“Humans, Data and Machines” was the 2018 theme of the annual public lecture series put on by the University of Arizona College of Science. As in past years, the ASA is sponsoring encore presentations of the talks for those who missed the original lectures.

How can we make sure Artificial Intelligence (AI) machines are simply reflecting reality and not actually shaping it? This is a major point in the next encore presentation of “Humans, Data and Machines,” the 2018 public lecture series put on by the University of Arizona College of Science.

Jane Bambauer, a professor in the UA’s James E. Rogers College of Law, will deliver a live reprise of her lecture, “Machine Influencers and Decision Makers,” at 3:30 p.m. Friday (May 25) in the ASA Henry and Phyllis Koffler Great Room.

Earlier lectures in the series dealt with how machines acquire and handle information; how machines think differently; and how to work with AI even when it seems to be displacing us.

Bambauer’s lecture deals with sources, quality, and relevance of information available to the machine and how that affects the choices AI presents to decision makers—be they policy makers, judges, regulators, CEOs, or doctors. Or you, when you use the internet.

She asks whether the data used to create the algorithms that machines deploy, properly represent reality or do they shape reality.  For example, do our credit scores reflect who we are or present a false us?  In other realms they can produce fake news or fake videos, making it hard to tell if they are real or not.

Jane Bambauer

A math and a law degree from Yale University prepare Bambauer to handle big data sets and their applications to privacy, regulation, criminal justice and more. In 2015 she was a Privacy Fellow at the George Mason University Economic and Policy Center and a frequent guest panelist or presenter at GMU’s Antonin Scalia Law School as well as presenting at many other universities.

Written by Brack Brown, Academy Village Volunteer

More Info on attending an event

 

 

 

 

 

May 25: Key to Good Artificial Intelligence? Reliable Data