Algorithmic decision-making systems (ADMs) now influence many facets of our lives. Whether it be in finance, employment, welfare management, romance, or dynamic pricing, these systems are practically ubiquitous throughout the public and private sectors.
Operating at scale ADMs can impact large swaths of people—for better or worse. Algorithmic accountability reporting has emerged as a response, an attempt to uncover the power wielded by ADMs and detail their biases, mistakes, or misuse. Algorithmic accountability entails understanding how and when people exercise power within and through an algorithmic system, and on whose behalf.
Algorithm Tips hopes to stimulate algorithmic accountability reporting and support a robust reporting beat on algorithms, in particular by making it easier to investigate algorithms used in government decision making.
To do this we curate a database of ADMs in the US federal government which are of potential interest for investigation (and hope to expand to local and international jurisdictions in the future). On our home page you can search the database for interesting algorithms using keywords relating to facets such as agency (e.g., Dept. of Justice) or topic (e.g., health, police, etc.). Next, on our resources page, you can learn how to submit public records requests about algorithms, or find news articles and research papers about the uses and risks of algorithms. We hope you can get some inspiration there. And finally, we actually dig into some of these leads ourselves and post write-ups to our blog. We hope that journalists and other stakeholders can build on these posts and develop even deeper investigations.
Algorithm Tips is a project of the Northwestern University Computational Journalism Lab. If you have any questions, comments, or concerns (or want to talk about how to help us expand the effort), get in touch: email@example.com. Thanks!