Data Skeptic

Ivan Oransky joins us to discuss his work documenting the scientific peer-review process at retractionwatch.com.  

Derek Lim joins us to discuss the paper Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform.  

Neil Johnson joins us to discuss the paper The online competition between pro- and anti-vaccination views.

Mashbat Suzuki joins us to discuss the paper How Many Freemasons Are There? The Consensus Voting Mechanism in Metric Spaces. Check out Mashbat’s and many other great talks at the 13th Symposium on Algorithmic Game Theory (SAGT 2020)

Steven Heilman joins us to discuss his paper Designing Stable Elections. For a general interest article, see: https://theconversation.com/the-electoral-college-is-surprisingly-vulnerable-to-popular-vote-changes-141104 Steven Heilman receives funding from the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

Sami Yousif joins us to discuss the paper The Illusion of Consensus: A Failure to Distinguish Between True and False Consensus. This work empirically explores how individuals evaluate consensus under different experimental conditions reviewing online news articles. More from Sami at samiyousif.org Link to survey mentioned by Daniel Kerrigan: https://forms.gle/TCdGem3WTUYEP31B8

In this solo episode, Kyle overviews the field of fraud detection with eCommerce as a use case.  He discusses some of the techniques and system architectures used by companies to fight fraud with a focus on why these things need to be approached from a real-time perspective.

In this episode, Kyle and Linhda review the results of our recent survey. Hear all about the demographic details and how we interpret these results.

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

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

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 accompanying codebase found here. Erik mentions the GANspace collab notebook which is a rapid way to try these ideas out for yourself.

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

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

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

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

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 interpretability and explainability, in some cases, their accuracy makes them incredibly useful. But does achiving “usefulness” require a black box? Can we be sure an equally valid but simpler solution does not exist? Cynthia Rudin helps us answer that question. We discuss her recent paper with co-author Joanna Radin titled (spoiler warning)… Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition

Daniel Kang joins us to discuss the paper Testing Robustness Against Unforeseen Adversaries.

Frank Mollica joins us to discuss the paper Humans store about 1.5 megabytes of information during language acquisition

Jayaraman Thiagarajan joins us to discuss the recent paper Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models.

What does it mean to understand a neural network? That’s the question posted on this arXiv paper. Kyle speaks with Tim Lillicrap about this and several other big questions.