718: How Leaders Can Use the Algorithms for Good, with Sandra Matz

Coaching for Leaders

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17d ago 38:54

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Sandra Matz: Mindmasters

Sandra Matz is a Columbia Business School professor, computational social scientist, and pioneering expert in psychological targeting. Her research uncovers the hidden relationships between our digital lives and our psychology with the goal of helping businesses and individuals make better decisions. She is the author of Mindmasters: The Data-Driven Science of Predicting and Changing Human Behavior*.

Algorithms are becoming more influential with each passing day. That’s why leaders must understand their power and then decide how their organizations engage. In this conversation, Sandra and I discuss where psychological targeting is at, where it’s going, and the opportunity you have to make the world a bit better.

Key Points

Everyone knows everything in a small town (for better or worse). In the same way, psychological targeting can be used for both evil and good.
Psychological targeting already is successful at identifying wealth, personality, income level, and sexual orientation – and keeps improving.
None of this is going away. Understanding how the game of targeting is played can help you make it work to your advantage.
Leaders and organizations who use targeting responsibly can do tremendous good, including helping people save money and flag early interventions for health crises.
Be transparent with what data you’re collecting and how you’re using it. Consider newer practices like federated learning that protect privacy and provide permission-based access.
Design systems and practices that anticipate the reality of future leaders with different values.

Resources Mentioned

Mindmasters: The Data-Driven Science of Predicting and Changing Human Behavior by Sandra Matz

Interview Notes

Download my interview notes in PDF format (free membership required).

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718: How Leaders Can Use the Algorithms for Good, with Sandra Matz

Coaching for Leaders

2,854 subscribers

published 17d ago

MP3•Episode home