When you search for “CEO” on an image platform, the results can be startling—but not about CEOs themselves, but about how they’re represented.
Women, who make up 27% of US CEOs, barely crack 11% of the top search results. It’s like the algorithm skipped a memo. AI, the so-called genius of the digital age, often falls flat when it comes to fairness. Instead of leveling the playing field, it sometimes reproduces the stereotypes and inequities it finds in the data it’s fed.
The stakes are high—AI isn’t just for fun chatbots or smarter apps anymore. It powers decisions about credit approvals, hiring, school placements, and even government services. When bias creeps in, these systems can do more harm than good.
Bias in the machine
AI bias doesn’t come out of thin air.
Data, which is supposed to power AI’s smarts, often carries all the messiness of human history: stereotypes, inequalities, and blind spots. For example, medical AI trained predominantly on data from white men struggles to accurately diagnose women and minorities. And then there are the design choices—algorithms that default to “one-size-fits-all” approaches or ignore nuances like non-binary genders.
Deployment can be equally dicey. AI systems often fail when applied in real-world contexts they weren’t designed for. Language models, for instance, have been known to reinforce stereotypes when applied in hiring or criminal justice.
And let’s not ignore the elephant in the room: the tech industry itself.
Only 18% of researchers at leading AI conferences are women. The share of women in computing has dropped by 26% since 1960. Nearly half of women who enter tech leave the field, more than double the rate of men. At Microsoft, just 4.5% of employees are Black, and 6.3% are Hispanic/Latinx. Without diverse teams shaping AI, these systems will continue to reflect the biases of the people designing them.
AI Is taking over (but are we ready?)
Let’s talk about the big picture. Beyond a shiny tech trend, AI is a global economic game-changer. PwC estimates AI will add a staggering $15.7 trillion to the global economy by 2030. Business leaders at IBM expect AI adoption to explode, with up to 90% of companies jumping on the bandwagon in the next two years.
And they’re already on their way.
According to the CompTIA 2024 AI Outlook, 22% of companies are diving headfirst into AI integration across products and workflows, while another 33% are testing the waters with limited implementations. AI has come a long way since the 1950s, when British mathematician Alan Turing dreamed of “thinking machines” and developed the Turing test to measure intelligence. Today, AI is everywhere, from your favorite shopping app to high-stakes medical diagnoses.
But there’s a catch.
A 2019 DataRobot report revealed that 42% of organizations using or producing AI are deeply worried about the reputational fallout of biased systems. Imagine the damage a hiring algorithm does if it systematically overlooks qualified candidates, or a credit model that unfairly denies loans. Beyond lawsuits and PR disasters, these issues chip away at consumer trust.
Then there’s the social cost. Biased AI doesn’t just reflect inequality—it locks it in. For communities that have historically been underserved or marginalised, biased AI can mean fewer opportunities, poorer services, and an even wider gap between the haves and the have-nots.
AI isn’t as smart as you think
AI may seem like it’s got all the answers, but it’s clueless without context. It’s great at spotting patterns, but it doesn’t understand why they exist. For example, if candidates from a particular college get rejected during a hiring freeze, AI might assume the college produces bad candidates, even though the real issue had nothing to do with their qualifications.
This is why human oversight is crucial.
Mirror effect
Here’s the reality: AI doesn’t create bias—it mirrors it. It exposes the cracks and flaws in our systems that we might prefer to overlook, offering us an opportunity to address them and strive for better outcomes.
Interrupting AI bias isn’t just about fixing algorithms—it’s about fixing the systems, data, and decision-making processes behind them. It’s about designing tools that serve everyone, not just the majority or the privileged few.
Businesses that get this right won’t just avoid scandals—they’ll gain a competitive edge. Fair, inclusive systems build trust, attract diverse talent, and resonate with consumers who increasingly expect companies to stand for something more than profits.
Interrupting bias at the source
Interrupting AI bias starts with acknowledging its human origins. Rather than viewing bias as an unsolvable flaw, we can leverage AI’s ability to surface and measure inequities to address them head-on. Key steps include:
- Better data practices: Build diverse and representative datasets with accurate labeling. Involve individuals from varied backgrounds in data collection and ensure transparency in how datasets are constructed.
- Bias audits and updates: Regularly test AI systems for fairness, accuracy, and inclusivity by analysing outcomes with sensitive variables (e.g., race, gender)
- Human expertise and inclusion: Maintain human oversight in decision-making to provide context and ethical judgment. Ensure diversity among model developers, giving them opportunities to identify and address biases.
At Serein, we’re redefining innovation by keeping human expertise at the heart of our approach of using technology to build inclusive workplaces. Reach out to hello@serein.in to learn more.