Before fixing bias in AI, let us fix our own

When residents in Bronx were told that door locks would be replaced with high-tech face recognition software, the residents were up in arms. Face recognition software works well with fair-skinned males in the 18-35 age group. Residents of those building were from other ethnicities and often got locked out by the tech that was built on a data set that did not have enough samples from people of color. 

Voice recognition software has a problem identifying high pitched voices. Children’s voices and many accents are often misunderstood. A team member of Microsoft’s Kinect discovered in the early prototypes that while the device understood gestures of the male members of the house, the gestures of women and children were not understood as well. 

Amazon tried to feed a decade-old pile of resumes of job applicants to identify what the ideal job applicant. The machine knew one variable for sure. The ideal candidate had to be male. That’s what the majority of resumes said. Even when the gender of the person is hard to guess from an androgynous name like Kiran, the machine can find out the gender from hobbies. If the applicant has listed cricket as a sport, there is a greater likelihood of the person being male, the algorithm figured. Sometimes the name of the college attended had “Women” in the name and that was a give away of the gender.

Diversity is a challenge everywhere

The challenge of inclusion is everywhere. After 92 years, the Best Picture award was given to a film that was not made in the English language. Stand up comedians have built a career around the lack of diversity in the Academy Awards panel of jury members. But the challenge remains stubbornly unchanged. How do we ensure that the diversity challenge is addressed?

The challenge of inclusion is everywhere. After 92 years, the best picture award was given to a film that was not made in the English language

Something happens to us when we see ourselves as the majority. The people who are not like us become invisible. The majority writes the rule book to favor themselves in every way. Think of the challenge of left-handed people who wish to play the guitar or use a pair of scissors. The French word for right is “droit” and that is how adroit becomes a synonym for skilful in English. The left hand is called “gauche” in French which is how we describe something that is socially awkward.

Anything that is an exception to the norm is termed as “abnormal”. Gender was viewed as binary and anything that challenged the definition was termed as illegal. The employees who are on the rolls of organizations are naturally given salary or health insurance but the same privilege is not extended to the freelancer or gig worker who does the work from outside the lines of payroll. 

Why is tech not inclusive?

Tech is designed by a homogenous group of people. The young male engineer is the default. These businesses are managed by leadership teams that look the same. In the tech world, anyone over the age of 35 is “old” and cannot be expected to learn fast enough. Hiring married women who have children means they will not work insane hours and expect perks like “work-life balance”, the hiring manager of a tech company told me on the condition of anonymity.

The biggest tech companies build campuses that can keep you land locked in the office. Everything from going to the gym and snacking is a brief break you take before you come back to code. It is precisely this lack of contact with the world outside that makes them blind to the opportunities. When the large tech firms move into a neighborhood, the rents go up because the young and footloose people in hoodies can pay higher rents. In a few years, the space begins to look like a ghetto with no diversity. 

In technology, everything is binary. Whereas the human world is anything but that. The more techies become part of the social system, the more nuanced is their view of the world of the consumer. They begin to notice consumers who are not literate but need to use their phones and may even wonder how they store all the phone numbers they need to call. And how do they retrieve the numbers they cannot read? How do the elderly cope with the lack of inclusion that technology has put down as minimum literacy. How do people cope with the massive disruption in their careers as the machines keep gobbling up jobs that feed the family.

Before we fix the bias in the algorithms, we need to first fix the biases that cloud our view of the future. Technology has to be defined in cognitive as well as human terms. Today the definition is only cognitive. It takes away the essence of what makes us human. The backlash against Big Tech will percolate downstream. It is only a matter of time.

Before we fix the bias in the algorithms, we need to first fix the biases that cloud our view of the future. Technology has to be defined in cognitive as well as human terms

'Heads up' for techies 

When Marie Antoinette suggested that hungry peasants should eat cake instead of bread, the consequence she faced should give us a heads up (pardon the dark humor). With almost five billion adults having phones, there is a bigger question for tech to think of – what products and services will we create for the users who do not speak English, whose paying capacity is limited but are ever curious and eager to adopt technology. There is no better place to start than India. The first wave of technology was about iPhones and Teslas. The biggest problems of the problem remain unsolved – free elections, sustainable technology, rapid upskilling and much more.

As the next billion goes online, and the balance of power moves from US to the “rest of the world” (just think of what that term smacks of), we have to stop changing our accents to match a more “globally understood” accent and let technology figure out the accents of 1.3 billion Indians because that is where the next market is. That is the future.