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WEF paper proposes principles to prevent discriminatory outcomes in machine learning

WEF paper proposes principles to prevent discriminatory outcomes in machine learning

The World Economic Forum (WEF)’s Global
Future Council on Human Rights
 recently issued
a white
to provide a framework for developers to prevent discrimination in
the development and application of machine learning (ML). The paper is based on
research and interviews with industry experts, academics, human rights
professionals and others working at the intersection of machine learning and
human rights.

The paper proposes a framework based on four guiding
principles – active inclusion, fairness, right to understanding, and access to
redress – for developers and businesses looking to use machine learning.

Artificial intelligence systems based on machine learning are
already being used to make decisions which have significant, life-altering
impact on people, such as hiring of job applicants, granting loans and
releasing prisoners on parole.  Machine
learning systems can help to eliminate human bias in decision-making, but they
can also end up reinforcing and perpetuating systemic bias and discrimination.

Concerns around opacity,
data, algorithm design

Previous algorithmic decision-making systems relied on
rules-based, “if/then” reasoning. But ML systems create more complex models in
which it is difficult to understand why and how decisions were made.

ML systems are opaque, due to their complexity and also to
the proprietary nature of their algorithms. Moreover, ML systems today are
almost entirely developed by small, homogenous teams, most often of men. The
massive data set required to train the sysem are often proprietary and require
largescale resources to collect or purchase. This effectively excludes many
companies, public and civil society bodies from the machine learning market. Though there is increasing availability of open data, companies who own massive proprietary datasets continue to enjoy definite advantages.

Training data may exclude classes of individual who do not
generate much data, such as those living in rural areas of low-income
countries, or those who have opted out of sharing their data. The report
presents an example where this might lead to discrimination. If an
application’s training data demonstrates that people who have influential
social networks or who are active in their social networks are “good”
employees, it might filter out people from lower-income backgrounds, those who
attended less prestigious schools, or those who are more cautious about posting
on social media.

Similarly, loan applicants from rural backgrounds, with less
digital infrastructure, could be unfairly excluded by algorithms trained on
data points captured from more urban populations.

Data may be biased or error-ridden. For instance, using
historical data might result in the ML system judging women to worse hires than
men, because historically women have been promoted less than men. Whereas the
actual reason is that workplaces have historically been biased.

Even if the data is good, the paper identifies five ways in which
design or deployment of ML algorithms could encode discrimination: choosing the
wrong model (or the wrong data); building a model with inadvertently
discriminatory features; absence of human oversight and involvement;
unpredictable and inscrutable systems; or unchecked and intentional

The authors cite examples of systems that disproportionately
identify people of colour as being at “higher risk” for committing a crime or
re-offending or which systematically exclude people with mental disabilities
from being hired. The risks are higher in low- and middle-income countries,
where existing inequalities run deeper, availability of training data is
limited, and government regulation and oversight are weaker.

Four proposed principles
for businesses

The paper notes that governments and international organisations
have a role to play but regulation tends to lag technological development. However,
even in the absence of regulation, the paper says that businesses need to
integrate principles of non-discrimination and empathy into their human rights
due diligence.

As part of ‘Active Inclusion’, the paper recommends that development
and design of ML applications must actively seek a diversity of input,
especially of the norms and values of specific populations affected by the
output of AI systems.

The second principle proposed is ‘Fairness’. People involved
in conceptualising, developing, and implementing ML systems should consider
which definition of fairness best applies to their context and application, and
prioritize it in the architecture of the machine learning system and its
evaluation metrics.  

To ensure ‘Right to Understanding’, the involvement of ML
systems in decision-making that affects individual rights must be disclosed. Also,
the systems must be able to provide an explanation of their decision-making
that is understandable to end users and reviewable by a competent human
authority. If that is impossible and human rights are at stake, the paper
states that leaders in the design, deployment and regulation of ML technology
must question whether or not it should be used.

The paper also proposes that leaders, designers and developers
of ML systems must make visible avenues for redress for those affected by
disparate impacts, and establish processes for the timely redress of any
discriminatory outputs.

To help companies adopt these principles, the paper recommends
that companies should identify human rights risks linked to business operations.
Common standards could be established and adopted for assessing the adequacy of
training data and its potential bias through a multistakeholder approach.

It is proposed that companies work on concrete ways to
enhance company governance, establishing or augmenting existing mechanisms and
models for ethical compliance. Additionally, companies should monitor their
machine learning applications and report findings, working with certified
third-party auditing bodies. Results of audits should be made public, together
with responses from the company. The authors say that large multinational
companies should set an example by taking the lead in this.

The authors express hope that this report will advance
internal corporate discussions of these topics as well as contribute to the
larger public debate.

“We encourage companies working with machine learning to
prioritize non-discrimination along with accuracy and efficiency to comply with
human rights standards and uphold the social contract,” said Erica Kochi,
Co-Chair of the Global Future Council for Human Rights and Co-Founder of UNICEF

Nicholas Davis, Head of Society and Innovation, Member of
the Executive Committee, World Economic Forum, said, “One of the most important
challenges we face today is ensuring we design positive values into systems
that use machine learning. This means deeply understanding how and where we
bias systems and creating innovative ways to protect people from being
discriminated against.”  

Read the paper here.

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