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How AI can close the equity gap

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Artificial intelligence may seem like a euphemism for institutionalized bias rather than a tool to eradicate it based on the (well-deserved) media attention this issue has garnered. And indeed, AI-powered platforms have committed mammoth grievances.

We’ve witnessed Amazon’s infamous hiring tool, Google’s faulty facial-recognition program, and Apple’s obtuse credit card, among others.

However, let’s think about the locus of control: Where does it lie? Not with AI. At the end of the day, control lies with us, the humans. Humans decide who wins in the age of AI because we’re the ones who research, engineer, and deploy this emerging technology.

Control means power, and power necessitates responsibility. It’s nothing to take lightly considering how approximately 85% of AI projects generate inaccurate reports as a result of algorithmic bias. Therefore, I believe it’s our responsibility to use AI for good: to eradicate inequity rather than propagate it.

Using AI to close the gender equity cap

The problem businesses have with gender equity isn’t awareness; it’s execution. U.S. corporations spend $8 billion every year on ineffective (and in some cases counter-effective) implicit bias training. An impressive 79% of businesses plan to spend more money this year than in 2021 to advance diversity, equity, and inclusion. Billions of corporate-backed dollars are pouring into gender and racial equity causes.

Meanwhile, gender equity indicators are barely budging. At current rates of progress, we won’t close the gender equity gap until 2289.

The problem businesses have with gender equity isn’t awareness.

The gender equity gap is ossifying when it should be shrinking. Women are taught to lean in. Lean out. Be a boss, but not too bossy. Negotiate, and then be penalized for doing so.

To move the needle on gender equity, companies need to embrace the tools of the Fourth Industrial Revolution.

With advanced technology like machine learning and cloud computing, organizations can ensure that human capital decisions across the entire employee lifecycle are both equitable and in the company’s financial best interest. Here’s an example to instantiate my techno-optimism.

AI as a tool for good

Companies make three key talent decisions every year:

  1. How will we pay our employees?
  2. How will we review their performance?
  3. How will we evaluate their potential?

The average Fortune 500 company, which has approximately 60,000 employees, has 180,000 opportunities to move closer to gender equity each year.

Make smarter, more equitable decisions at scale

By removing bias from these 180,000+ talent decisions, AI can drive smarter, more equitable outcomes for all. Companies know they must pivot from informal, subjective criteria to equitable, data-driven recommendations as the basis for their decision-making. People decisions are no different.

Plus, the same tools that augment human capital decisions can also quantify the projected financial upside of each decision. That means leaders can rest assured knowing that the equitable decision is the right decision for their people, their communities, and their top-line growth.

If we were to grade corporate America on their progress toward equity, they’d receive an A for effort but a D for achievement. We need to stop trying to change women and start changing the system. With advanced technology, it’s not a question of if we can close the gender equity gap, it’s will we choose to.


A version of this article originally published on Katica Roy’s website and is reprinted with permission.






Artificial intelligence may seem like a euphemism for institutionalized bias rather than a tool to eradicate it based on the (well-deserved) media attention this issue has garnered. And indeed, AI-powered platforms have committed mammoth grievances.

We’ve witnessed Amazon’s infamous hiring tool, Google’s faulty facial-recognition program, and Apple’s obtuse credit card, among others.

However, let’s think about the locus of control: Where does it lie? Not with AI. At the end of the day, control lies with us, the humans. Humans decide who wins in the age of AI because we’re the ones who research, engineer, and deploy this emerging technology.

Control means power, and power necessitates responsibility. It’s nothing to take lightly considering how approximately 85% of AI projects generate inaccurate reports as a result of algorithmic bias. Therefore, I believe it’s our responsibility to use AI for good: to eradicate inequity rather than propagate it.

Using AI to close the gender equity cap

The problem businesses have with gender equity isn’t awareness; it’s execution. U.S. corporations spend $8 billion every year on ineffective (and in some cases counter-effective) implicit bias training. An impressive 79% of businesses plan to spend more money this year than in 2021 to advance diversity, equity, and inclusion. Billions of corporate-backed dollars are pouring into gender and racial equity causes.

Meanwhile, gender equity indicators are barely budging. At current rates of progress, we won’t close the gender equity gap until 2289.

The problem businesses have with gender equity isn’t awareness.

The gender equity gap is ossifying when it should be shrinking. Women are taught to lean in. Lean out. Be a boss, but not too bossy. Negotiate, and then be penalized for doing so.

To move the needle on gender equity, companies need to embrace the tools of the Fourth Industrial Revolution.

With advanced technology like machine learning and cloud computing, organizations can ensure that human capital decisions across the entire employee lifecycle are both equitable and in the company’s financial best interest. Here’s an example to instantiate my techno-optimism.

AI as a tool for good

Companies make three key talent decisions every year:

  1. How will we pay our employees?
  2. How will we review their performance?
  3. How will we evaluate their potential?

The average Fortune 500 company, which has approximately 60,000 employees, has 180,000 opportunities to move closer to gender equity each year.

Make smarter, more equitable decisions at scale

By removing bias from these 180,000+ talent decisions, AI can drive smarter, more equitable outcomes for all. Companies know they must pivot from informal, subjective criteria to equitable, data-driven recommendations as the basis for their decision-making. People decisions are no different.

Plus, the same tools that augment human capital decisions can also quantify the projected financial upside of each decision. That means leaders can rest assured knowing that the equitable decision is the right decision for their people, their communities, and their top-line growth.

If we were to grade corporate America on their progress toward equity, they’d receive an A for effort but a D for achievement. We need to stop trying to change women and start changing the system. With advanced technology, it’s not a question of if we can close the gender equity gap, it’s will we choose to.


A version of this article originally published on Katica Roy’s website and is reprinted with permission.


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