Why does it matter if AI is unbiased?

27th June 2025

In the future, AI may decide what you can and can’t do based on things out of your control – is that fair? Or should we be more careful about how we train our AI?

Like humans, AI has opinions: it learns and develops based on experience and, just like us, creates points of view that aren’t always completely accurate, just aligned to our knowledge or experience. We’ve previously looked at how AI can become biased: now we want to dive into some real-world examples and why it is important that we don’t pass biases on to AI.

Here are some examples of when AI being biases could have a negative impact on people:

Facial recognition: these systems have been known to misidentify people of colour more than white individuals, likely because the media they are fed contained more white people than people of colour. This could lead to people being mistaken for criminals in court cases if facial recognition in the camera can’t distinguish between one person and another, which is massively unjust.

Hiring algorithms: some have been known to favour male candidates over female ones. This is based on historical data, so it makes sense why the AI would come to this conclusion as traditionally more men worked and had jobs, more men were prompted to higher positions than women and more men took credit for accomplishments than women. In 2025 though, you’d like AI to reflect steps towards gender equality in the workplace, rather than outdated stereotypes.

Loan approval: AI systems that can approve or deny loans may not permit credit to certain demographics based on their location or background. Again, the AI can’t help developing from what it is given, but it means that with everyone grouped into this one biased opinion, people who are prime candidates for loans are overlooked.

This is why it is important to ensure AI is properly trained without biases: otherwise, huge groups of people are lumped in together, potentially impacting their finances, their work or their livelihoods all based on interpretation of unfair data. If you use AI, work to make sure it is ethical, to create better and more accurate models.

If you’d like more information about AI biases and their impacts, please contact Interfuture Systems.