Ensuring that Current and Future Intelligent Machines are Well-Behaved
Present: You can read about ongoing projects extending and leveraging the Seldonian framework here.
February 2020: Prof. Philip Thomas testified to the US House Committee on Financial Services, Task Force on Artificial Intelligence, in a hearing titlded "Equitable Algorithms: Axamining Ways to Reduce AI Bias in Financial Services." Details can be found here, and video of the hearing is provided below.
January 2020: According to Almetric, the Science paper is now in the top 0.1% of all publications tracked by Almetric. Media coverage includes Wired, LA Times, and The Economist, as well as international coverage from Spain (SINC), Russia (Popmech), and China (Sohu) among others. A partial list of coverage can be found here.
December 2019: We published a follow-up paper to the Science paper at the top conference NeurIPS, presenting a Seldonian algorithm for solving problems called contextual bandits, with example applications to loan approval and predicting criminal recidivism, as well as a user study showing how adaptive online courses powered by Seldonian algorithms can ensure that they do not disciminate against minorities in the classroom. [link]
November 2019: Our paper introducing Seldonian algorithms was published in Science [link]. [Press releases 1, 2]