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Combatting Creepiness in Big Data

Contains material from Jun 2020

Combatting Creepiness in Big Data
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“Creepy” has become the go-to descriptor for data programs that just don’t seem…right. This presentation considers the development of that concept in law and ethics and offers recommendations to better set and manage data use expectations and realities.

Includes: Video Audio Slides

  • Total Credit Hours:
  • 0.75 | 0.50 ethics
  • Credit Info
  • TX, CA
  • TX MCLE credit expires: 6/30/2021

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Credit

1. Combatting Creepiness in Big Data (Jun 2020)

Justin Koplow

0.75 0.50 0.00 0.75 | 0.50 ethics
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(mp4)
47 mins
(mp3)
46 mins
(pdf)
7 pgs
Session 1 — 47 mins, credit 0.75 | 0.50 ethics
Combatting Creepiness in Big Data (Jun 2020)

“Creepy” has become the go-to descriptor for data programs that just don’t seem…right. This presentation considers the development of that concept in law and ethics and offers recommendations to better set and manage data use expectations and realities.

Submit your questions, comments, and/or experiences in advance to ConferenceQA@utcle.org, or come armed with your toughest questions.

Originally presented at: May 2020 Technology Law Conference

Justin Koplow, AT&T - Dallas, TX

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