Data-mining algorithms are increasingly being used to monitor and enforce governmental policies. For example, they are being used to shortlist people for tax auditing by the revenue services in several countries. They are also used by businesses to identify and target potential customers.
Thanks to some high profile cases, there is now increasing concern about how their usage. Should they be restricted? Should they be used more often? Should we be concerned about their emerging omnipresence?
In an earlier set of posts, I looked at the case for transparency in relation to the use of such algorithms. Transparency advocates claim that full or partial disclosure of the methods for collecting and processing our data would be virtuous in any number of ways. For example, there are those who claim that it would promote innovation and efficiency, increase fairness, protect privacy and respect autonomy. I analysed their arguments at some length in that earlier set of posts.
Today, I want to look at the flip-side of the transparency debate. I want to consider arguments for thinking that transparency would actually be a bad thing. I look at two such arguments below. The first argument claims that transparency is bad because it thwarts legitimate government aims; the second claims that transparency is bad because it leads to increased levels of social stigmatisation and prejudice.