Data Skeptic

Latest Episodes:

Uncertainty Representations Apr 04, 2020

Show notes

Jessica Hullman joins us to share her expertise on data visualization and communication of data in the media. We discuss Jessica's work on visualizing uncertainty, interviewing visualization designers on why they don't visualize uncertainty, and modeling interactions with visualizations as Bayesian updates.

Homepage: http://users.eecs.northwestern.edu/~jhullman/

Lab: MU Collective

AlphaGo, COVID-19 Contact Tracing and New Data Set Mar 28, 2020

Show notes

Announcing Journal Club

I am pleased to announce Data Skeptic is launching a new spin-off show called "Journal Club" with similar themes but a very different format to the Data Skeptic everyone is used to.

In Journal Club, we will have a regular panel and occasional guest panelists to discuss interesting news items and one featured journal article every week in a roundtable discussion. Each week, I'll be joined by Lan Guo and George Kemp for a discussion of interesting data science related news articles and a featured journal or pre-print article.

We hope that this podcast will give listeners an introduction to the works we cover and how people discuss these works. Our topics will often coincide with the original Data Skeptic podcast's current Interpretability theme, but we have few rules right now or what we pick. We enjoy discussing these items with each other and we hope you will do.

In the coming weeks, we will start opening up the guest chair more often to bring new voices to our discussion. After that we'll be looking for ways we can engage with our audience.

Keep reading and thanks for listening!

Kyle

Visualizing Uncertainty Mar 20, 2020

Show notes

Interpretability Tooling Mar 13, 2020

Show notes

Pramit Choudhary joins us to talk about the methodologies and tools used to assist with model interpretability.

Shapley Values Mar 06, 2020

Show notes

Kyle and Linhda discuss how Shapley Values might be a good tool for determining what makes the cut for a home renovation.

Anchors as Explanations Feb 28, 2020

Show notes

We welcome back Marco Tulio Ribeiro to discuss research he has done since our original discussion on LIME.

In particular, we ask the question Are Red Roses Red? and discuss how Anchors provide high precision model-agnostic explanations.

Please take our listener survey.

Mathematical Models of Ecological Systems Feb 22, 2020

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Adversarial Explanations Feb 14, 2020

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Walt Woods joins us to discuss his paper Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness with co-authors Jack Chen and Christof Teuscher.

ObjectNet Feb 07, 2020

Show notes

Andrei Barbu joins us to discuss ObjectNet - a new kind of vision dataset.

In contrast to ImageNet, ObjectNet seeks to provide images that are more representative of the types of images an autonomous machine is likely to encounter in the real world. Collecting a dataset in this way required careful use of Mechanical Turk to get Turkers to provide a corpus of images that removes some of the bias found in ImageNet.

http://0xab.com/

Visualization and Interpretability Jan 31, 2020

Show notes

Enrico Bertini joins us to discuss how data visualization can be used to help make machine learning more interpretable and explainable.

Find out more about Enrico at http://enrico.bertini.io/.

More from Enrico with co-host Moritz Stefaner on the Data Stories podcast!