Algorithmic Accountability & Transparency

Software and algorithms have come to adjudicate an ever broader swath of our lives, including everything from search engine personalization and advertising systems, to teacher evaluation, banking and finance, political campaigns, and police surveillance. But these algorithms can make mistakes. They have biases. Yet they sit in opaque black boxes, their inner workings, their inner “thoughts” hidden behind layers of complexity. We need to get inside that black box, to understand how they may be exerting power on us, and to understand where they might be making unjust mistakes. This research tackles this issue and proposes a practical method based on reverse engineering and auditing that journalists can employ in the investigation of algorithms.

More recently we have built a database and web interface of potentially newsworthy algorithms used by the US federal government. The goal is to lower the bar and make it easier for journalists or other actors in civil society to get started with algorithmic accountability reporting. Check out the site here:

Related Articles

  • N. Diakopoulos. Automating the News: How Algorithms are Rewriting the Media. Harvard University Press. June, 2019. [Link]
  • D. Trielli and N. Diakopoulos. Search as News Curator: The Role of Google in Shaping Attention to News InformationProc. Conference on Human Factors in Computing Systems (CHI). May, 2019. [PDF]
  • N. Diakopoulos. The Algorithms BeatData Journalism Handbook. Eds. Liliana Bornegru and Jonathan Gray. 2018 [Link]
  • M. Koliska and N. Diakopoulos. Disclose, Decode and Demystify: An Empirical Guide to Algorithmic TransparencyThe Routledge Handbook of Developments in Digital Journalism Studies. Eds. Scott Eldridge II and Bob Franklin. October, 2018.
  • N. Diakopoulos, D. Trielli, J. Stark, S. Mussenden. I vote for – How search informs our choice of candidate. In: Digital Dominance: The Power of Google, Amazon, Facebook, and Apple. Eds. M. Moore and D. Tambini. June, 2018.
  • D. Trielli, J. Stark and N. Diakopoulos. Algorithm Tips: A Resource for Algorithmic Accountability in Government. Computation + Journalism Symposium. October, 2017. [PDF][Link]
  • N. Diakopoulos and M. Koliska. Algorithmic Transparency in the News Media. Digital Journalism. 2016. [PDF]
  • D. Trielli, S. Mussenden, J. Stark, N. Diakopoulos. Googling Politics: How the Google issue guide on candidates is biased. Slate. June, 2016. Link
  • J. Stark and N. Diakopoulos. Uber seems to offer better service in areas with more white people. That raises some tough questions. Washington Post. March, 2016. Link
  • N. Diakopoulos. Accountability in Algorithmic Decision Making. Communications of the ACM (CACM). Feb. 2016. [PDF]
  • D. Trielli, S. Mussenden, N. Diakopoulos. Why Google Search Results Favor Democrats. Slate. Dec., 2015. Link
  • N. Diakopoulos. How Uber Surge Pricing Really Works. Washington Post. April, 2015. Link
  • N. Diakopoulos. Bots on the Beat. Slate. 2014. Link
  • N. Diakopoulos. Algorithmic Accountability: Journalistic Investigation of Computational Power Structures. Digital Journalism. 2015. [PDF]
  • N. Diakopoulos. Algorithmic Accountability Reporting: On the Investigation of Black Boxes. Tow Center. February 2014. [PDF]
  • N. Diakopoulos. Rage Against the Algorithms. The Atlantic. October 2013. Link
  • N. Diakopoulos. Sex, Violence, and Autocomplete Algorithms. Slate. August 2013. Link
  • N. Diakopoulos. Algorithmic Defamation: The Case of the Shameless Autocomplete. Tow Center. August 2013. Link