This article is the first in our three-part series focused on data privacy considerations related to the use of Artificial Intelligence (AI) and machine learning. This first article.
There’s a Spider-Man story where Peter Parker signs up to work for one Tony Stark, CEO of the global tech giant, Stark Industries. As a new company man, he gets
solved. so it can be summarized in what i ve called the transparency paradox which means essentially that the platforms are being simultaneously pressured to be open and transparent about what s going on, how do your news feed algorithms work, how do the trending algorithms work, who gets to see what and why. and at the same time they re facing extreme pressure to be secure and private with data. and so these two things are in conflict. there is an industry academic partnership called social science one started by researchers at harvard and stanford that is attempting to get facebook data in order to study things like election manipulation and political communication, and so on. craig silverman at buzz feed reported this week that two articles, one that the data has been delayed, the data that s been promised by facebook has not been revealed by facebook. and, two, that the funders of the initiative are threatening to pull out by the end of september if that data is not released to the
promised. facebook has responded and their response is essentially that we re having difficulty anonymizing and securing the private data of individuals to do this in a legitimate way. i think that that is a true and real response. i do think that they may be delaying a little bit. but i do think that it s not easy to thread the needle of the transparency paradox. they have to be open and transparent and secure at the same time. they have to use tools like differential privacy to release anonymous data sets. that s what they re working on. you just mentioned privacy. i want to bring it up in a different context because you wrote this as well. although privacy legislation may prohibit the retention of consumer data, such data may also be critical to understanding how to harden our democracy. we must manage these trade-offs and overcome multidisciplinary method logical challenges simultaneously. what do you mean by that? what i mean is that we are attempting to think about how to