Earlier this month I wrote about the “Rule of Suspicion Algorithms” . Using computer expert systems in order to predict who is more or less likely to become a criminal or a political dissident is not so different from predicting peoples’ policy positions. Michael Laver, an authority on computer-aided quantitative content analysis in political science from New York University, is enthusiastic about the prospects that the large new data troves generated by users themselves hold for political science data analysis:
There is no reason, for example, why we should not set out to measure the policy positions of every single person who uses social media and, with appropriate modeling, to make inferences from these positions about people who do not use social media.
While this indeed is exciting, from a normative perspective concerned with the quality of democracy I’d like to add that it does matter whether such information is generated by academics in order to inform the academic debate and the wider public or if this information will only inform the few, such as security services and corporations. If information about the many is accessible to the many — in aggregated form — societies may reach a higher degree of self-understanding. This would be on the basis of symmetric information distribution. An asymmetric information distribution, on the other hand, would diminish the quality of democracy by granting a limited set of the population privileged access to information which offers them possibilities for manipulating opinion and perception — from the macro to the micro-scale.
How such kinds of information are used will likely become a defining feature of politics over the years to come.
The decision-making criteria of computer expert systems are often so complex that they are beyond the comprehension of their individual users and creators. For example, computer systems equipped with artificial intelligence can be used for estimating the degree to which somebody is likely to default on her credit. These days, computer programs can also be used for the large-scale monitoring of populations and for attempts at predicting who is more or less likely to become a criminal or a political dissident.
When computers start to decide who is likely to be a threat and who isn’t and neither secret services, law enforcement nor the subjects of surveillance understand how a threat assessment comes about, the shared understanding of what constitutes suspicious behaviour gets lost. Writing in the Intercept Dan Froomkin cites Phillip Rogaway, a professor of computer science at the University of California, Davis:
If the algorithms NSA computers use to identify threats are too complex for humans to understand, Rogaway wrote, “it will be impossible to understand the contours of the surveillance apparatus by which one is judged. All that people will be able to do is to try your best to behave just like everyone else.
If people don’t understand the criteria by which they are judged anymore, one can still find it reasonable to use such computer systems. Yet, their “suspicion algorithms” themselves don’t express human reasoning anymore. People become subject to a governance by statistical probabilities instead of human value choices . The computers may not rule as they don’t possess true agency yet. Still, humans delegate their assessment of who is an insider and who an outsider, of who is a friend and a potential foe to systems whose calculations are beyond their comprehension. Reasoning about an essentially political decision is transferred to machines .
The data is there, the algorithms set in place. An ethics of the data age has yet to emerge.
It probably doesn’t come as news but I just got curious why this little program called gkoverride wants to call someone through my firewall when I try to install a patch for SPSS and I found a pretty good explanation on Zdziarski’s Blog of Things saying that Apple basically checks new program installations for security purposes. However, without asking they also keep track of the programs users install — in the name of security. Most users probably care more about security than about privacy but I think it should be made more clear and transparent and it would be important to also provide alternative security mechanisms — as suggested by Zdziarski.
What moneyed interests support a politician? It would clearly enhance the politics section of any newspaper if that type of contextual information could be presented as an accompaniment to news articles that feature the words and voting behaviour of elected representatives. That’s probably exactly what a teenager in the USA was thinking when he developed a browser plug-in that “when you mouse-over the name of a US lawmaker, will serve up a list of which parties have donated to their campaign funds, and the quantities”.
One could think of many interesting extensions or alternative applications: For example, one could adapt it to other polities by drawing on datasets from other countries or it would be possible to switch the perspective from lawmakers to firms and represent information on firms’ lobbying history via mouse-overs.
In their book “Full Disclosure. The Perils and Promise of Transparency”, Fung, Graham and Weil (2007) call this type of emergent transparency “collaborative transparency”. In the age of big data, ubiquitous information technology and smart kids, this is going to stay exciting for a long time to come.