Data analysis, interpretation and explanations

There was a recent data-related kerfuffle between the New York Times and the makers of the Tesla electric car. If you haven't read the articles, (and the NYT public editor's post on this has good links), the crude summary is this:

• NYT reporter takes Tesla on long test drive, and reports problems with running out of charge.
• Tesla Motors CEO Elon Musk writes a long takedown of the reporter's review, complete with graphs generated from data the car recorded during the drive. 
• NYT comes back with their own interpretation of data, rebutting Musk's claims.
• Others attempt to reproduce the reporter's experience and fail, but arguably in different weather conditions that might or might not make a difference.

In an insightful meta-analysis of the dustup, Taylor Owen of the Tow Center for Digital Journalism discusses the implications for journalism in a data-driven world. He also references an article by David Brooks that makes the point:

People are really good at telling stories that weave together multiple causes and multiple contexts. Data analysis is pretty bad at narrative and emergent thinking...

I've felt for a while now that it's time to design mechanisms for providing context and narrative to data analysis*. Some of the research my student Parasaran does is on metaclustering: essentially the meta-analysis of different perspectives (clusterings) of data to draw out a larger meaning. We've also just submitted a paper on how to derive local markers of prediction validity in clustering (rather than just relying on global measures of quality). And my seminar this semester is about the larger problems of explanations and accounting in data mining.

I think as computer scientists, we have a lot to offer in the realm of data mining - not just in the design of tools for prediction, but in the design of tools to facilitate better understanding.


* None of this is surprising to experimental scientists, who will routinely attack a problem from multiple directions in order to acquire a larger understanding of a phenomenon rather than just the ability to predict. 

TCS+ hangout on the TSP polytope.

+Thomas Vidick  and the folks at the +TCS+ community have started a new experiment in G+ hangout talks. The first talk in this series was by Ronald de Wolf on the STOC 2012 paper that showed that there was no polynomial-sized lifted representation of the TSP polytope.

Overall, it was a very pleasant experience. I was able to reserve one of the 10 slots (a Hangout limit) for myself and my students at the University of Utah to attend and interact with the speaker, and from Thomas's post-review it seems that many more were signed on for "view-only" access to the stream.

There were very few technical hiccups, and +Oded Regev did a great job making sure that people were muted when not talking, and had a chance to ask questions in an orderly way. The chat sidechannel was a great help.

The talk itself was the best part: de Wolf did an excellent job conveying the main ideas of the proof without getting bogged down in details, and it felt as comfortable as listening to a talk live at a conference. Given the number of people listening in, this was already approaching medium-sized-workshop levels.

I'm looking forward to more of these events, and I'm glad that the +TCS+  folks are doing this. I also hope they can try more experiments with Google Hangout. For example, two ideas come to mind:

• A reddit-style AMA ("Ask Me Anything"). One way to make this work is that the speaker would do a short presentation (maybe 5-10 minutes) and then would open up the floor for questions. To keep things manageable, people could write in questions on chat, and the moderator could filter them and ask the questions live. With sufficient preparation, some questions could be submitted ahead of time.
• A live panel discussion with a moderator and a few participants, which again could have questions from the audience moderated by the moderator.