Data Skeptic

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 autho...

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...

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.

Dan Elton  joins us to discuss self-explaining AI. What could be better than an interpretable model? How about a model wich explains itself in a conversational way, engaging in a back and forth with the user. We discuss the paper  Self-explaining AI as an alternative to interpretable AI  which presents a framework for self-explainging AI.

Becca Taylor  joins us to discuss her work studying the impact of plastic bag bans as published in  Bag Leakage: The Effect of Disposable Carryout Bag Regulations on Unregulated Bags  from the Journal of Environmental Economics and Management. How does one measure the impact of these bans? Are they achieving their intended goals? Join us and find out!

We are joined by  Arash Kalatian  to discuss  Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning .

Computer Vision is not Perfect Julia Evans  joins us help answer the question  why do neural networks think a panda is a vulture . Kyle talks to Julia about her hands-on work fooling neural networks. Julia runs  Wizard Zines  which publishes works such as  Your Linux Toolbox . You can find her on Twitter  @b0rk

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

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,...

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

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