Data Skeptic

Official twitter account for the Data Skeptic podcast, hosted by @kpolich. Find us on iTunes, Google Music, Stitcher, Pandora, Youtube...

[Data Skeptic @DataSkeptic]

Anas Buhayh breaks down the S'mores framework—a radical approach to algorithmic pluralism where YOU choose which recommender serves your content.Horror fan? Soul-funk enthusiast? There's an algorithm for that.Listen now 🎧 https://t.co/OvJUoZPUAO

[Data Skeptic @DataSkeptic]

Cory Zechmann, shares 16 years of wisdom on the art of "algatorial" curation—where human expertise meets machine learning.Why does TikTok work so well? What's the CODE framework? How do we balance discovery with familiarity? 🎧 https://t.co/r8OgZr1yxa

[Data Skeptic @DataSkeptic]

💼 AI-powered job matching sounds great... but can you trust the recommendations?Roan Schellingerhout discusses explainable recommender systems for recruitment—and why "healthy friction" might actually help users make better decisions.Listen 🎧 https://t.co/FvrZCXOcAk

[Data Skeptic @DataSkeptic]

Václav Blahut from https://t.co/YLTXEedrHH explains "inverse recommendation"—finding the right users for niche content instead of the usual approach.A clever repurposing of two-tower models that gives long-tail content a fighting chance.Dive in 🎧 https://t.co/4g3MkdGX5P

[Data Skeptic @DataSkeptic]

@ek8terina (@MIT) talks strategic learning in rec sys 🎯
The paradox: algorithmic "protest movements" can actually HELP platforms by providing clearer signals
We explore game theory, coordinated user behavior, and the platform vs. user arms race https://t.co/B7JMo1VkC4

[Data Skeptic @DataSkeptic]

@ervindervishaj https://t.co/WhKTsnzgpe

[Data Skeptic @DataSkeptic]

Can recommender systems be both powerful AND interpretable? 🔍
@ervindervishaj (@UniCopenhagen) shares research on disentanglement in RecSys
Key finding: strong correlation between disentanglement & interpretability, but not always with performance

[Data Skeptic @DataSkeptic]

🎵 How can music recommendations be fairer? @Rebeccasalganik, @UofR, presents LARP, a framework tackling popularity and multi-interest bias in playlist continuation. Her Music Semantics dataset captures how ppl describe music—atmosphere, context, vibes. 🎯https://t.co/DrKtNzqu9P

[Data Skeptic @DataSkeptic]

@ek8terina https://t.co/MM46l3S8RN

[Data Skeptic @DataSkeptic]

🧠 What happens when users coordinate to game recommendation algorithms?
@ek8terina reveals her findings: algorithmic "protest movements" can paradoxically benefit platforms by providing clearer preference signals.

[Data Skeptic @DataSkeptic]

Can ML be unfair without bad intent? Yes. 🤔 @dayvidliu (@Cornell) shows how PCA over-specializes on popular content, neglecting niche users. But his item-weighted PCA improves BOTH fairness AND performance! ⚖️ #AlgorithmicFairness #ResponsibleAI https://t.co/Fr4lmdFQLU

[Data Skeptic @DataSkeptic]

What if we tracked eyes, not just clicks? 👁️ Santiago reveals how eye tracking uncovers what users actually see in recommendations. Introducing RecGaze—the 1st eye tracking dataset for rec systems! Changes everything about positional bias. 🎬 #RecSys
https://t.co/Rt69sbBtTV

[Data Skeptic @DataSkeptic]

Study recommendation algorithms without direct data access! Our guests present a "recommender neutral user model" to deduce algorithmic impact when exposure data is missing. This breakthrough aids in understanding complex social media systems. #RecSys 🎯 https://t.co/lZJrIKNPqZ

[Data Skeptic @DataSkeptic]

📊 @alberto_mancino of @sisinflab talks DataRec—a Python library solving dataset management headaches in recommender systems research. Automated downloads, checksum verification, standardized filtering. Game-changer for reproducibility! 🚀 #RecSys #MLOps https://t.co/Pz3cXMFNwr

[Data Skeptic @DataSkeptic]

Can we build greener AI without losing performance? 🌱 @AntonioPuri00 of @SapienzaRoma talks EcoAware Graph Neural Networks—measuring and reducing the enviro impact of recommender systems. Learn to make your ML models more sustainable! 🌍 #GreenAI #RecSys https://t.co/VzorIFKtxP

[Data Skeptic @DataSkeptic]

How do fake profiles game recommendation algorithms? 🎯 @aditya_chichani from Walmart breaks down shilling attacks—from Spotify playlist manipulation to fake product reviews. Essential listening for anyone building rec systems! #MachineLearning #RecSys https://t.co/S83fvCzVOx

[Data Skeptic @DataSkeptic]

🌍 Can AI make tourism more sustainable? @ashmi_banerjee from @TU_Muenchen talks using recommender systems to promote responsible travel. Learn how LLMs make synthetic tourism data & how algorithms can balance traveler satisfaction with enviro impact. 🎯 https://t.co/pIk4iCCRFD

[Data Skeptic @DataSkeptic]

@Vida19231939 https://t.co/v2f03Nn0rv

[Data Skeptic @DataSkeptic]

🚀 The cold start problem is REAL! @Vida19231939 breaks down how hybrid recommender systems combine collaborative filtering + embeddings + bandit learning to make great recommendations for brand new users
🎧️ Listen👇
#MachineLearning #RecommenderSystems #DataScience #AI

[Data Skeptic @DataSkeptic]

🎙️ Check out Kyle's recent interview with @center4inquiry! He dives into AI, skepticism, and how technology intersects with critical thinking—covering everything from recommendation systems to the future of AI development. 🤖🔍

[Center for Inquiry @center4inquiry]

“The Terminator 2 solution”: fight AI with AI.

@DataSkeptic's @kpolich on why the panic cycle misses the point and what to worry about instead: https://t.co/moY3YUVEdv