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Why Diversity and Inclusion are Keys to AI Startup Success

· 5 min read

Artificial Intelligence (AI) is no longer the stuff of science fiction; it has become part of everyday life. In 2022 alone, AI startups received $52.1 billion in funding from 3,396 venture capital deals. Investment figures like this are a strong indicator that AI has great potential to transform our everyday lives.

Rapid growth, however, comes with challenges. In the case of AI, an increasingly pertinent concern is the issue of AI bias. The tech industry has long struggled with diversity and inclusion. According to a 2019 Wired survey, Silicon Valley still suffers from imbalances, with the combined population of Black, Hispanic, and Indigenous individuals making up only 5% of the workforce. In 2023, a staggering 83% of tech executives are white, and only 37% of tech companies have women on their boards. This striking lack of representation extends to AI, where it's estimated that 78% of global professionals possessing AI skills are male, resulting in a significant gender gap in the field.

The gender and race gap also leads to another problem: the pay gap. When underrepresented groups are paid less than their white, male counterparts, women and minorities may leave the industry, creating a self-perpetuating cycle of underrepresentation. 

The Challenge of AI Bias 

The lack of diversity in the AI workforce can have a direct effect on the technology itself. A workforce that doesn't include people with differing backgrounds and life experiences can produce AI products based on flawed or prejudiced training data. Flawed training data will result in flawed and biased outcomes, which will continue to perpetuate bias. 

Biased AI technology can pose a significant risk to society. Certain cutting-edge AI advancements have already exposed deep-rooted biases in the digital world. For example, look at facial recognition software. Facial recognition is touted as a way to improve security and access control, assist law enforcement, improve health and safety, and even provide a more enjoyable retail experience. While this sounds great in theory,  the reality is that facial recognition software has trouble accurately accounting for differences in skin tone and gender.

A well-known example of a facial recognition failure happened in 2018 when the public photos of all then-members of Congress were run through Amazon's tool, Rekognition, and matched against a database of 25,000 publicly available mugshots. Rekognition incorrectly matched 28 members of Congress as individuals in the database, falsely identifying them as people who had been arrested for a crime. Congress was made up of 20% people of color, yet of the incorrectly matched members, 39% were people of color.  Put another way, POC members of Congress were misidentified 10% of the time, while white members were misidentified 4% of the time.

Faces aren't the only things that AI struggles with. In 2016, Microsoft launched its Twitter bot, Tay. Microsoft claimed the bot had been trained using relevant public data that had been modeled, cleaned, and filtered. Yet when they released Tay on Twitter, within one day it started spewing racist and sexist content. The bot was disabled and later that year was replaced by Zo, which ultimately succumbed to similar issues and was shut down in 2019. 

Cultivating a Diverse AI Workforce 

More startups are emerging in the AI landscape, leading to more occasions to perpetuate social inequalities. While this issue may seem like a losing battle, these startups also represent opportunities to eliminate this bias and ensure that AI remains an instrument of progress and empowerment.

AI thrives on the quality of its underlying data. Biased data begets biased technology --- as the old saying goes, "Garbage in, garbage out." To ensure a better, more diverse AI technology, startups must first cultivate a diverse workforce. Inclusivity sets the stage for a truly representative AI. Embracing inclusivity by recruiting teams comprising individuals with distinct viewpoints, backgrounds, experiences, and perspectives, lays the foundation for profound breakthroughs.

Diverse tech talent is out there, but it may take some tweaks to your hiring process to ensure that you have a diverse talent pool for your startup. McKinsey & Company offers these seven hiring practices to implement to improve your diversity:   

  1. Make diversity and inclusion part of your overall hiring process, instead of treating it as a separate initiative.
  2. Collect data about your organization's diversity and set explicit goals so you know where you are and where you want to go.
  3. Update your job descriptions to be relevant and attractive to candidates from diverse cultures, backgrounds, and lifestyles. 
  4. Expand your hiring sources, rather than relying on a handful of candidate sites, recruiters, or universities.
  5. Prepare candidates for the technical interview process to make sure everyone is on equal footing going in.
  6. Remove unconscious biases in recruiting by training interviewers to use objective criteria in the hiring process instead of relying on "gut feelings."
  7. Once you've hired diverse talent, retain those workers by building an inclusive culture.

AI Startups Need to Proactively Foster Diversity and Inclusion

As the AI landscape rapidly evolves, startups must take a proactive approach to create a culture of diversity and inclusion. 

A diverse, inclusive workforce changes a company's perspective.  Employees with different viewpoints and backgrounds are more likely to identify and challenge biases that may otherwise creep into AI algorithms. They are more likely to build AI technologies that are truly equitable and fair, which will result in a better, more effective product that benefits more people.

Creating diversity in AI startups is not only the right thing to do; it's also the smartest business decision you can make. By cultivating a culture of diversity and inclusion, recruiting diverse talent, and providing training and education, AI startups can achieve success while eliminating biases at every opportunity. In short, building a better future for AI technology means building a better future for everyone.