with Prof. Philip B. Stark
Saturday October 21, 2017 at 11:00 AM
100 Genetics & Plant Biology, UC Berkeley
There’s no perfect way to count votes. To paraphrase Ulysses S. Grant and Richard M. Nixon, “Mistakes will be made.” Voters don’t always follow instructions. Voting systems can be mis-programmed. Ballots can be misplaced. Election fraud is not entirely unknown in the U.S. And the more elections depend on technology, the more vulnerable they are to failures, bugs, and hacking–domestic and foreign.
How can we protect elections against honest mistakes and nation states that want to influence our political system? If we vote on paper ballots and keep track of them well, we can double-check election results by inspecting a random sample of ballots. If the results are right, a very small random sample can suffice to confirm the results; if the results are wrong, a full manual count may be required to set the record straight.
“Risk-limiting audits” (RLAs), developed in 2007, guarantee that if the outcome is wrong, there is a large chance that the audit will correct the record before the results are official. They have been tested in California, Colorado, Ohio, and Denmark. Colorado will be the first state to routinely conduct RLAs, starting in November, 2017, and Rhode Island just passed a law requiring RLAs starting in 2018. An immediate national push for RLAs could give the public justified confidence in the 2018 midterm elections and the 2020 presidential election. But time is short.
Philip B. Stark is Associate Dean of the Division of Mathematical and Physical Sciences and Professor of Statistics at University of California, Berkeley.
Stark’s research centers on inference (inverse) problems, especially confidence procedures tailored for specific goals. Applications include the Big Bang, causal inference, the U.S. census, climate modeling, earthquake prediction, election auditing, food web models, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, nonparametrics (confidence sets for function and probability density estimates with constraints), risk assessment, the seismic structure of Sun and Earth, spectroscopy, spectrum estimation, and uncertainty quantification for computational models of complex systems. Numerical optimization is important to his work; he has published some optimization software. He is also interested in nutrition, food equity, and sustainability and is studying whether foraging wild foods could contribute meaningfully to nutrition, especially in “food deserts.”