D&S affiliate Sorelle Friedler, with Nicholas Diakopoulos, discuss five principles to hold algorithmic systems accountable. Recent investigations show that risk assessment algorithms can be racially biased, generating scor... Read on MIT Technology Review

D&S founder danah boyd spoke at the Algorithmic Accountability and Transparency in the Digital Economy panel at the EU Parliament. Read on ALDE Group

D&S fellow Anne L. Washington published a Points piece responding to Cathy O'Neil's Weapons of Math Destruction. "Complex models with high stakes require rigorous periodic taste tests. Unfortunately most organizations us... Read on Points

D&S fellow Ravi Shroff examines Cathy O'Neil's analysis of criminal justice algorithms, like predictive policing. "There are a few minor mischaracterizations and omissions in this chapter of Weapons of Math Destruction t... Read on Points

D&S fellow Mark Ackerman develops a checklist to address the sociotechnical issues demonstrated in Cathy O'Neil's Weapons of Math Destruction. "These checklist items for socio-technical design are all important for polic... Read on Points

D&S affiliate Angèle Christin writes a response piece to Cathy O'Neil's Weapons of Math Destruction. "One of the most striking findings of my research so far is that there is often a major gap between what the top admini... Read on Points

D&S researcher Claire Fontaine looks at how school performance data can lead to segregation. In our technocratic society, we are predisposed toward privileging the quantitative. So, we need to find ways to highlight what i... Read on Connected Learning Alliance

"Discriminating Tastes" examines how bias may creep into evaluations of Uber drivers through consumer-sourced rating systems. Read more

D&S researchers Alex Rosenblat and Tim Hwang explore "the significant role of worker motivations and regional political environments on the social and economic outcomes of automation" in this report. Read more

D&S affiliate Surya Mattu, with Julia Angwin, Terry Parris Jr., and Seongtaek Lim, continue the Black Box series. Depending on what data they are trained on, machines can “learn” to be biased. That’s what happened in the f... Read on ProPublica