The Economic Case for Using AI to Close the Gender Equity Gap

There was a movement when I was in college toward becoming “colorblind.” The colorblind movement was based on a belief that said if we didn’t see the color of a person’s skin, we would judge them on their merits. That hypothesis has since proven false. It’s actually shown to engender biases against people of color.

We see a similar trend playing out with gender and meritocracies. By becoming gender blind and focusing on meritocracies or hiring the best talent, we wind up perpetuating bias against women. We are even finding a comparable pattern in AI when we are blind to the biases inherent in the data sets used.

But what if we flipped the script, and instead of AI being blind to bias, AI was aware of bias and recognized it as such?

Sundar Pichai, CEO of Google, said, “AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire.” I agree.

Can AI Close the Gender Equity Gap?

If we aren’t aware of our biases, especially in aggregate, we wouldn’t know how to fix them. That’s one thing the aforementioned blindnesses got right. The next step is not to ignore the biases. It is to recognize our biases and program AI to find them.

When we program AI to find our biases, we can root those biases out of decision making.

Rather than AI saying that white men make the best CEOs (an example of pattern matching), we can look at other attributes of CEOs and control for them. AI allows us to intercept our own decision making in order to make smarter, more equitable choices that benefit everyone.

Augmented Decision-Making: The Way of the Future

We are in the midst of the fourth industrial revolution. Augmented decision making is the way of the future. For all of the money spent on women’s leadership programs and implicit bias training, those measures are still incomplete because we have yet to begin intercepting people’s decision making.

Many companies struggle with two things in regards to closing the gender equity gap:

  1. Ensuring that each human capital decision is bias-free.
  2. Guaranteeing that each human capital decision is made in the company’s best interest.

Let’s take the gender pay gap as an example. Companies will spend months in retrospective reviews about pay decisions only to realize that they’re chasing a moving target: closing the gender pay gap is not a one-and-done task.

Every new hire, new promotion, and new pay decision risks re-opening the gap.

Despite even the best intentions, achieving and maintaining gender equity is an issue many companies will grapple with, but it doesn’t need to be this way. We can use AI to inform bias-free decision making. Not only is it the right thing to do, it’s is the smart thing to do.

Gender Equity: An Issue of Economic Importance

Closing the gender equity gap could increase the United States’ GDP by $2T. While $2T is the top line number, let’s peel it back and start to understand the economic implications of using AI to ensure we have the bias-free decision making needed to close the gender equity gap.

Beyond fixing the leaky pipeline, closing the gender pay gap would close the Social Security Savings gap by a third. Equal pay alone would reduce poverty rates now and in the future.

For instance, equal pay would cut poverty among working women and their families by more than half and add $513B to the US economy. We would also reduce poverty rates in retirement since more than twice as many women live in poverty than men among people 65 and older.

Potential Downsides of AI

No technology is a panacea. What can be used for good can also be used for the nefarious. Look at Amazon’s facial “Rekognition” software deployed in police departments as an example. Concerns by civil liberties groups and Amazon employees about civil liberties were raised when Amazon started selling Rekognition to police departments.

Deploying advanced software such as Rekognition into our police departments could threaten civil liberties, especially “in overpoliced communities of color,” where it’s said that Rekognition could infringe upon civil liberties. As well, “[t]he federal government could use this facial recognition technology to continuously track immigrants as they embark on new lives. Local police could use it to identify political protesters captured by officer body cameras.”

If we are to use AI for good, we must also address its potential drawbacks and remember that giving systems biased data results in the systems themselves being biased.

Automating human bias into algorithms is very possible and comes to the detriment of both male and female workers. This practice of algorithmic bias is already prevalent in some industries, and particularly concerning is the potential to automate gender bias in companies.

As AI technologies become more accessible, the likelihood of human bias being automated into algorithms increases.

The Power to Shape History

On one hand, it is within our power to safeguard the technology we produce against adverse practices and applications. On the other hand, it is within our power to use the technologies we produce to solve society’s most pressing challenges.

Just as Amazon’s employees stood up and asked the company to stop selling its Rekognition software to police departments, we too must use our collective voices to stand up, speak out, and bend the arc of history toward inclusion.

With AI as part of our solution to close the gender equity gap, we can increase the economic pie for all.

The fourth industrial revolution is here. Let’s shape history.



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