DOES UNCONSCIOUS BIAS STILL MATTER IN 2025？

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Unconscious bias has been an important phrase in the world of diversity for a long time - so long, in fact, that some people are starting to doubt whether it’s still relevant today. Don’t we all know enough about unconscious bias already to avoid it? Haven’t we moved past the point where this is an issue and got to a position of better self-awareness?

Unconscious bias still matters in today’s world because although we are all more aware of it, it’s still very prevalent. In fact, it’s thought to be much more common than conscious bias, because it’s far easier for it to go unchallenged for years, decades, or even lifetimes. When somebody has a conscious bias, it’s much easier for them to work against it, improve their understanding of the subject, and gradually become less biased.

Unconscious biases, though, are much harder for us to deal with even when we actively want to, precisely because they happen on a level that we aren’t really aware of. It’s therefore difficult to prevent them from filtering through into our conscious minds and affecting how we behave. After all, you can’t stop what you aren’t aware of!

What’s more, it’s thought that training people to be more aware of biases and to subject their thought processes to more scrutiny is not necessarily enough to change the behavior that stems from these biases. That means that even if we are aware of the problems unconscious biases can cause, we aren’t necessarily well-equipped to stop them from affecting us. Even those who are self-aware and eager to interact with the world in unbiased ways are likely to struggle.

Unfortunately, this problem is both pervasive and very serious. It can, quite literally, kill. Let’s take biases in the healthcare world as an example.

If a doctor is predisposed to take men’s symptoms more seriously than women’s or to dismiss black patients more quickly than white patients, this is going to cause major disparities in treatment. Perhaps they’re more likely to prescribe pain medication to men because they believe men are braver and so less likely to complain. Maybe they’re more likely to dismiss women’s physical symptoms as emotional. Fat phobia is another example of a very serious bias in the medical world.

Remember, none of these will be conscious thought processes that the medical professional can short-circuit and address. They’re subconscious ideas that affect what decisions are made and have very serious implications for patients. These kinds of biases are widespread and misdiagnoses are made every day, even though the entire ethos of the healthcare field involves providing equal treatment to everyone.

Of course, you can apply the same principle to any field, whether it’s teachers who spend less time on religious students because they think they are less intelligent, or policemen who are more likely to shoot at a black person than a white one because they believe black people are more inherently violent. Unconscious bias also appears in a myriad of ways throughout organizations, having an impact on people across the board when it comes to hiring, firing, work assignments, layoffs, training opportunities, and more.

Unconscious bias is hardwired in the brain

Unconscious bias stems from the brain’s natural tendency to categorize information quickly. This mental shortcut, known as  heuristics, evolved to help humans make rapid decisions in survival situations. However, in modern contexts, these biases can lead to unfair judgments about people based on race, gender, age, appearance, or other characteristics.

Even in 2025, as we become more aware of these biases, they will still operate beneath the surface, influencing hiring, promotions, performance evaluations, and everyday interactions. The post-pandemic world has amplified these challenges, as remote work, economic uncertainty, and shifting workplace dynamics have created new opportunities for bias to manifest. In a VUCA world marked by volatility, uncertainty, complexity, and ambiguity, compounded by geopolitical tensions, wars, and polarized political landscapes, people may unconsciously retreat to familiar patterns and biases as a coping mechanism. For instance, during times of crisis, individuals may favor those who resemble themselves or align with their worldview, perpetuating exclusion and inequality.

Additionally, the rise of hybrid work models has introduced new forms of bias, such as proximity bias, where in-office employees are favored over remote workers. Despite increased awareness and technological advancements, the persistence of unconscious bias in such a turbulent world underscores the need for proactive, systemic solutions to ensure fairness and inclusivity in every aspect of life.

Technology can amplify bias

As technology becomes even more integrated into our lives, the risk of bias being embedded in algorithms and systems will remain a significant concern. While technology has the potential to reduce bias through objective, data-driven processes, it often reflects and even exacerbates the biases present in the data it’s trained on. For examples:

AI and algorithms: Machine learning models trained on biased data can replicate and even amplify existing biases. One element of this is that facial recognition systems have been shown to have higher error rates for people of color.

Social media algorithms: Platforms often reinforce echo chambers, exposing users to content that aligns with their existing beliefs and biases.

Automated decision-making: Tools used for resume screening, performance evaluations, or even healthcare diagnostics can inadvertently favor certain groups over others.

As artificial intelligence and automation become even more pervasive, the stakes will only grow higher. Without careful oversight, these technologies can institutionalize bias on a massive scale, making it harder to detect and address. For example, biased algorithms in hiring tools could systematically exclude qualified candidates from underrepresented groups, while biased healthcare algorithms could lead to unequal treatment for marginalized communities.

Moreover, the rapid pace of technological innovation often outstrips the development of ethical guidelines and regulatory frameworks. This creates a gap where biased technologies can be deployed before their potential harms are fully understood. For instance, generative AI tools like ChatGPT and Deepseek, while revolutionary, have been shown to produce biased or harmful outputs when prompted with certain questions, highlighting the need for ongoing vigilance and improvement.

Bias is persistent because it’s unconscious

The very nature of unconscious bias makes it difficult to eradicate. People often don’t realize they’re acting on biased assumptions, and even well-intentioned individuals can perpetuate bias without meaning to. Since these biases operate below the surface, they can subtly shape decision-making, workplace dynamics, and opportunities, reinforcing systemic inequities over time.

While training and awareness programs have made progress, they might not always be effective, and ultimately, they are not enough on their own. In 2025, organizations must take proactive and systemic approaches  to addressing bias. This includes structured decision-making, where standardized criteria are used for hiring, promotions, and evaluations to minimize subjectivity. Bias audits should be conducted regularly to identify and correct patterns of bias in workplace practices. Finally, accountability measures must be in place to ensure leaders and teams are actively fostering inclusive environments and driving meaningful change.  

In a world that is becoming increasingly diverse and interconnected, unconscious bias isn’t just a moral issue; it’s a business problem. Organizations that fail to address bias risk falling behind in talent acquisition, innovation, and overall performance.

Time to get hands-on!

To reduce bias in AI, organizations and policymakers must take a proactive, multi-layered approach by integrating ethics into AI development. Key actions include:

  • Inclusive data design: Ensuring datasets are diverse to prevent biased outcomes.

  • Regular bias audits: Independently testing AI models to detect and correct bias.

  • Algorithmic transparency: Making AI decisions traceable for accountability.

  • Human oversight: Embedding expert reviews to refine automated judgments.

  • Adaptive policies: Establishing governance that evolves with AI advancements.