Well, we spent almost two billion dollars on political digital ads last year around the world, and that’s a low number compared to what we’ll spend next year. We don’t need to walk through the dozens of articles published every month on the implications of this, except to say that people who think about the social effects of technology are ever-concerned about big data. Systems are so prone to abuse that some progressive governments are regulating them, Spain being the latest example, with its call “for a Data Protection Officer (DPO), a Data Protection Impact Assessment (DPIA) and security measures for processing high risk data” in elections, and its insistence that “for personal data to be used in election campaigning it must have been ‘freely expressed’ – not just with free will but in the strictest sense of an exercise of the fundamental rights to free expression and freedom of political opinion protected by Articles 16 and 20 of the Spanish Constitution.”
So on the bright side, here are four potentially helpful ways we can engage with big data responsibly, reciprocally, and in the public interest.
1. Tracking Local Political Participation.
“In 2018, three BU political scientists used big data to study local political participation in housing and development policy.” They coded “thousands of instances of people who chose to speak about housing development at planning and zoning board meetings in 97 cities and towns in eastern Massachusetts, then matched the participants with voter and property tax data.” Their findings that the conversations tended to be dominated by older white male homeowners instead of being representative of residents in general can help inform activists of the barriers to participation in policy discussions that exist now. “[T]he dynamic,” the researchers conclude, “contributes to the failure of towns to produce a sufficient housing supply. If local politicians hear predominantly from people opposed to a certain issue, it’s logical that they may be persuaded to vote against it, based on what they think their community wants.”
2. Big Data as Ethnographic Tool
This seems counterintuitive because people think big data contributes to the abstraction of political views and lifestyle preferences, but some researchers are concluding something in the other direction, arguing that “[Big data] can be used as a powerful data-recording tool because it stores (…) actual behaviour expressed in a natural environment,” a practice “we normally associate with the naturalistic ideals of ethnography: ‘Studying people in everyday circumstances by ordinary means’.” We aren’t just learning numbers; we’re seeing how people behave in everyday life.
I’m including this because it teaches data readers about themselves. The conversation stems from recent attempts at self-correction by Facebook, Google, and other big companies. Web tester Elena Yakimova spoke to former head of Facebook elections integrity operations Yael Eisenstat, who touts “cognitive bias training [as] the key along with time, better Data Science and bigger, cleaner input data” as ways that those who read that data–and ask the questions–can check their own biases while searching for wider and deeper variables to circumvent their own cognitive (and therefore social) biases.
This is the coolest of all the ideas– it’s a way to engage big data to teach people about people. In Guatemala and Costa Rica, public officials are creating events like open and participatory election surveys where people can not only participate in the questionnaires, but also examine the results; or participate in examining data for participatory budgeting and other municipal functions. Thus, the “For Whom I Vote?” virtual platform has “users fill a questionnaire that measures their preference with parties participating in the electoral process. This allows each user to identify firstly their own ideological position, but also how closely they are with each political party.” It’s all transparent, and participants learn about the process as they participate.
That commitment to openness is a good way to wrap the post. As data practices evolve, there are opportunities for “dissemination of knowledge in free, open and more inclusive ways.”