2020 is forcing us to confront some hard truths about the world we live
in. The Covid-19 pandemic has cast a sobering spotlight on the unsustainable
path we are on.
One such truth is symbolised by the global #BlackLivesMatter movement, which has once again highlighted the embedded biases in our interconnected social fabric, forcing us all, to re-evaluate long standing notions of morality, fairness and ethics.
It is worth taking pause, to consider whether the exponential
technological progress is not also amplifying some of the very same challenges
we are trying to overcome, as a global society.
As we strive to meet the unmet and unarticulated needs of customers, we
continuously look towards technology to help meet those needs. We see leading
companies globally investing heavily in technologies such as cloud computing,
internet of things, advanced analytics, edge computing, virtual and augmented
reality, 3D printing and of course artificial intelligence. And it is AI, that
many experts tout as one of the most transformational technologies of our time,
in terms of sheer transformational impact on humanity.
Global use of AI has ballooned
by 270% over the past five years,
with estimated revenues of more
than $118-billion by 2025. AI powered
technology solutions have become so pervasive, a recent Gallup poll found that nearly
9 in 10 Americans use AI based solutions in their everyday lives.
And yet, a dark side of AI is surfacing with alarming frequency as AI
engrains itself in our daily lives.
The dark side of AI
In 2018, reports
emerged of Gmail’s predictive text
capability automatically assigning “investor” as “male”. When a research
scientist typed “I am meeting an investor next week”, Gmail’s Smart Compose
tool thought they would want to follow up with the question: “Do you want to
meet him?”
That same year, Amazon had to decommission its AI-powered talent
acquisition system after
it appeared to favour male candidates.
The software seemingly downgraded female candidates if their resumes included
phrases with the word “women’s” in them, for example “women’s hockey club captain.”
Errant algorithms can cause greater harm than a few missed employment
opportunities.
In June 2020, the New
York Times reported on an African American
man wrongfully arrested for a crime he didn’t commit after a flawed match from
a facial recognition algorithm.
Recent
studies by MIT found that facial
recognition software, used by US police departments for decades, work
relatively well on certain demographics, but is far less effective on other
demographics, mainly due to a lack of diversity in the data that the developers
used to train these algorithms.
Microsoft and Amazon have halted sales of their facial recognition
software until there is a better understanding and mitigation of their impact,
on especially vulnerable or minority communities. IBM has even gone as far to
halt offering,
developing or researching facial recognition technology.
What causes bias in AI?
There is growing
evidence that it is the underlying data
that perpetuates bias in AI. For example, using news articles for natural
language processing could instil the common gender stereotypes we find in
society simply due to the nature of the language used.
Many of the early algorithms were also trained using web data, which is
often rife with our raw, unfiltered thoughts and prejudices. A person
commenting anonymously on an online forum arguably has more freedom to display
prejudices without much consequence. Any algorithm trained on this data is
likely to assimilate the embedded biases.
As Princeton
researcher Olga Russakovsky says: “Debiasing humans is a lot harder than debiasing AI systems.”
Steps to greater fairness
There is arguably a need for greater diversity in the development rooms
where AI algorithms are created. A cursory glance at
the demographics of the big tech firms shows a disproportionate gender and
demographic bias. More must be done to accelerate the synthesis of diverse and
inclusive perspectives in the AI creation process, so that AI algorithms and
the data they are trained on embody a broad range of perspectives, allowing
them to drive more optimal outcomes for all those represented in society.
What can we do to mitigate bias in the AI solutions we increasingly use
to make potentially life changing decisions? Greater awareness of bias can help
developers see the context in which AI could amplify embedded bias and guide
them to put corrective measures in place. Testing processes should also be
developed with bias in mind: AI creators should deliberately create processes
and practices that test for and correct bias.
Finally, AI firms need to make investments into bias research,
partnering with other disciplines far beyond technology such as psychology or
philosophy, and share the learnings broadly to ensure all the algorithms we use
can operate alongside humans in a responsible and helpful manner.
Fixing bias is not something we can do overnight. It’s a process, just like solving discrimination in any other part of society. However, with greater awareness and a purposeful approach to combating
bias, AI algorithm creators have a hugely influential role to play in helping
establish a more fair and just society for everyone.
This could be one silver lining in the ominous cloud that is 2020.
