The business of big data audits: monetizing fairness
I gave a talk to the invitation-only NYC CTO Club a couple of weeks ago about my fears about big data modeling, namely:
- that big data modeling is discriminatory,
- that big data modeling increases inequality, and
- that big data modeling threatens democracy.
I had three things on my “to do” list for the audience of senior technologists, namely:
- test internal, proprietary models for discrimination,
- help regulators like the CFPB develop reasonable audits, and
- get behind certain models being transparent and publicly accessible, including credit scoring, teacher evaluations, and political messaging models.
Given the provocative nature of my talk, I was pleasantly surprised by the positive reception I was given. Those guys were great – interactive, talkative, and very thoughtful. I think it helped that I wasn’t trying to sell them something.
Even so, I shouldn’t have been surprised when one of them followed up with me to talk about a possible business model for “fairness audits.” The idea is that, what with the recent bad press about discrimination in big data modeling (some of the audience had actually worked with the Podesta team), there will likely be a business advantage to being able to claim that your models are fair. So someone should develop those tests that companies can take. Quick, someone, monetize fairness!
One reason I think this might actually work – and more importantly, be useful – is that I focused on “effects-based” discrimination, which is to say testing a model by treating it like a black box and seeing how it works on different inputs and gives different outputs. In other words, I want to give a resume-sorting algorithm different resumes with similar qualifications but different races. An algorithmically induced randomized experiment, if you will.
From the business perspective, a test that allows a model to remain a black box feels safe, because it does not require true transparency, and allows the “secret sauce” to remain secret.
One thing, though. I don’t think it makes too much sense to have a proprietary model for fairness auditing. In fact the way I was imagining this was to develop an open-source audit model that the CFPB could use. What I don’t want, and which would be worse than nothing, would be if some private company developed a proprietary “fairness audit” model that we cannot trust and would claim to solve the very real problems listed above.
Update: something like this is already happening for privacy compliance in the big data world (hat tip David Austin).