<< Week of June 02 >>

Wednesday, June 5, 2019

Simons Institute Workshop — Wrong at the Root: Racial Bias and the Tension Between Numbers and Words in Non-Internet Data

Workshop: Theory of Computing: CS: Data Science | June 5 – 7, 2019 every day | 9 a.m.-5 p.m. |  Calvin Laboratory (Simons Institute for the Theory of Computing)

 Simons Institute for the Theory of Computing

Artificially intelligent systems extrapolate from historical training data. While the training process is robust to “noisy” data, systematically biased data will inexorably lead to biased systems. The emerging field of algorithmic fairness seeks interventions to blunt the downstream effects of data bias. Initial work has focused on classification and prediction algorithms.

This cross-cutting...   More >

 Free

  Register online

Thursday, June 6, 2019

Simons Institute Workshop — Wrong at the Root: Racial Bias and the Tension Between Numbers and Words in Non-Internet Data

Workshop: Theory of Computing: CS: Data Science | June 5 – 7, 2019 every day | 9 a.m.-5 p.m. |  Calvin Laboratory (Simons Institute for the Theory of Computing)

 Simons Institute for the Theory of Computing

Artificially intelligent systems extrapolate from historical training data. While the training process is robust to “noisy” data, systematically biased data will inexorably lead to biased systems. The emerging field of algorithmic fairness seeks interventions to blunt the downstream effects of data bias. Initial work has focused on classification and prediction algorithms.

This cross-cutting...   More >

 Free

  Register online

Friday, June 7, 2019

Simons Institute Workshop — Wrong at the Root: Racial Bias and the Tension Between Numbers and Words in Non-Internet Data

Workshop: Theory of Computing: CS: Data Science | June 5 – 7, 2019 every day | 9 a.m.-5 p.m. |  Calvin Laboratory (Simons Institute for the Theory of Computing)

 Simons Institute for the Theory of Computing

Artificially intelligent systems extrapolate from historical training data. While the training process is robust to “noisy” data, systematically biased data will inexorably lead to biased systems. The emerging field of algorithmic fairness seeks interventions to blunt the downstream effects of data bias. Initial work has focused on classification and prediction algorithms.

This cross-cutting...   More >

 Free

  Register online

Simons Institute Theoretically Speaking Series — Algorithms and the Law

Lecture: Theory of Computing: CS | June 7 | 6-7:30 p.m. |  David Brower Center

 2150 Allston Way, Berkeley, CA 94704

 Shafi Goldwasser, Simons Institute for the Theory of Computing; Martha Minow, Harvard University; Elliot Schrage, Facebook; Patricia Williams, Columbia University

 Peter Bartlett, Simons Institute for the Theory of Computing

 Simons Institute for the Theory of Computing

This panel will explore the contemporary promises and challenges of computer algorithms from the perspectives of lawyers, ethicists, philosophers, and computer scientists. What is missing from the current technological debates on the fairness and privacy of algorithmic decision making and their impact on the social fabric? What are promising tools of governance and frames of analysis? What do...   More >