analysis platforms

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have released machine learning
libraries and toolboxes.

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For example, with Scikit-learn,
an open source Python library,

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someone familiar
with programming

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can get started with
training machine

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learning models
within a few days.

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While the applications of
machine learning are great,

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it is also important to
acknowledge its limitations.

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The impacts of machine
learning on society

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are still not well understood.

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Without careful attention
to these issues,

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we run the risk of
applying systems

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that are not only ineffective,
but could also harm people

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by reinforcing existing
patterns of social inequity

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and exclusion.

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There is a large
community of research

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in this area in
developed countries,

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such as the FAT
or AI Now groups.

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A well-known example is about
gender-differentiated credit

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scoring.

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Low-income women in
developing countries

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often lack credit histories
and formal income.

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Despite evidence showing
that these women default

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on loans less than
men, the lack of data

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makes it difficult for
lending organizations

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to provide these women loans.

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As a result, taking predatory
loans from informal lenders

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is a common practice.

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While many of these concepts
translate to ethics and machine

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learning, research
about fairness

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in machine learning
specifically for issues

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relevant to
international development

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is at an early stage.

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Ongoing efforts to
ensure ethical usage

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of this technology will be
discussed in the next video.

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We will cover content from an
appropriate usage framework

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developed by the Center
for digital development

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at the US Agency for
International Development.

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In the second set
of modules, we will

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present case studies
of organizations

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that are currently
using machine learning

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in international
development, and discuss

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potential ethical issues that
may arise in these examples.

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At a high level, we
will present approaches

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on how to address
these issues and what

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the outcomes could mean.

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In the third set of modules,
we will present a framework

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for addressing
ethical challenges.

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Content will include
protected attributes, fairness

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through unawareness, choices
and fairness criteria,

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and techniques for
mitigating bias.

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In the fourth set of modules,
we will present case studies

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from organizations with examples
of how machine learning could

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be implemented appropriately.

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Thank you for taking the
time to watch these videos,

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and we hope that you
will continue to watch

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the rest of the series.

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[MUSIC PLAYING]