In this podcast the author explains a paper to me, your host, Professor Sune Lehmann (https://sunelehmann.com). The participants are authors of a paper in network science or data science. Sometimes I feature a group of co-authors! The intended audience is PhD students, PostDocs and other scientists. The idea is to start with a bit about the paper's author, the idea for the paper. Then talk about the research itself. And we’ll end by gossiping about the reviewing process, etc. The whole thing is based on the idea that papers are so formal. And that when two people talk to each other informally, it’s often more fun – and tends to get ideas across more effectively.
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episode 4: Piotr Sapiezynski - Explaining the Filter Bubble!
Today’s guest on the pod is Piotr Sapiezynski!
Piotr (1) is an Associate Research Scientist at Northeastern University in Boston, MA.
My interview with Piotr is part two of my three part series on of algorithms & filter bubbles. And today’s is a great conversation, not to be missed. Piotr really explain the logic and strong evidence that he (& a team of collaborators) has discovered around filter bubbles. I already knew a lot of this, but my mind was still blown.
The core of Piotr’s work is auditing platforms and their algorithms for fairness and privacy.
Together with his collaborators, Piotr investigates systems that are optimized for corporate profit yet drive many aspects of our daily lives. All too often we find these systems have (possibly unintended but often predictable) side effects that bring harm to individuals and the society.
Before diving into algorithm audits he worked on analyses of behavioral data collected from smartphones to model human mobility, spread of diseases, development of relationships, and to predict life outcomes. This experience made him closely aware of and alert to the privacy risks associated with accumulation of personal data.
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References
(1) https://sapiezynski.com