How Algorithms form Journalism — my year at Nieman, Harvard and MIT
Currently I’m spending my days biking between Harvard and MIT, listening to professors, talking to other fellows and learning about algorithms, journalism and the US way to deal with tech innovation. I was incredibly lucky to get accepted as Nieman Fellow and I am part of an international group of journalists spending a year in Cambridge, MA — basically to learn from each other and to use all those huge resources at Harvard and MIT. As Nieman Fellow you apply with a field of interest and I plan to dedicate my year to algorithmic accountability, machine bias and automation in journalism. I want to get a better understanding how algorithms form the journalism of the future: stories and products. And of course to look left and right for anything else I had no idea that might contribute to what I want to know.
For that experience I temporarily left BR Data, my team at German Public Broadcasting (BR), dedicated to investigative data journalism and interactive storytelling. In the last years we explored with our interdisciplinary team algorithmic research methods: We did a large scale investigation of the German housing rental market together with our colleagues from Spiegel Online, we used machine learning to analyze party political policy shifting during the federal election period and we started to dive into algorithmic accountability reporting with an investigation of the German credit scoring system.
The Nieman program has started a few weeks ago and I can already tell that Boston and Cambridge are great places for this kind of journey. You meet people with the same interests basically everywhere: of course in class and on campus, but you can also talk about decision trees and algorithmic research methods during grocery shopping, in the gym or even on the playground. The Cambridge/Boston area is a melting pot for people curious for digital innovation, future business models and algorithmic responsibility.
What I love most about Harvard and MIT so far is that all disciplines and people with completely different backgrounds tackle the same problems. Core of these interdisciplinary encounters are epicenters like MIT Media Lab or Berkman Klein Center for Internet and Society which prove to become the most interesting places for me to hang out. I’m part of a working group dedicated to Artificial Intelligence together with hackers, computer scientists, open data activists and lawyers discussing recent developments in AI and the implications for society. The Berkman Klein crowd has coined a term condensing this way to collaborate: antidisciplinary :-)
At Harvard Kennedy School I’m looking more into statistics and machine learning from a practical point of view, at MIT Business School I’m learning about product readiness and business models of innovation products. And the Nieman program keeps us busy with workshops about recent developments and leadership in journalism, business models and underreported issues.
Here I want to share what strikes me most during my time here in Harvard and at MIT. Be prepared for irregularly published raw ideas and unfinished thoughts — I’m looking forward to discuss them. And in the meantime I’m exploring new heights and streams:
