ACM SIGMETRICS 2018
Irvine, California, USA
June 18-22, 2018
Inherent Trade-Offs in Algorithmic Fairness
When we use data-analytic techniques to classify, we are naturally led to questions about what it means for our classifications to be fair. Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing inherent trade-offs between these conditions. We also consider a variety of methods for promoting fairness and related notions for classification and selection problems that involve sets rather than just individuals.
This talk is based on joint work with Sendhil Mullainathan, Manish Raghavan, and Maithra Raghu.
Jon Kleinberg is the Tisch University Professor in the Departments of Computer Science and Information Science at Cornell University. His research focuses on issues at the interface of algorithms, networks, and information, with an emphasis on the social and information networks that underpin the Web and other on-line media. He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences; and he is the recipient of research fellowships from the MacArthur, Packard, Simons, and Sloan Foundations, as well as awards including the Harvey, Lanchester, and Nevanlinna Prizes, the Newell Award, and the ACM Prize in Computing.
New Metrics and Models for a Post-ISA Era:
Managing complexity and scaling performance in Heterogeneous Parallelism and Internet-of-Things
Pushed by both application and technology trends, today's computer systems employ unprecedented levels of heterogeneity, parallelism, and complexity as they seek to extend performance scaling and support new application domains. From datacenters to Internet-of-Things devices, these scaling gains come at the expense of degraded hardware-software abstraction layers, increased complexity at the hardware-software interface, and increased challenges for software reliability, interoperability, and performance portability. This talk will explore how new metrics, models, and analysis techniques can be effective in this "Post-ISA" era of shifting abstractions. The talk will cover hardware and software design opportunities, methods for formal verification, and a look into the implications on technologies like IoT.
Margaret Martonosi is the Hugh Trumbull Adams '35 Professor of Computer Science at Princeton University, where she has been on the faculty since 1994. She is also Director of Princeton University's Keller Center for Innovation in Engineering Education. Martonosi's research interests are in computer architecture and mobile computing. Her work has included the development of the Wattch power modeling tool and the Princeton ZebraNet mobile sensor network project for the design and real-world deployment of zebra tracking collars in Kenya. Her current research focuses on hardware-software interface approaches to manage heterogeneous parallelism and power-performance tradeoffs in systems ranging from smartphones to chip multiprocessors to large-scale data centers. Martonosi is a Fellow of both IEEE and ACM. Notable awards include the 2018 IEEE Technical Achievement Award, the 2010 Princeton University Graduate Mentoring Award, and the 2013 Anita Borg Institute Technical Leadership Award. Her research has earned four recent Test-of-Time Paper Awards: the 2015 ISCA Long-Term Influential Paper Award, 2017 ACM SIGMOBILE Test-of-Time Award, 2017 ACM SenSys Test-of-Time Paper award, and 2018 (Inaugural) HPCA Test-of-Time Paper award.