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PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Pacific/Honolulu
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DTSTART:19470608T023000
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BEGIN:VEVENT
DTSTAMP:20240308T003652Z
UID:314571AB-D2F8-4125-8687-BA2DD64D3718
DTSTART;TZID=Pacific/Honolulu:20230711T173000
DTEND;TZID=Pacific/Honolulu:20230711T193000
DESCRIPTION:Our speaker\, Dr. Kush Varshney\, is a 2023 Signal Processing S
 ociety Distinguished Lecturer. He conducts academic research on the theory
  and methods of trustworthy machine learning. Dr. Varshney will discuss th
 e concepts for developing accurate\, fair\, robust\, explainable\, transpa
 rent\, inclusive\, empowering\, and beneficial machine learning systems.\n
 \nThis will be a hybrid meeting with a remote lecturer and a live Q&A sess
 ion. Come early to meet the other attendees and enjoy some refreshments be
 fore the meeting.\n\nPlease register for this meeting so that we many mana
 ge our space and refreshments. If you plan to attend remotely\, use meet.g
 oogle.com/dqy-hibn-hmj for remote attendance.\n\nCo-sponsored by: Hewaii T
 echnology Development Corporation (HTDC)\, Hub Coworking\, & Entrepreneurs
  Sandbox\n\nSpeaker(s): Dr. Kush Varshney\, \n\nAgenda: \nWe will discuss 
 the concepts for developing accurate\, fair\, robust\, explainable\, trans
 parent\, inclusive\, empowering\, and beneficial machine learning systems.
  Accuracy is not enough when you’re developing machine learning systems 
 for consequential application domains. You also need to make sure that you
 r models are fair\, have not been tampered with\, will not fall apart in d
 ifferent conditions\, and can be understood by people. Your design and dev
 elopment process has to be transparent and inclusive. You don’t want the
  systems you create to be harmful but to help people flourish in ways they
  consent to. ChatGPT and other large language models have introduced new c
 lear and present risks\, including hallucination and toxicity. All of thes
 e considerations beyond accuracy that make machine learning safe\, respons
 ible\, and worthy of our trust are essential and imminent challenges to ov
 ercome.\n\nRoom: Purple Box\, Bldg: Entrepreneurs Sandbox\, 643 Ilalo St\,
  Honolulu\, Hawaii\, United States\, 96813\, Virtual: https://events.vtool
 s.ieee.org/m/363148
LOCATION:Room: Purple Box\, Bldg: Entrepreneurs Sandbox\, 643 Ilalo St\, Ho
 nolulu\, Hawaii\, United States\, 96813\, Virtual: https://events.vtools.i
 eee.org/m/363148
ORGANIZER:eugene.chang@ieee.org
SEQUENCE:22
SUMMARY:Trustworthy Machine Learning
URL;VALUE=URI:https://events.vtools.ieee.org/m/363148
X-ALT-DESC:Description: <br /><p class="xmsonormal"><span style="font-size:
  14pt\;">Our speaker\, Dr. Kush Varshney\, is a 2023 Signal Processing Soc
 iety Distinguished Lecturer. He conducts academic research on the theory a
 nd methods of trustworthy machine learning. Dr. Varshney will discuss the 
 concepts for developing accurate\, fair\, robust\, explainable\, transpare
 nt\, inclusive\, empowering\, and beneficial machine learning systems.</sp
 an></p>\n<p class="xmsonormal"><span style="font-size: 14pt\;">This will b
 e a hybrid meeting with a remote lecturer and a live Q&amp\;A session. Com
 e early to meet the other attendees and enjoy some refreshments before the
  meeting.</span></p>\n<p class="xmsonormal"><span style="font-size: 14pt\;
 ">Please register for this meeting so that we many manage our space and re
 freshments. If you plan to attend remotely\, use meet.google.com/dqy-hibn-
 hmj for remote attendance.</span></p>\n<p class="xmsonormal">&nbsp\;</p>\n
 <p class="xmsonormal">&nbsp\;</p><br /><br />Agenda: <br /><p class="xmson
 ormal"><span style="font-size: 14pt\;">We will discuss the concepts for de
 veloping accurate\, fair\, robust\, explainable\, transparent\, inclusive\
 , empowering\, and beneficial machine learning systems. Accuracy is not en
 ough when you&rsquo\;re developing machine learning systems for consequent
 ial application domains. You also need to make sure that your models are f
 air\, have not been tampered with\, will not fall apart in different condi
 tions\, and can be understood by people. Your design and development proce
 ss has to be transparent and inclusive. You don&rsquo\;t want the systems 
 you create to be harmful but to help people flourish in ways they consent 
 to. ChatGPT and other large language models have introduced new clear and 
 present risks\, including hallucination and toxicity. &nbsp\;All of these 
 considerations beyond accuracy that make machine learning safe\, responsib
 le\, and worthy of our trust are essential and imminent challenges to over
 come.</span></p>
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