Data Skeptic: Interpretability

Uncertainty Representations

Uncertainty Representations Jessica Hullman joins us to share her expertise on data visualization and communication of data in the media. We discuss Jessica's work interviewing and researching visualization designers on techniques for conveying uncertainty. http://users.eecs.northwestern.edu/~jhullman/ ... [more]


AlphaGo, COVID-19 Contact Tracing, and New Data Set

George led a discussion about AlphaGo - The Movie | Full Documentary. Lan informed us about the COVID-19 Open Research Dataset. Kyle shared some thoughts about the paper Beyond R_0: the importance of contact tracing when predicting epidemics. ... [more]


Visualizing Uncertainty

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Interpretability Tooling

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


Shapley Values

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


Anchors as Explanations

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. ... [more]


Mathematical Models of Ecological Systems

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Adversarial Explanations

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. ... [more]


ObjectNet

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/ ... [more]


Visualization and Interpretability

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! ... [more]


Interpretable One Shot Learning

We welcome Su Wang back to Data Skeptic to discuss the paper Distributional modeling on a diet: One-shot word learning from text only. ... [more]


Fooling Computer Vision

Wiebe van Ranst joins us to talk about a project in which specially designed printed images can fool a computer vision system, preventing it from identifying a person.  Their attack targets the popular YOLO2 pre-trained image recognition model, and thus, is likely to be widely applicable. ... [more]


Algorithmic Fairness

This episode includes an interview with Aaron Roth author of The Ethical Algorithm. ... [more]


Interpretability

Interpretability Machine learning has shown a rapid expansion into every sector and industry. With increasing reliance on models and increasing stakes for the decisions of models, questions of how models actually work are becoming increasingly important to ask. Welcome to Data Skeptic Interpretability. In this episode, Kyle interviews Christoph Molnar about his book Interpretable Machine Learning. Thanks to our sponsor, the Gartner Data & Analytics Summit going on in Grapevine, TX on March 23 – 26, 2020. Use discount code: dataskeptic. Music Our new theme song is #5 by Big D and the Kids Table. Incidental music by Tanuki Suit Riot. ... [more]