DeepSummary
The podcast episode discusses the role of algorithms in matching people with opportunities, specifically in the contexts of dating apps and volunteer matching platforms. Daniela Saban, an associate professor at Stanford Graduate School of Business, shares insights from her research on how algorithms can be designed to improve fairness and effectiveness in these matching processes.
Saban's team developed an algorithm for a dating app that increased the number of matches by accounting for factors like user preferences, mutual interest, and recent user activity. They found that incorporating users' history and adjusting for biases in liking or matching behavior led to better outcomes.
For the volunteer matching platform, Saban's research revealed an imbalance where some opportunities received an overwhelming number of signups while others received none. By tweaking the algorithm to display opportunities based on the number of signups already received, they were able to distribute volunteers more equitably without significantly impacting the overall number of signups.
Key Episodes Takeaways
- Algorithms play a crucial role in matching people with opportunities, shaping real-world experiences and outcomes.
- Algorithms are not neutral; they reflect the values and priorities of their designers and can perpetuate or mitigate inequities.
- By accounting for factors like user preferences, mutual interest, and recent activity, algorithms can be optimized to improve match rates and distribute opportunities more fairly.
- Incorporating user history and adjusting for biases in behavior can lead to better matching outcomes.
- Designing algorithms with a conscious focus on fairness and equity can create systems that work for the good of all users.
- Providing meaningful and positive experiences is essential for maintaining user engagement and avoiding discouragement.
- Gender dynamics and imbalances can influence user behavior on platforms like dating apps, which should be considered in algorithm design.
- Field experiments and validation are crucial for testing and refining algorithm performance in real-world settings.
Top Episodes Quotes
- “So every day, I'm sure you use a lot of apps on your phone, and what you may not realize is that when you open the app, the app is making a lot of decisions in real time, and not only for you, but also for many other users of that app.“ by Daniela Savan
- “So what we found is that there is an effect on the number of matches that you had in the recent past in your lag behavior. In particular, there's a negative effect on the number of matches that you had in your like behavior, which means that if you see the same profile and you haven't had any match in the recent past, you're more likely to like that profile than if you had had a lot of success in the recent past.“ by Daniela Savan
- “So, basically, what we try to do here is to build this notion of equity into the algorithm, meaning that we wanted to give all these volunteering opportunities a more equitable chance of being displayed and therefore attract their volunteers.“ by Daniela Savan
- “If someone raises their hand to serve or volunteer and then are given an opportunity that is not meaningful or not positive, we've just sent a message to that person that we don't actually need them.“ by Josh Friday
- “Typically, in dating apps, you have more men than women. And basically, the absurd behavior is that when men were sending a message, it was much more likely that this message will go unanswered than if a woman sent the message somewhere between four times and ten times more likely that this message will go unanswered, depending on the dating app you were.“ by Daniela Savan
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Episode Information
If/Then: Research findings to help us navigate complex issues in business, leadership, and society
Stanford GSB
6/12/24
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
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