Invisible Matchmakers: How Algorithms Pa...

If we want to get fair outcomes, then we need to build fairness into algorithms.

Whether you’re looking for a job, a house, or a romantic partner, there’s an app for that. But as people increasingly turn to digital platforms in search of opportunity, Daniela Saban says it’s time we took a critical look at the role of algorithms, the invisible matchmakers operating behind our screens.

Saban is an Associate Professor of Operations, Information & Technology at Stanford Graduate School of Business whose research interests lie at the intersection of operations, economics, and computer science. With algorithms significantly influencing who gets matched with opportunities, she advocates for building “equity into the algorithm.”

In this episode of If/Then: Business, Leadership, Society, Saban explores how properly designed algorithms can improve the fairness and effectiveness of matching processes. If we want algorithms to work for good, then we need to make conscious choices about how we design them.

Key Takeaways: 

• Algorithms shape online experiences and real-world outcomes: On dating apps, volunteer matching services, and job websites, algorithms play a crucial role in matching people with opportunities. While these matchups are facilitated in the digital domain, they impact real people in the real world.

• Algorithms are not neutral: Algorithms reflect the values and priorities of their designers and have the power to either perpetuate or mitigate inequities.

• Thoughtful algorithm design can improve outcomes for all: Saban's research demonstrates that algorithms can be optimized to create more balanced and successful matching experiences. By consciously choosing to prioritize fairness and equity in algorithm design, we can create systems that work for the good of all users.

More Resources:

• Daniela Saban, Stanford GSB faculty profile
• Stanford GSB Insights, "Cupid’s Code: Tweaking an Algorithm Can Alter the Course of Finding Love Online"
• "Improving Match Rates in Dating Markets Through Assortment Optimization" as published in Manufacturing & Service Operations Management

If/Then is a podcast from Stanford Graduate School of Business that examines research findings that can help us navigate the complex issues we face in business, leadership, and society. Each episode features an interview with a Stanford GSB faculty member.

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