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AI bias mitigation playbook: Strategies you need to know
AI can be biased too, affecting critical corporate decisions. Effective mitigation requires diverse teams, tool audits and responsible data practices — not just technical fixes.
Companies are introducing AI across their operations in the hopes of improving efficiency, but sometimes these new AI tools don't just offer answers – they create new challenges. Users are increasingly discovering that their AI programs have bias built into their systems, which organizations need to address before it becomes a widespread issue.
Bias is a phenomenon that nearly all adults experience, resulting from the culmination of their personal experiences. It manifests as a prejudice toward or against something, one that is unsupported by facts; for instance, believing that one group of people is more likely to work hard because of their nationality, or believing that everyone with a specific accent is less intelligent. This often leads to unfair outcomes and subjective decision-making, both at the individual and systemic levels. When AI has a bias, it's because it has formed similarly prejudicial opinions on a topic, group or individual, as a result of the data it was trained on. The results can be similarly damaging.
Human bias is often understood to be an inevitable result of our individual lives, but it is still recognized as harmful and, therefore, something to mitigate. Rather than shaming people for their personal biases, the focus must be on identifying areas where bias has an impact and reducing its effects. This is equally true for the bias that occurs within AI programs and, therefore, within business operations.
"Mitigating AI bias is an ongoing effort," said Ora Tanner, CEO of Black Unicorn Education. "No system has 0% bias because it is built on data from us humans– and I've never met a human who didn't have a bias."
How AI bias causes harm in the workplace
AI is, simply put, trending. Executives in all industries are looking to get a competitive edge in their market, but seemingly everyone has come up with the same fix: to bring in AI. It's easy to see why, as AI companies offer easy integrations that appear to solve all modern workplace problems. This makes AI bias even more dangerous, as many businesses don't know to look out for it – or what to look for.
"Most companies adopt AI tools because of the promise of increased efficiency, productivity, decreased cost, innovation potential and improving customer experience," said Tanner. "Each of these aspects is a potential point of entry for AI bias."
Tanner gives the example of an AI resume-scanning platform that determines whose resume gets seen by the hiring committee, or HR platforms that deploy AI to help determine who gets raises or promotions. Humans often believe that technology will be able to make a more objective decision than they would, since it isn't clouded by emotion or personal experience. In reality, the AI is shaped by the large language model (LLM) it was trained on; it is subjective after all. And its influence can be deeply biased.
A test, conducted by LSE researcher Ruhi Khan, found ChatGPT consistently described male candidates as more capable, higher performing, and better suited to leadership than female candidates – even when the only difference provided in the prompt was a gendered name. This gender bias could have an outsized effect on HR decision-making, especially when placed in the context of a June 2025 study from ResumeBuilder that found 60% of hiring managers rely on AI to make decisions about their direct reports. These decisions include determining raises (78%), promotions (77%), layoffs (66%) and terminations (64%).
"With more than half of managers using biased AI systems, women and other traditionally impacted groups may find it increasingly difficult to make meaningful gains in the workforce," said Tanner, referencing the ResumeBuilder study.
Strategies to mitigate AI bias in a corporate setting
The potential damage of AI bias is clear, but mitigating its impact is a bit more complex. Fortunately, there are a few steps that can be followed to audit, resolve and monitor AI bias within the workplace.
Get all employees involved
To make meaningful changes, the entire company needs to agree that this is a present and harmful issue.
"Mitigation of AI bias shouldn't only be left to the IT team," said Tanner. "All employees within a workplace, regardless of title or rank, should be educated about AI bias and its potential harm to individuals, an organization and society at large."
By engaging the full workforce with the issue, the company has a better chance of catching the various instances of AI bias occurring internally. This is because employees from all over the business will be on the lookout, as will employees of different backgrounds and perspectives. To combat prejudice, it is critical to listen to diversity of thought and cultivate a culture of ethical responsibility around AI use.
It may be helpful to establish a task force to organize and lead bias mitigation efforts, especially within larger enterprises. In this instance, teams should comprise multidisciplinary, diverse members, not just members of the IT team; this is as much about bias as it is about AI.
Create an inventory of all AI tools, platforms and applications
As with any corporate mitigation effort, a comprehensive audit must come first. To catch all instances of AI bias, every AI deployment must be identified and organized into a single, centralized inventory. This will create greater visibility around AI usage within the company and make it easier for future frameworks to be applied consistently and at scale. This inventory can also grow and evolve alongside the company's own AI usage, ensuring that any new tools are similarly monitored.
Conduct bias assessments
This is the meat of the strategy: Assessing each item in the inventory for potential vulnerabilities to AI bias. This could look like running tests on new scenarios to determine whether the AI tool provides different recommendations to otherwise-identical inputs when presented with certain identifying information. Alternatively, it could look like conducting an analysis on all previous outputs to see if there are any concerning patterns or trends that suggest bias.
In instances where AI bias is identified, connect those tools with the datasets they were trained on. If this is an externally developed tool, this might prompt a conversation with the provider. For applications trained on internal data, those datasets should be reviewed to see where the bias is coming from.
Practice responsible dataset development for AI training
For long-term mitigation, it is important to not just catch current instances of bias but also future-proof current AI models. This means establishing a responsible method for data collection and management, one that holds objectivity and impartiality at its heart. Teams would also be wise to regularly review how these operations perform, to ensure that there is no shifting away from the original goalposts over time.
Consult existing AI bias mitigation frameworks
Companies don't need to enact these strategies without support. There are several existing resources that are freely available to help guide teams through their own AI bias audits and build more objective systems going forward. Tanner recommends the following frameworks as useful guides to AI bias mitigation at the enterprise level:
- Berkeley Haas AI Mitigation playbook.
- FabriXAI Business Framework to Reduce AI Bias.
- National Institute of Standards and Technology (NIST) AI Risk Management Framework.
Communicate activity around AI bias mitigation
Humans are flawed creatures, so it is unsurprising that human inventions may also share some of these flaws. In the case of bias, companies can do a lot of good by simply acknowledging and sharing any instances of AI bias with their teams and communicating their efforts to correct them. Not only does this AI transparency help to foster trust between employees and the company, but it also ensures that all individuals within the workforce are brought into the larger AI bias mitigation effort.
Common mistakes that management makes around AI bias
Mitigating the effects of AI bias is not a simple task, but executives can make it harder for themselves if they approach it from the wrong angle.
"One of the biggest mistakes executives make is addressing AI bias from a purely technical perspective," said Tanner. "While modifying datasets so they are more representative and ensuring machine learning algorithms are fair is needed, this is not sufficient."
Even NIST describes AI bias as being about more than just data. According to Tanner, reducing its impact on operations requires "a diverse group of stakeholders who can address the social, political, ecological, economic, and ethical dimensions of AI bias." By understanding the human bias that underpins AI bias, teams can be much more effective at resolving this issue long-term.
Madeleine Streets is a senior content manager for WhatIs. She has also been published in 'TIME,' 'WWD,' 'Self' and Observer.
