What I Learned About Bias in People Analytics (And Why Governments Should Pay Attention)
- Maria Alice Maia
- Apr 21
- 4 min read
Updated: Jul 16
More than a decade ago, when I was tasked with a Green Belt project at Ambev to build a predictive hiring model, the goal seemed clear and compelling: use data to improve the assertiveness of our recruitment process for salespeople. We would decode the DNA of a successful employee and build a machine to find them. The technical challenge was solvable. The ethical challenge, however, was something I would carry with me from the corporate world into my current work in academic research.
We quickly hit a wall, but it wasn’t a technical one. Our model, trained on years of historical hiring and performance data, was exceptionally good at finding candidates who were very much alike to the ones our leaders used to favor in interviews. It learned that certain universities, certain life paths, and certain demographic profiles were strong predictors of success. The model was working perfectly, but in doing so, it was threatening to create a monoculture—an efficient machine for filtering out anyone who broke the mold.
This was my first hands-on lesson in algorithmic bias. The data wasn’t lying, but it was replicating a biased story. Even the performance data we used as input was proven to be, in some cases, biased. The experience of dismantling and rebuilding that model, moving beyond simple predictive accuracy to measure and correct for bias, taught me more about fairness, strategy, and governance than any textbook could. And these lessons from the corporate trenches are not just business insights, but also urgent warnings for governments rushing to deploy AI in public services.
The Corporate Crucible: Defining "Fairness" at Ambev
The hardest part of my work leading People Analytics at Ambev was not building dashboards or running regressions; it was forcing a conversation about what "fairness" actually meant. When your data shows a pattern, you have to decide if that pattern is a recipe for success or a residue of historical bias.
We had to move beyond optimizing for accuracy and start auditing for impact. We had to measure abstract concepts like ‘Inclusion’ and ‘Engagement’ and understand how our models affected them. This meant asking difficult questions:
Were we building a tool that inadvertently perpetuated discrimination against certain groups?
Was "performance" itself being measured in a biased way?
What was the real business cost of low diversity, and how could we model the value of bringing in different profiles of talent?
We learned that "fairness" isn't a single metric you can toggle on or off. It's a complex, context-dependent dialogue. It’s a policy choice. We had to accept that a model with slightly lower predictive accuracy might actually be better for the long-term health and innovation of the company if it produced a more diverse and resilient workforce. This is a trade-off that machines can't make for you. Humans must.
From Corporate Risk to Societal Harm
This experience, in the controlled environment of a single company, is a stark warning for the public sector. If a well-resourced organization like Ambev has to wrestle so intensely with the ethics of a hiring model, the stakes are exponentially higher when a government uses an algorithm to decide who gets welfare benefits, which child is flagged as "at-risk," or how long a person is sentenced to prison.
The potential for systemic harm is immense. A biased corporate model costs you talent and market share. A biased public model can deny a citizen their liberty, their education, or their basic human dignity.
Recent AI research highlights this danger, showing that the internal representations in these models are systematically biased by extraneous properties that have nothing to do with the task at hand. Research also shows that well-intentioned efforts to improve data quality can inadvertently reduce the representation of certain cultural contexts, effectively erasing minority groups from the data that will shape their lives.
Three Critical Lessons for AI in Government
The lessons learned in the private sector are directly applicable to building a more just and effective digital state.
Your Data is a Dirty Mirror. Government data is not objective truth. It is a reflection of historical policies, societal biases, and past human decisions. Building an AI on this foundation without a rigorous, independent audit is like building a new courthouse on a toxic waste site. The contamination will spread.
Fairness is a Public Dialogue, Not a Technical Setting. A government cannot outsource ethics to its IT department. The decision of how to balance competing values—like efficiency versus equity—is a political act. This requires public deliberation, not just technical validation.
Mandate Audits Before Deployment. We must invert the burden of proof. The EU AI Act's risk-based approach is a start. For any "high-risk" system used in public services, the government agency must be required to prove—through independent, transparent audits—that the system is safe, accurate, and equitable before it is deployed. The risk should be on the creator of the system, not the citizen who is subject to its decision.
My journey has taken me from decoding business and consumer behavior to decoding the drivers of political polarization and public policies. The common thread is a deep-seated belief that we can, and must, measure what matters. The messy, human-centric challenges of deploying AI responsibly in the corporate world are the most valuable playbook we have for getting it right in the public square. This knowledge isn’t mine to keep.
If you are a leader in HR or public policy grappling with these issues, I invite you to join my email list for more no-nonsense, research-backed insights.
And if you have a real-world case where technology, data, and human behavior collide, let’s talk. I'm opening up a few 20-minute consultation slots to help solve these critical challenges together.
#PeopleAnalytics #AlgorithmicBias #AIethics #HRTech #GovTech #PublicPolicy #Leadership #MariaAliceMaia
