Bugbears or Legitimate Threats? (Social) Scientists' Criticisms of Machine Learning

author: Sendhil Mullainathan, Department of Economics, Harvard University
published: Oct. 7, 2014,   recorded: August 2014,   views: 6540


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Social scientists increasingly criticize the use of machine learning techniques to understand human behavior. Criticisms include: (1) They are atheoretical and hence of limited scientific value; (2) They do not address causality and are hence of limited policy value; and (3) They are uninterpretable and hence of limited generalizability value (outside contexts very narrowly similar to the training dataset). These criticisms, I argue, miss the enormous opportunity offered by ML techniques to fundamentally improve the practice of empirical social science. Yet each criticism does contain a grain of truth and overcoming them will require innovations to existing methodologies. Some of these innovations are being developed today and some are yet to be tackled. I will in this talk sketch (1) what these innovations look like or should look like; (2) why they are needed; and (3) the technical challenges they raise. I will illustrate my points using a set of applications that range from financial markets to social policy problems to computational models of basic psychological processes. This talk describes joint work with Jon Kleinberg and individual projects with Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Anuj Shah, Chenhao Tan, Mike Yeomans and Tom Zimmerman.

See Also:

Download slides icon Download slides: kdd2014_mullainathan_machine_learning_01.pdf (1.2┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: