WHY ELIMINATING BIAS IN AI is Key to AI Success
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 some of the more questionable facets of our way of life and is making it clear, that continuing on this unsustainable path we have been on will inevitably lead to a very bleak future for our species. By Rudeon Snell, Global Senior Director: Industries & Customer Advisory at SAP
3RD QUARTER 2020
ONE SUCH truth is symbolised by the global #BlackLivesMatter movement, which was sparked in the US, but has spread to cities all around the globe. This movement has once again highlighted the embedded biases in our interconnected social fabric, forcing us all, to not only rethink, but completely reevaluate long standing notions of morality, fairness and ethics. It is worth taking pause, just for a moment, to consider whether the exponential technological progress we have been experiencing, and which does aim to create a better future for humanity, is not also amplifying some of the very same challenges we are trying to overcome as a global society.
SYNAPSE
18
As we strive to meet the unmet and unarticulated needs of customers, we continuously look towards technology to fulfil the promise of ushering in a new era of human advancement. We see leading companies globally investing heavily in technologies such as Cloud Computing, Internet of Things, Advanced Analytics, Edge Computing, Virtual and Augmented reality, 3-D printing and of course Artificial Intelligence. And it is AI, which many experts tout as one of the most transformational technologies of our time, perhaps even more transformational than electricity or fire, in terms of sheer 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. Phones, apps, search engines, social media, email, cars and even appliances in our homes are all powered by AI infused technologies today, with this trend set to accelerate in future. And yet, a dark side of AI is surfacing with alarming frequency as AI engrains itself in our daily lives. Questions that
are increasingly being posed and must be addressed, concern themselves with whether AI algorithms are indeed perpetuating various forms of bias to the detriment of under-represented communities and minorities? To what extent do AI-imbued solutions discriminate against exposed classes of our society due to embedded bias?
Bias in the machine
There are ample examples of algorithms displaying forms of bias. In 2018, reports emerged of Gmail’s predictive text tool 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 their AIpowered 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”. Many of the large tech firms battle with diversity, with men much better represented than women in most major tech companies. Having gender bias embedded in algorithms designed to support the hiring process presents a significant risk to efforts at achieving greater diversity: Mercer’s Global Talent Trends report for 2019 highlights that 88% of companies globally, already use AI powered solutions in some way for HR, with 100% of Chinese firms and 83% of US employers relying on some form of AI for HR. For Amazon, it has forced a rethink of how they recruit globally, no small feat for a company that employs more than 575 000 workers.
Persecuted by an algorithm
Errant algorithms can be responsible for greater harm than just 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. Experts at the time believed it was the first such case to be tested in US courts. I’d wager, it won’t be the last. Recent studies by MIT found that facial recognition software, used by US police departments for decades, works 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.
