Using biased statistics in hiring makes it more difficult to predict job performance. Photo via Getty Images

The Latin phrase scientia potentia est translates to “knowledge is power.”

In the world of business, there’s a school of thought that takes “knowledge is power” to an extreme. It’s called statistical discrimination theory. This framework suggests that companies should use all available information to make decisions and maximize profits, including the group characteristics of potential hires — such as race and gender — that correlate with (but do not cause) productivity.

Statistical discrimination theory suggests that if there's a choice between equally qualified candidates — let's say, a man and a woman — the hiring manager should use gender-based statistics to the company's benefit. If there's data showing that male employees typically have larger networks and more access to professional development opportunities, the hiring manager should select the male candidate, believing such information points to a more productive employee.

Recent research suggests otherwise.

A peer-reviewed study out of Rice Business and Michigan Ross undercuts the premise of statistical discrimination theory. According to researchers Diana Jue-Rajasingh (Rice Business), Felipe A. Csaszar (Michigan) and Michael Jensen (Michigan), hiring outcomes actually improve when decision-makers ignore statistics that correlate employee productivity with characteristics like race and gender.

Here's Why “Less is More”

Statistical discrimination theory assumes a correlation between individual productivity and group characteristics (e.g., race and gender). But Jue-Rajasingh and her colleagues highlight three factors that undercut that assumption:

  • Environmental uncertainty
  • Biased interpretations of productivity
  • Decision-maker inconsistency

This third factor plays the biggest role in the researchers' model. “For statistical discrimination theory to work,” Jue-Rajasingh says, “it must assume that managers are infallible and decision-making conditions are optimal.”

Indeed, when accounting for uncertainty, inconsistency and interpretive bias, the researchers found that using information about group characteristics actually reduces the accuracy of job performance predictions.

That’s because the more information you include in the decision-making process, the more complex that process becomes. Complex processes make it more difficult to navigate uncertain environments and create more space for managers to make mistakes. It seems counterintuitive, but when firms use less information and keep their processes simple, they are more accurate in predicting the productivity of their hires.

The less-is-more strategy is known as a “heuristic.” Heuristics are simple, efficient rules or mental shortcuts that help decision-makers navigate complex environments and make judgments more quickly and with less information. In the context of this study, published by Organization Science, the heuristic approach suggests that by focusing on fewer, more relevant cues, managers can make better hiring decisions.

Two Types of Information "Cues"

The “less is more” heuristic works better than statistical discrimination theory largely because decision makers are inconsistent in how they weight the available information. To factor for inconsistency, Jue-Rajasingh and her colleagues created a model that reflects the “noise” of external factors, such as a decision maker’s mood or the ambiguity of certain information.

The model breaks the decision-making process into two main components: the environment and the decision maker.

In the environment component, there are two types of information, or “cues,” about job candidates. First, there’s the unobservable, causal cue (e.g., programming ability), which directly relates to job performance. Second, there's the observable, discriminatory cue (e.g., race or gender), which doesn't affect how well someone can do the job but, because of how society has historically worked, might statistically seem connected to job skills.

Even if the decision maker knows they shouldn't rely too much on information like race or gender, they might still use it to predict productivity. But job descriptions change, contexts are unstable, and people don’t consistently consider all variables. Between the inconsistency of decision-makers and the environmental noise created by discriminatory cues, it’s ultimately counterproductive to consider this information.

The Bottom Line

Jue-Rajasingh and her colleagues find that avoiding gender- and race-based statistics improves the accuracy of job performance predictions. The fewer discriminatory cues decision-makers rely on, the less likely their process will lead to errors.

That said: With the advent of AI, it could become easier to justify statistical discrimination theory. The element of human inconsistency would be removed from the equation. But because AI is often rooted in biased data, its use in hiring must be carefully examined to prevent worsening inequity.

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This article originally ran on Rice Business Wisdom based on research by Rice University's Diana Jue-Rajasingh, Felipe A. Csaszar (Michigan) and Michael Jensen (Michigan). For more, see Csaszar, et al. “When Less is More: How Statistical Discrimination Can Decrease Predictive Accuracy.”

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Houston femtech co. debuts new lactation and wellness pods

mom pod

Houston-based femtech company Work&, previously known as Work&Mother, has introduced new products in recent months aimed at supporting working mothers and the overall health of all employees.

The company's new Lactation Pod and Hybrid Pod serve as dual-use lactation and wellness spaces to meet employer demand, the company shared in a news release. The compact pods offer flexible design options that can serve permanent offices and nearly all commercial spaces.

They feature a fully compliant lactation station while also offering wellness functionalities that can support meditation, mental health, telehealth and prayer. In line with Work&'s other spaces, the pods utilize the Work& scheduling platform, which prioritizes lactation bookings to help employers comply with the PUMP Act.

“This isn’t about perks,” Jules Lairson, Work& co-founder and COO, said in the release. “It’s about meeting people where they are—with dignity and intentional design. That includes the mother returning to work, the employee managing anxiety, and everyone in between.”

According to the company, several Fortune 500 companies are already using the pods, and Work& has plans to grow the products' reach.

Earlier this year, Work& introduced its first employee wellness space at MetroNational’s Memorial City Plazas, representing Work&'s shift to offer an array of holistic health and wellness solutions for landlords and tenants.

The company, founded in 2017 by Lairson and CEO Abbey Donnell, was initially focused on outfitting commercial buildings with lactation accommodations for working parents. While Work& still offers these services through its Work&Mother branch, the addition of its Work&Wellbeing arm allowed the company to also address the broader wellness needs of all employees.

The company rebranded as Work& earlier this year.

Rice biotech studio secures investment from Modi Ventures, adds founder to board

fresh funding

RBL LLC, which supports commercialization for ventures formed at the Rice University Biotech Launch Pad, has secured an investment from Houston-based Modi Ventures.

Additionally, RBL announced that it has named Sahir Ali, founder and general partner of Modi Ventures, to its board of directors.

Modi Ventures invests in biotech companies that are working to advance diagnostics, engineered therapeutics and AI-driven drug discovery. The firm has $134 million under management after closing an oversubscribed round this summer.

RBL launched in 2024 and is based out of Houston’s Texas Medical Center Helix Park. William McKeon, president and CEO of the TMC, previously called the launch of RBL a “critical step forward” for Houston’s life sciences ecosystem.

“RBL is dedicated to building companies focused on pioneering and intelligent bioelectronic therapeutics,” Ali said in a LinkedIn post. “This partnership strengthens the Houston biotech ecosystem and accelerates the transition of groundbreaking lab discoveries into impactful therapies.”

Ali will join board members like managing partner Paul Wotton, Rice bioengineering professor Omid Veiseh, scientist and partner at KdT Ventures Rima Chakrabarti, Rice alum John Jaggers, CEO of Arbor Biotechnologies Devyn Smith, and veteran executive in the life sciences sector James Watson.

Ali has led transformative work and built companies across AI, cloud computing and precision medicine. Ali also serves on the board of directors of the Drug Information Association, which helps to collaborate in drug, device and diagnostics developments.

“This investment by Modi Ventures will be instrumental to RBL’s growth as it reinforces confidence in our venture creation model and accelerates our ability to develop successful biotech startups,” Wotton said in the announcement. "Sahir’s addition to the board will also amplify this collaboration with Modi. His strategic counsel and deep understanding of field-defining technologies will be invaluable as we continue to grow and deliver on our mission.”