Data Skeptic

Data Skeptic

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.

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Episoder(605)

Human Computer Interaction and Online Privacy

Human Computer Interaction and Online Privacy

Moses Namara from the HATLab joins us to discuss his research into the interaction between privacy and human-computer interaction.

27 Jul 202032min

Authorship Attribution of Lennon McCartney Songs

Authorship Attribution of Lennon McCartney Songs

Mark Glickman joins us to discuss the paper Data in the Life: Authorship Attribution in Lennon-McCartney Songs.

20 Jul 202033min

GANs Can Be Interpretable

GANs Can Be Interpretable

Erik Härkönen joins us to discuss the paper GANSpace: Discovering Interpretable GAN Controls. During the interview, Kyle makes reference to this amazing interpretable GAN controls video and it's accom...

11 Jul 202026min

Sentiment Preserving Fake Reviews

Sentiment Preserving Fake Reviews

David Ifeoluwa Adelani joins us to discuss Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection.

6 Jul 202028min

Interpretability Practitioners

Interpretability Practitioners

Sungsoo Ray Hong joins us to discuss the paper Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs.

26 Jun 202032min

Facial Recognition Auditing

Facial Recognition Auditing

Deb Raji joins us to discuss her recent publication Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing.

19 Jun 202047min

Robust Fit to Nature

Robust Fit to Nature

Uri Hasson joins us this week to discuss the paper Robust-fit to Nature: An Evolutionary Perspective on Biological (and Artificial) Neural Networks.

12 Jun 202038min

Black Boxes Are Not Required

Black Boxes Are Not Required

Deep neural networks are undeniably effective. They rely on such a high number of parameters, that they are appropriately described as "black boxes". While black boxes lack desirably properties like i...

5 Jun 202032min

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