The bias in AI is a human problem, not a machine problem

Artificial Intelligence doesn’t think. It doesn’t have an agenda. It simply reflects the world as we have built it.

We often talk about AI bias as though it’s an accident, a glitch in the system. But in reality, AI bias is a direct consequence of who builds it, what data we train it on, and the systemic inequalities embedded in the very societies AI is designed to serve. Instead of asking, “Why is AI biased?”, the better question is: Why wouldn’t it be?

For decades, hiring, healthcare, security, and consumer behaviour have been shaped by human biases, subtle and overt, conscious and unconscious. AI does not create new biases. It automates existing ones and scales them at unprecedented levels. The risk? If we do nothing, AI will not just reflect our past; it will cement it into our future.

AI is Learning from a Flawed World, And,  We’re Letting It

1. AI in Hiring – Efficiency or Institutionalized Discrimination?

AI-driven hiring tools promise to remove human prejudice by focusing on skills, experience, and qualifications. Yet, if the data used to train these algorithms reflects historical discrimination, AI will replicate those same patterns.

Amazon’s AI Hiring Debacle – Amazon had to scrap its AI recruitment tool when it was found to downgrade resumes with the word “women’s” (e.g., women’s chess club) simply because the model was trained on past hiring data that favoured men.

Selective Job Ad Targeting – Studies show that AI-driven job ads disproportionately display higher-paying opportunities to men while steering women toward lower-wage positions. If you aren’t even shown a job listing, how can you apply for it?

What’s the real issue? Hiring discrimination has always existed. AI didn’t invent it, it just exposed how deeply ingrained it is in our hiring systems.

The Real Danger? AI Removes the “Benefit of the Doubt”
In a human hiring process, a recruiter might reconsider an application after an initial rejection. AI, however, has no such flexibility, if the algorithm decides a candidate is a low match, there’s no second chance, no negotiation, no reconsideration.

2. Healthcare AI – A New Tool with Old Blind Spots

AI is revolutionizing medicine, from diagnostics to personalized treatments. But the foundation of medical AI is historical healthcare data, data that often excludes women, ethnic minorities, and lower-income populations.

Heart Attack Misdiagnoses – The Journal of the American Heart Association found that women experiencing heart attacks were 50% more likely to be misdiagnosed, partly because most medical research historically focused on male patients. AI models trained on such data can continue to overlook life-threatening symptoms in women.

Drug Development Bias: Many drugs have been tested predominantly on white, male participants for decades. AI-powered drug discovery models built on this data might not account for genetic variations across different populations.

Who Does AI Prioritize? Predictive analytics in healthcare is used to determine which patients get urgent care. Some AI models used in hospitals have been found to deprioritize Black patients for certain treatments, because their historical healthcare data showed lower medical spending per capita, which AI would mistakenly interpreted as “lower risk.”

The Real Issue?
Medical bias has existed for centuries. AI doesn’t introduce new prejudices; it just makes them more efficient. But efficiency isn’t the same as fairness.

3. Facial Recognition & Security – Who AI Sees (And Who It Doesn’t)

Facial recognition technology is used for everything from unlocking smartphones to criminal investigations. But studies show it misidentifies people from minority backgrounds far more often than white men.

Error Rates by Demographic (Gender Shades Study by MIT Media Lab):

• White men: <1% error rate
• Women of colour: 35% error rate

Intersectionality Matters – Men and Women from marginalized ethnic groups are often at the highest risk. Inaccurate facial recognition has already led to wrongful arrests.

The Real Issue?
Surveillance technologies were designed for control, not equity. AI’s role in law enforcement isn’t making the system more just, it’s making past injustices more permanent.

4. AI in Consumer Behaviour – How Algorithms Reinforce Stereotypes

AI shapes what we see, buy, and believe. But when algorithms serve us content based on past interactions, they also reinforce societal stereotypes.

Echo Chambers in Online Advertising

• AI-driven ads show men more financial and tech-related products.
• Women see lifestyle and home-related recommendations more often.
• If a user clicks on one type of content, the AI assumes they’ll only want similar content in the future, limiting exposure to broader career, financial, or educational opportunities.

Smart Home Assistants – Who is the Default Helper?
Most virtual assistants (Alexa, Siri, Google Assistant) have female voices. This wasn’t an accident; companies chose female voices because studies showed people were more comfortable hearing women in “assistant” roles.

The Real Issue?
AI reflects the roles society has historically assigned to different groups. And if we don’t challenge those roles, AI will keep enforcing them.

The Real Problem – AI Bias is a Design Choice

AI bias is not an inevitable flaw. It is a result of the choices we make:

• Who builds the AI (Are the development teams diverse?)
• What data it learns from (Is it representative of all populations?)
• Who audits the outcomes (Are there safeguards in place?)

AI is not inherently good or bad, but if we let it learn from a world full of bias and injustice without intervention, it will not only replicate those issues, it will automate and scale them faster than ever before.

Can It Be Fixed? Yes, Here’s How

Identifying AI bias is just the first step, fixing it requires deliberate, enforceable actions across industries. Here’s how we can turn ethical AI principles into real-world solutions:

1️. Diversify AI Teams & Training Data

• • AI must be trained on representative datasets reflecting different genders, races, and socioeconomic backgrounds.
  • Incentivize diversity in tech hiring through funding, scholarships, and fast-track AI training for underrepresented groups.

• Use federated learning* to improve data inclusivity while protecting privacy. 

2. Mandatory AI Bias Audits & Transparency

• • AI models must undergo regular, independent audits to detect and correct biases before deployment.

• Companies using AI for hiring, healthcare, or security must provide ‘Explainable AI’** decisions, allowing individuals to challenge unfair outcomes. 

3️. Stronger AI Regulations & Accountability

• AI ethics policies should ensure that AI used in hiring, healthcare, and security does not reinforce systemic inequalities.
• Companies should be held accountable for biased AI decisions, just as they would be for discriminatory human decisions.

4️. Public Awareness & User Control

• AI-driven decisions should be clearly labelled, with consumers given opt-out options and the right to human review.
• Schools and workplaces must introduce AI literacy programs, empowering individuals to challenge algorithmic biases.
• Individuals should demand transparency in AI use, from companies, governments, and online platforms.

Final Takeaway 

AI Shouldn’t Automate Discrimination, It Should Challenge It

AI doesn’t have a moral compass(yet), it just learns what we teach it. If we want a fairer, more equitable world, we need to actively design AI to challenge biases, not reinforce them.

The future of AI isn’t just about better technology, it’s about better choices. If we get it right, AI could help us build a world that is fairer than the one we inherited. But only if we demand it.

*Federated learning is a machine learning technique that involves multiple devices training a single model collaboratively

** Explainable AI (XAI) is the ability to explain how an AI system makes decisions in a way that people can understand. XAI helps build trust in AI systems by making them more accountable and transparent.