Machine Learning 2016

In AI We Trust? Ethics & Bias in Machine Learning

September 16, 2016

The big data era enabled organizations to store more data at a lower cost, and process data more quickly and efficiently. Organizations now seek ways to transform their data into meaningful insights, insights that inspire new products, drive revenue, or enable automation that impacts the bottom line.

Machine learning is at the center of this next wave of innovation and growth. But to use new algorithms effectively, organizations should first understand how they work, the reality vs. the hype, and the unique challenges and opportunities to the private sector and society at large.

Attendees & Speakers

Matt Abrams

Seven Peaks Ventures

Kathryn Hume

Fast Forward Labs

Clare Corthell

Luminant Data

Ray Velez

Razorfish

Angelique Augereau

Independent Consultant

Jon MIller

Cylance

Ron Talwalkar

Cylance

David Archer

Galois

Bill Welser

RAND Corporation

Jeff Larson

ProPublica

Aaron Rieke

Upturn

Katie Amrine

Insight Data Science

Doug Downey

Northwestern University

Diego Klabjan

Northwestern University

Olly Downs

Amplero

Igor Barani

Enlitic

Evan Macmillan

GridSpace

Osonde Ope Osoba

RAND Corporation

Amelia Taylor

Insight Data Science Fellow /

Alice Albrecht

Simple Finance

Adam Carroll

Amplion

Michael Jones

Salesforce

Garrett Tenold

Amplero

Siri Mehus

Blink UX

Jeff Alpen

Blink UX

Tony Falco

Silicon Valley Data Science

Jeff Carr

SlamData

Alex Trisoglio

McKinsey

Yong Bakos

OSU-Cascades

Brian Brennan

Pacific Crest Securities

John Hummel

District Attorney

Mounir Shita

Kimera Systems

Dave Baiocchi

RAND Corporation

Tom Egan

Launch Capital

Preston Callicut

Five Talent Software

Seth Taylor

Amplion

Silvano Gai

Cisco Fellow

Mark Thompson

Cylance

Bruce Cleveland

Wildcat Venture Partners

Dan Conner

Lewis & Clark Ventures

Matt Kern

Bloomcrush

Samih Fadli

Publicis.Sapient / Razorfish

Andrew Duchon

Manzama

Argyro Karanasiou

Bournemouth University

Lior Kogot

Oracle

Agenda

Machine Learning Summit Agenda

Welcome Dinner & Reception

09/15/16

5:30pm – 8:00pm

The 1001 Tech Center, 1001 SW Emkay Drive, Bend, OR 97702

Opening Remarks

09/16/16

9:00am – 9:10am

Matt Abrams, Seven Peaks Ventures

Fairness, Bias, Interpretability: The Ethics of Algorithms

9:10am – 9:35am

Kathryn Hume, Fast Forward Labs

Engineering for Ethics

9:35-10:05

Clare Corthell, Luminant Data

Fairness in Practice: Marketing with Machine Learning

10:05am – 10:45am

Ray Velez, Razorfish | Angelique Augereau, Independent Consultant

Security & Trust in the Age of AI: A Business Perspective

11:00am – 11:45am

Jon Miller & Ron Talwalkar, Cylance | David Burke, Galois

Angelique Augereau, Independent Consultant

Machine Learning and the Future of Jobs and Labor

11:45am – 12:30pm

Bill Welser, RAND Corporation

Lunch

12:30pm – 1:30pm

Designing for Conversational AI and ML Interfaces – A Q&A Session on Lessons from the Field

1:30pm – 2:00pm

Jeff Alpen, Blink UX

Algorithmic Law Enforcement: Social Justice Issues

2:00pm – 2:30pm

Jeff Larson, ProPublica | Aaron Rieke, Upturn | Katie Amrine, Insight Data Science

Fact or Fiction? Cutting through the Hype around AI

3:00pm – 3:30pm

Moises Goldszmidt, Samsung | Scott Penberthy, PwC | Kathryn Hume, Fast Forward Labs

Advancements in Academic Research

3:45pm – 4:15pm

Doug Downey & Diego Klabjan, Northwestern

Realities, Obstacles & Requirements for Enterprise AI Startups

4:15pm – 5:15pm

Discussion and Demos from Amplero, Amplion, Enlitic, Gridspace, and Manzama

Dinner

6:00pm – 9:00pm

Breakfast and Outdoor Adventure

09/17/16

8:00am – 11:00am

Informal breakfast and hike in the beautiful local surroundings

Companies