Your Public Policy Officer attended the USACM Panel on Algorithmic Transparency and Accountability on Thursday, Sept 14th at the National Press Club. The panelists were moderator Simson Garfinkel, Jeanna Neefe Matthews, Nicholas Diakopoulos, Dan Rubins, Geoff Cohen, and Ansgar Koene. USACM Chair Stuart Shapiro opened the event, and Ben Sneiderman provided comments from the audience.
USACM and EUACM have identified and codified a set of principles intended to ensure fairness in this evolving policy and technology ecosystem. These were a focus of the panel discussion and are as follows:(1) awareness;
(2) access and redress;
(5) data provenance;
(6) audit-ability; and
(7) validation and testing.
See also the full letter in the September, 2017, issue of CACM.
The panel and audience discussion ranged from frameworks for evaluating algorithms and creating policy for fairness to examples of algorithmic abuse. Language for clear communication with the public and policymakers, as well as even scientists, was a concern — as has been discussed in our Public Policy blog. Algorithms in the strict sense may not always be the issue, but rather the data used to build and train a system, especially when the system is used for prediction and decision making. Much was said about the types of bias and unfairness that can be embedded in modern AI and machine learning systems. The essence of the concerns includes the ability to explain how a system works, the need to develop models of algorithmic transparency, and how policy or an independent clearinghouse might identify fair and problematic algorithmic systems.
Please read more about the panel discussion at https://www.acm.org/public-policy/algorithmic-panel
watch the very informative YouTube video of the panel at https://www.youtube.com/watch?v=DDW-nM8idgg&feature=youtu.be