14–17 Oct 2019

Schedule: Privacy, Ethics, and Compliance sessions

Add to your personal schedule
11:5512:35 Wednesday, 16 October 2019
Location: Westminster Suite
Martin Benson (Jaywing)
Machine learning has been used in credit scoring for three decades. Martin Benson discusses the history of machine learning in credit scoring and the need for explainable and justified decisions made by machine learning systems. Come find out if it's possible to overcome the black box problem and learn how machine learning systems are evolving and how to bypass the challenges to adoption. Read more.
Add to your personal schedule
14:3515:15 Wednesday, 16 October 2019
Location: Westminster Suite
Tobias Martens (whoelse.ai)
Average rating: ***..
(3.50, 2 ratings)
More than 50% of all interactions between humans and machines are expected to be speech-based by 2022. The challenge: Every AI interprets human language slightly different. Tobias Martens details current issues in NLP interoperability and uses Chomsky's theory of universal hard-wired grammar to outline a framework to make the human voice in AI universal, accountable, and computable. Read more.
Add to your personal schedule
11:0511:45 Thursday, 17 October 2019
Location: Windsor Suite
Secondary topics:  Reinforcement Learning
Rebecca Gu (Electron), Cris Lowery (Baringa Partners)
Average rating: ****.
(4.33, 3 ratings)
In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu and Cris Lowery explore how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like. Read more.
Add to your personal schedule
11:5512:35 Thursday, 17 October 2019
Location: Windsor Suite
Katharine Jarmul (KIProtect)
Average rating: *****
(5.00, 2 ratings)
Katharine Jarmul sates your curiosity about how far we've come in implementing privacy within machine learning systems. She dives into recent advances in privacy measurements and explains how this changed the approach of privacy in machine learning. You'll discover new techniques including differentially private data collection, federated learning, and homomorphic techniques. Read more.

Contact us

confreg@oreilly.com

For conference registration information and customer service

partners@oreilly.com

For more information on community discounts and trade opportunities with O’Reilly conferences

aisponsorships@oreilly.com

For information on exhibiting or sponsoring a conference

pr@oreilly.com

For media/analyst press inquires