BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20190310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20191103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20190926T100422Z UID:59137978-D876-4C77-BD9C-C81D093BE050 DTSTART;TZID=America/New_York:20190925T183000 DTEND;TZID=America/New_York:20190925T203000 DESCRIPTION:We are in a new golden age of artificial intelligence research\ , eclipsing the postwar zenith and driven by a fundamentally different con ceptual approach. It's powered by big data\, new storage and processing ca pacities\, and pattern-discrimination systems that learn from those data\, constructing rules for their behavior as they go. This isn’t just in th e lab. These machine learning systems are implemented by all the big tech companies in everything from ad auctions to photo-tagging\, and are supple menting or replacing human decision making in a host of more mundane\, but possibly more consequential\, areas like loans\, bail\, policing\, and hi ring. And we’ve already seen plenty of dangerous failures: Computer visi on systems that don’t recognize black faces or classify them as gorillas \, risk assessment tools systematically rating black arrestees as riskier than white ones\, hiring algorithms that learned to reject women. These is sues force a fundamental reconsideration of core democratic values—not j ust in what decisions are made\, but how they are reached\, and with sort of accountability. This talk will review fundamental issues of fairness an d equity in machine learning systems and demonstrate how they play out in the specific domain of policing. Finally\, we will discuss emergent approa ches for designing\, auditing\, and regulating these systems\, and what we can learn both from other fields that have faced similar conflicts\, and from activists on the ground.\n\nSpeaker(s): Dr Daniel Greene\, \n\nAgenda : \n6:30 PM to 7:00 PM - Refreshments and Networking\n\n7:00 PM - 7:05 PM - Chapter announcements and Speaker Introduction\n\n7:05 PM - Talk\n\nRoom : Large Meeting Room (2nd Floor)\, Bldg: Tenley-Friendship Neighborhood Li brary\, 4450 Wisconsin Ave NW\, Metro Station: Tenleytown-AU\, Washington\ , District of Columbia\, United States LOCATION:Room: Large Meeting Room (2nd Floor)\, Bldg: Tenley-Friendship Nei ghborhood Library\, 4450 Wisconsin Ave NW\, Metro Station: Tenleytown-AU\, Washington\, District of Columbia\, United States ORGANIZER:murtyp@ieee.org SEQUENCE:4 SUMMARY:Garbage In\, Garbage Out: The Predictable and Unpredictable Challen ges of Regulating Machine Learning Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/200590 X-ALT-DESC:Description: <br /><p style="margin: 0px 0px 10.66px\;"><span st yle="font-family: Calibri\;">We are in a new golden age of artificial inte lligence research\, eclipsing the postwar zenith and driven by a fundament ally different conceptual approach. It's powered by big data\, new storage and processing capacities\, and pattern-discrimination systems that learn from those data\, constructing rules for their behavior as they go. This isn&rsquo\;t just in the lab. These machine learning systems are implement ed by all the big tech companies in everything from ad auctions to photo-t agging\, and are supplementing or replacing human decision making in a hos t of more mundane\, but possibly more consequential\, areas like loans\, b ail\, policing\, and hiring. And we&rsquo\;ve already seen plenty of dange rous failures: Computer vision systems that don&rsquo\;t recognize black f aces or classify them as gorillas\, risk assessment tools systematically r ating black arrestees as riskier than white ones\, hiring algorithms that learned to reject women. These issues force a fundamental reconsideration of core democratic values&mdash\;not just in what decisions are made\, but how they are reached\, and with sort of accountability. This talk will re view fundamental issues of fairness and equity in machine learning systems and demonstrate how they play out in the specific domain of policing. Fin ally\, we will discuss emergent approaches for designing\, auditing\, and regulating these systems\, and what we can learn both from other fields th at have faced similar conflicts\, and from activists on the ground.<span s tyle="margin: 0px\;">&nbsp\; </span></span></p><br /><br />Agenda: <br />< p>6:30 PM to 7:00 PM - Refreshments and Networking</p>\n<p>7:00 PM - 7:05 PM - Chapter announcements and Speaker Introduction</p>\n<p>7:05 PM - Talk </p> END:VEVENT END:VCALENDAR