Artificial intelligence is often discussed as if it were objective and impartial. As we increasingly rely on AI to make real-world decisions for various purposes across industries, many people treat it as simply a neutral tool that crunches numbers and spits out accurate answers.
But the truth is, these technologies and systems aren’t infallible. Bias is creeping into these systems and can affect the lives of people in our community.
With AI Appreciation Day earlier this month on July 16th, it’s the perfect time to pause and understand the risks so you can use this powerful technology the right way in your everyday operations.
This article breaks down how bias makes its way into AI, what it looks like in practice, and most importantly, how your organization can recognize it, mitigate it, and rely on more trustworthy, responsible systems.
Where Does AI Bias Come From?
Artificial intelligence depends on machine learning to function. That means that it uses algorithms to analyze data, recognize patterns, and make decisions or predictions based on what it learns. Instead of following fixed rules from a developer, it learns from new data and feedback to improve over time.
So where does AI bias come from? The key word here is data, data that comes from people, who decide:
- what to include
- what to exclude
- how to label it
As a result, AI identifies and mimics patterns in datasets that reflect human choices and historical realities. And if the data is incomplete, imbalanced, or shaped by social inequalities, the AI ends up learning a distorted version of reality. It will absorb and repeat bias, even reinforcing it at scale.
What does this look like in practice? Let’s think about the fact that more than 90% of companies today use automated, AI-powered candidate screening tools to support the hiring process. If that tool learns from past resumes at a company, it might end up favoring male candidates simply because they were hired more often in the past. Or take facial recognition software. If it’s mostly trained on light-skinned faces, it may struggle with accuracy on darker skin tones.
Why Does AI Bias Matter?
When generative AI first came on the scene, people had fun playing around with what the tools could accomplish. Writing an email with the click of a button? Instantly turning a rough idea into a poem? And what about generating a recipe based on whatever’s in your fridge? It felt fun, sometimes silly, or even exciting.
But artificial intelligence isn’t just a tool that can help everyday people accomplish tasks faster. AI is now part of systems that carry real weight and making decisions that affect people in real and lasting ways.
When those systems are biased, the consequences can be serious:
- Hiring processes: Qualified candidates can be overlooked, making it harder for them to access fair job opportunities. This is especially those who don’t fit traditional expectations for a role or come from underrepresented backgrounds. AI systems have shown bias against younger candidates, for example.
- Loan approvals: Communities that have historically been denied credit may continue to face the same barriers if AI tools are fed human biases. One research study showed that an algorithm in one system unfairly assigned extra mortgage interest to prospective borrowers from people in groups who are legally protected from discrimination.
- Policing and surveillance: Certain communities, particularly minorities, may face increased police attention and monitoring when AI systems rely on flawed or biased facial recognition and crime data. This can result in individuals being unfairly targeted, experiencing more frequent stops or arrests that impact their quality of life.
Over time, this type of bias will erode people’s trust in emerging technologies and the organizations that use them. If we want people to keep connecting with us, we need to use tools in ways that show our values and treat everyone fairly.
How Organizations Can Avoid AI Bias
Avoiding artificial intelligence altogether isn’t the solution to bias. As Dr. Rhoda Au, PhD, put it, “There’s a lot of pushback against AI because it can promote bias, but humans have been promoting biases for a really long time.”
Bias isn’t unique to this specific technology; it’s a human issue that shows up in the systems we build. Instead of rejecting this emerging technology, we need to rethink how we use it.
Biased outputs also aren’t a given. With the right approach, you can use AI in a way that’s fairer, more accurate, and better aligned with your goals. Here’s how to get started:
Use artificial intelligence strategically and intentionally
It’s a lot easier to use AI unethically when you approach its use without purpose or careful planning. Prioritizing effective governance with concrete policies and procedures can make a huge difference. You’re much more likely to avoid replicating harmful biases if you follow best practices to integrate AI into your operations, such as:
- Setting clear rules for how organizational data is used
- Monitoring results from AI systems
- Regularly conducting bias audits to test AI systems for discriminatory patterns
- Use diverse datasets that reflect the full spectrum of human experience.
Taking this hands-on approach will ultimately make your AI work smarter and fairer for everyone.
Prioritize transparency with AI
If you want to build trust with your users and other stakeholders while reducing risk, it’s important to understand how your AI systems make decisions and make that information accessible to others. Avoid tools that operate like a black box and choose tools that provide explainable outputs, so your team isn’t left guessing why the AI made a certain recommendation.
Keep clear internal records of what data your AI systems are learning from, how decisions get reviewed, and who’s in charge of keeping an eye on things. It will be a lot easier to catch mistakes, explain outcomes, and stay on track.
Rely on human oversight to guide AI decisions
Keeping humans in the loop is one of the most effective ways to prevent AI systems from making decisions that are unfair, inaccurate, or harmful.
While AI can process massive amounts of data and spot patterns quickly, it doesn’t understand nuance, context, or ethics the way people do.
It’s important to create policies that define exactly when human oversight is needed—such as before your organization makes major decisions or when using sensitive personal data—and to follow through consistently. When people stay involved in the process, they can step in to catch problems early and correct course before harm is done.
But human oversight only works when the people involved are equipped to do it well. You’ll also want to train staff to understand how AI works, where it might fall short, and how to evaluate its outputs critically. Just as important are feedback loops that enable employees or users to easily flag issues, ask questions, or suggest improvements.
Discover How PC Corp Puts Responsible Technology First
AI can be incredibly useful, but only when it serves people fairly. Too often, systems built with good intentions end up unintentionally amplifying inequality if we don’t actively design them to do better.
At PC Corp, we see building fair and transparent AI systems as part of our commitment to meaningful community impact. We believe technology should reflect the values of the people it serves. For us, AI is just one more area where those values come to life, helping us create systems that are not only effective, but also equitable and responsible.
Our DEI Committee plays a vital role in shaping how we approach technology, yes, but also in helping us create a welcoming environment. This group guides various internal initiatives, such as hiring practices that give equal access to opportunities, reviewing policies through an equity lens and supporting our commitment to becoming an Alberta Living Wage Employer and a recognized UNGC Forward Faster company for gender equality.
Curious to know more about our approach to technology? Contact us to discuss how you can work with community-driven IT experts.
