Auditing Black Box Models
- Where: FAT*
- When: 2018-02-23, 4-5PM EST, Vanderbilt Hall 204
- Who: Suresh Venkatasubramanian, Carlos Scheidegger, Charlie Marx
Models learned through machine learning can be hard to interpret. A model that takes many inputs and has many parameters might depend on the inputs in complicated ways. This makes it hard to know if, for example,
• the model might indirectly depend on protected attributes of the input that users of the model might prefer it not do (for example race via zipcode).
• the model depends heavily on input attributes that domain knowledge might suggest should not be an important factor (for example, a system that predicts the output of a chemical reaction but appears to be influenced by the time the reaction is run).
This tutorial will teach the audience to use a software library developed by the presenters of the tutorial through a sequence of simple examples in a Jupyter notebook. The presenters will focus on understanding current strengths and limitations of the method, and how tutorial attendees can use this method in their own datasets.
- Get the talk slides, in PDF or PPTX.
- Follow the installation instructions.
- Get the Jupyter Notebook for the hands-on demo.
Suresh, Carlos, and Charlie are the ones presenting the tutorial; but the techniques themselves were designed in collaboration with many other people, and the work is described in these papers:
- Feldman et al., Certifying and Removing Disparate Impact, KDD 2015
- Adler et al., Auditing Black-Box Models for Indirect Influence, Knowledge and Information Systems, 54(1):95–122, 2018.
The source code for the
BlackBoxAuditing library itself is currently hosted on a GitHub repository.