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Artwork for Data Skeptic

Data Skeptic

Kyle Polich
Recommender Systems
Machine Learning
Large Language Models
Network Science
Artificial Intelligence
Data Science
Animal Behavior
Computer Vision
Network Analysis
Graph Theory
Social Network Analysis
Generative AI
Community Detection
Social Media
Graph Neural Networks
Criminal Networks
Graph Databases
Natural Language Processing
Lemurs
Fraud Detection

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

PublishesTwice monthlyEpisodes607Founded12 years ago
Number of ListenersCategories
TechnologyMathematicsScience

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Artwork for Data Skeptic

Latest Episodes

In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for? Drawing on conversations from across the season, the episode explores engagement, fi... more

Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factori... more

Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems, the limitations of optimizing purely for accuracy... more

News recommendation algorithms influence far more than what stories we click—they can shape our understanding of the world. In this episode, Kyle Polich speaks with Andreea Iana about responsible AI, filter bubbles, multilingual news recommendation, ... more

Key Facts

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Recent Guests

Andrea Ayanna
Researcher on news recommendations and filter bubbles
University of Mannheim
Episode: Recommender Systems Origin Story
Santiago de Leon
PhD student at the Kaplan Institute
Kaplan Institute
Episode: Recommender Systems Origin Story
Asaf Shapiro
Co-host of Data Skeptic Graphs and Networks
Data Skeptic
Episode: Recommender Systems Origin Story
Robin Burke
Professor in the Department of Information Science, University of Colorado Boulder
University of Colorado Boulder
Episode: Social Choice for Fair Recommendations
Andreea Iana
Postdoctoral researcher in the Data and Web Science Group, University of Mannheim
University of Mannheim
Episode: News Recommendations
Fuyuan Lyu
PhD student in recommendation systems, Mila Quebec AI Institute
McGill University; Mila Quebec AI Institute
Episode: Give Users the Wheel
Hieu Le
Academic researcher at FTC, focusing on privacy, automated systems, and applied machine learning; expert on data collection practices and recommendation systems; senior technologist at FTC's Office of Technology
Federal Trade Commission
Episode: AutoLike
Aaron Payne
MBA student at Georgia Tech, senior insights analyst at Chick-fil-A
Georgia Tech / Chick-fil-A
Episode: Student Spotlight: Aaron Payne, Data Analyst
Yashar Deldjoo
Associate Professor at Polytechnic University of Bari; senior research scientist in recommender systems
Polytechnic University of Bari
Episode: The Future is Agentic in Recommender Systems

Hosts

Kerry
Host affiliated with Data Skeptic; appears as a primary host across episodes.
Kyle
Co-host and lead presenter on recommender systems topics.

Reviews

4.6 out of 5 stars from 1k ratings
  • LOVE THE SHOW

    I just absolutely love the show and I’m just wondering if maybe you can cover as a topic sub polynomial compute for graph networks?

    Apple Podcasts
    5
    Cdascientist
    United States7 months ago
  • A gem of a podcast

    I am not a data scientist, but I very much enjoy the podcast! It is fascinating, the interviewer’s really know their field and it has provided me with topics that could be interesting to follow-up. If you are interested in technology, or a student, then this is a podcast that is well worth following and listening to. Top marks!

    Apple Podcasts
    5
    AH24Z
    United Kingdoma year ago
  • great

    great

    Apple Podcasts
    4
    JVo12
    Canada3 years ago
  • Nice podcast

    Listen it to learn industry English and new technology . Really good for me

    Apple Podcasts
    5
    jiazhi chao
    China3 years ago
  • Great resource

    A colleague introduced me to Data Skeptic last year and I’ve been enjoying the episodes. Kyle’s good at covering topics from many levels of data science understanding—his mini series with a non-data scientist are a great way to learn the basics!

    Apple Podcasts
    5
    calzone.onsets
    United States3 years ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Guests are consistently strong and diverse in expertise, offering valuable insights for research and practice.
Listeners praise the depth and breadth of topics, noting accessibility despite technical complexity.
Some listeners critique mic/production quality and co-host dynamics, suggesting more balanced conversations.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#249
United States/Technology
Apple Podcasts
#165
Canada/Technology
Apple Podcasts
#106
Italy/Technology
Apple Podcasts
#168
Germany/Technology
Apple Podcasts
#183
France/Technology
Apple Podcasts
#30
Ireland/Technology

Talking Points

Recent interactions between the hosts and their guests.

Social Choice for Fair Recommendations
Q: Could you talk a little bit about how you chose to implement the aggregation of multiple fairness concerns?
We use a social-choice-inspired framework where multiple fairness agents vote on the final ranking, producing a re-ranked list that balances user interests with several fairness objectives without retraining the main recommender.
Give Users the Wheel
Q: What do LLMs unlock for this opportunity, and how does the UI change with this approach?
LLMs enable a chat-like, natural interaction that bypasses complicated UI designs; users can instruct the system with nuanced prompts, and the model can interpret and apply these instructions during ranking and filtering, reducing the burden of traditional UI/HCI development.
Give Users the Wheel
Q: Can you give listeners a sense of what's under the umbrella of this framework and how it fits into current recommender systems?
The framework starts with offshore embeddings from language models, uses standard sequential recommender models for the base predictions, and then fuses the language-model and user-instruction signals through two hidden representations to guide final recommendations without re-training the entire system.
AutoLike
Q: Who are the intended users of Auto-Like and what are they using it for?
Intended users include regulators (e.g., FTC) to gather evidence of content being promoted by platforms and to test policy implications, as well as platform designers who want to evaluate and improve how their systems behave toward or away from certain content.
Student Spotlight: Aaron Payne, Data Analyst
Q: Can you describe the project with Confama and what problem you were solving?
We worked with Confama to forecast their affiliated population to better plan social services amid pandemic-related disruptions and urbanization in Colombia; the goal was to produce updated forecasts using robust statistical methods and machine learning while ensuring the outputs were interpretable for decision-makers.

Audience Metrics

Listeners, social reach, demographics and more for this podcast.

Listeners per Episode
Gender Skew
Location
Interests
Professions
Age Range
Household Income
Social Media Reach

Frequently Asked Questions About Data Skeptic

What is Data Skeptic about and what kind of topics does it cover?

The show centers on data science, statistics, ML, and AI, with frequent deep dives into practical methods, fairness, interpretability, and the societal implications of algorithmic systems. Episodes mix theory, architecture, and real-world case studies, often featuring researchers and practitioners who discuss how to apply rigorous thinking to complex data problems. A notable strength is the breadth of topics within recommender systems, responsible AI, and governance, rendered through accessible interviews that balance technical depth with practical insights. Listeners can expect thoughtful exploration of cutting-edge methods, evaluation challenges, and multi-stakeholder perspectives, making it valuable for both technical professionals and l... more

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Which podcasts are similar to Data Skeptic?

These podcasts share a similar audience with Data Skeptic:

1. Super Data Science: ML & AI Podcast with Jon Krohn
2. Practical AI
3. DataFramed
4. Machine Learning Street Talk (MLST)
5. Google DeepMind: The Podcast

How many episodes of Data Skeptic are there?

Data Skeptic launched 12 years ago and published 607 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Data Skeptic?

Recent guests on Data Skeptic include:

1. Andrea Ayanna
2. Santiago de Leon
3. Asaf Shapiro
4. Robin Burke
5. Andreea Iana
6. Fuyuan Lyu
7. Hieu Le
8. Aaron Payne

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