AI, Bias, And Empathy: How To Ensure Fairness In An AI World
Algorithms have not only enhanced how we work, they are reshaping how we hire, assess value, and define success. But what happens when they also absorb our deepest biases, judging people before they even get a chance to show up?
AI can be a powerful tool for improving how organizations perform and how productive employees are. But when algorithms are biased, they can undermine fair hiring practices, leading to discrimination based on gender, race, age, or even faith. A Scientific Reports paper shows that when employees were evaluated by AI systems (like algorithms or automated tools) instead of human managers, they were more likely to feel disrespected or devalued.
Biases in the datasets used to train AI models can both skew recommendations and the decision-making processes of the leaders who use them.
In the past, hiring teams handled tasks like reviewing resumes, onboarding new employees, and conducting performance evaluations. These moments created opportunities to build connection, show curiosity, and develop meaningful workplace relationships. But in today's AI-driven workplace, many of these tasks can be automated to save time and boost efficiency. The downside? As we replace human interaction with algorithms, we risk losing those moments of genuine connection. And while human decision-making can be biased, whether consciously or not, AI can carry those same biases too, just in less visible ways.
In a previous article, I explored whether empathy is still essential in the age of AI, or if we can simply outsource it. While the benefits of using AI in the workplace are clear, there are some challenges it can't fix, like the biases built into AI systems and the crucial role empathy plays in addressing them.
Empathy is a vital first step toward simply understanding how people feel about AI and the future of work. A recent report from the Pew Research Center highlights a striking divide: '73% of AI experts surveyed say AI will have a very or somewhat positive impact on how people do their jobs over the next 20 years.' In contrast, among U.S. adults, 'that share drops to 23%.' An empathetic leader will get curious about the inverse of this statistic: the 77% of U.S. adults who don't believe AI will have a positive impact on how they do their jobs now and in the future.
An empathetic leader wants to acknowledge hardship and listen to the perspectives that often go unspoken — without fear. In an episode of The Empathy Edge podcast, speaker, author, and filmmaker Minter Dial highlights the key questions we need to ask to bring heart into AI and the workplace: 'What is your intention? Before you bring in the AI, what are you trying to achieve? Is it linked to your strategy? Or is it just linked to saving money, cutting corners, getting rid of the hassle of dealing with people?'
Until we get clear on both our relational and business goals, we can't truly embed empathy into the way we use AI.
Here are three ways leaders can embed empathy in AI-driven hiring, performance, and decision-making processes.
Transparency Over Opacity
Transparency is the foundation of an empathetic workplace. It's essential for both hiring managers and job seekers to understand required skills and pay scales. Leaders want insight into what their teams need, which benefits attract top candidates, where to find great talent, and what skills are worth developing. At the same time, employees deserve to know how AI is being used in HR. The more we know, the more confidently we can make decisions.
The same principle applies to AI. Recruitment algorithms should be transparent and easy to audit. As David Paffenholz writes for the Forbes Technology Council, 'algorithms must account for gaps in candidate data and use systems to evaluate passive and active candidates equitably. This inclusivity ensures your AI tools identify the best talent rather than the most visible talent.'
Create Diverse Development Teams
Empathy starts in the design room. A PwC report on algorithmic bias and trust emphasizes that involving people from diverse backgrounds in developing and testing AI systems is key to building trust. When teams include a mix of races, genders, ages, economic backgrounds, education levels, and abilities, they're better equipped to spot and address different types of bias. As the report notes, 'Building diverse teams helps reduce the potential risk of bias falling through the cracks,' because 'each will have their own view of the threat of bias and how to help mitigate it.'
Juji, an AI company pioneering human-centered agents that combine generative and cognitive AI to automate complex, nuanced business interactions, aims to create empathic AI solutions. Co-founder and CEO Dr. Michelle Zhou, in her interview on The Empathy Edge podcast, explains that while AI is designed to identify patterns and similarities, becoming more empathetic means learning to recognize differences too, and not just what's common.
Still, as the Pew Research Center report shows, public trust in AI, especially in the workplace, is far from guaranteed. That's why human oversight remains critical for sensitive decisions. Even if humans can't process vast datasets as quickly, for employees who are cautious or skeptical of AI, knowing there's a person involved in final hiring and performance decisions can make all the difference.
Conduct Empathy Audits First
Effective people management starts with putting people first, and management second. According to Businessolver's 2024 State of Workplace Empathy Executive Report, leaders need to regularly reflect on whether they're truly meeting employees' needs and expectations. That also means being open and honest about where they may be falling short. From this place of transparency, empathy can be practiced, not just by supporting employees as professionals, but as whole people, embedded in broader communities.
When leaders tune into the human dynamics within their organizations, especially how past decisions have affected different groups, they gain valuable insight into their own internal biases. This kind of reflection doesn't just benefit workplace culture; it also informs better practices for AI audits. As an Emerald Insights report on AI bias auditing explains, involving diverse stakeholders and community voices is essential to building rigorous, inclusive audit processes. In this way, empathy audits are more than just a tool for supporting teams, they lay the foundation for human-centered, bias-aware AI systems.
As AI transforms the workplace, empathy must remain at the center. It's not just about smarter systems, it's about fairer, more human ones. By leading with empathy, prioritizing transparency, and involving diverse voices, we can design AI that supports both performance and people. The future of work should be efficient, yes. But never at the cost of connection or equity.
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