Sep 23–26, 2019

Schedule: Ethics sessions

Ethics and compliance are areas of interest to many in the data community. Beyond privacy, data professionals are much more engaged in topics such as fairness, accountability, transparency, and explainability in machine learning. Are data sets that are being used for model training representative of the broader population? For certain application domains and settings, transparency and interpretability are essential and regulators may require more transparent models, even at the expense of power and accuracy. More generally, how do companies mitigate risk when using ML?

11:20am–12:00pm Wednesday, September 25, 2019

Location: 3B - Expo Hall

Unified tooling for machine learning interpretability

Data Science, Machine Learning, & AI, Expo Hall

Harsha Nori (Microsoft), Samuel Jenkins (Microsoft), Rich Caruana (Microsoft)

Understanding decisions made by machine learning systems is critical for sensitive uses, ensuring fairness, and debugging production models. Interpretability presents options for trying to understand model decisions. Harsha Nori, Sameul Jenkins, and Rich Caruana explore the tools Microsoft is releasing to help you train powerful, interpretable models and interpret existing black box systems. Read more.

1:15pm–1:55pm Wednesday, September 25, 2019

Location: 1E 14

War stories from the front lines of ML

Law and Ethics, Strata Business Summit

Andrew Burt (bnh.ai), Brenda Leong (Future of Privacy Forum), David Florsek (IDEMIA NSS), Alex Beutel (Google Brain), Chris Wheeler (Mastercard)

Machine learning techniques are being deployed across almost every industry and sector. But this adoption comes with real, and oftentimes underestimated, privacy and security risks. Andrew Burt and Brenda Leong convene a panel of experts including David Florsek, Chris Wheeler, and Alex Beutel to detail real-life examples of when ML goes wrong, and the lessons they learned. Read more.

2:05pm–2:45pm Wednesday, September 25, 2019

Location: 1A 12/14

Fair, privacy-preserving, and secure ML

Data Science, Machine Learning, & AI, Security and Privacy

Mikio Braun (Zalando)

With ML becoming more mainstream, the side effects of machine learning and AI on our lives become more visible. You have to take extra measures to make machine learning models fair and unbiased. And awareness for preserving the privacy in ML models is rapidly growing. Mikio Braun explores techniques and concepts around fairness, privacy, and security when it comes to machine learning models. Read more.

2:05pm–2:45pm Wednesday, September 25, 2019

Location: 1E 14

Regulations and the future of data

Security and Privacy, Strata Business Summit

Andrew Burt (bnh.ai), Brenda Leong (Future of Privacy Forum), Boris Segalis (Cooley), Susan Israel (Loeb & Loeb, LLP)

From the EU to California and China, more of the world is regulating how data can be used. Andrew Burt and Brenda Leong convene leading experts on law and data science for a deep dive into ways to regulate the use of AI and advanced analytics. Come learn why these laws are being proposed, how they’ll impact data, and what the future has in store. Read more.

2:55pm–3:35pm Wednesday, September 25, 2019

Location: 1E 10/11

Executive Briefing: Understanding the cult of prediction

Executive Briefing and best practices, Strata Business Summit

Farrah Bostic (The Difference Engine)

We're living in a culture obsessed with predictions. In politics and business, we collect data in service of the obsession. But our need for certainty and control leads some organizations to be duped by unproven technology or pseudoscience—often with unforeseen societal consequences. Farrah Bostic looks at historical—and sometimes funny—examples of sacrificing understanding for "data." Read more.

5:25pm–6:05pm Wednesday, September 25, 2019

Location: 1E 14

Looking beyond the binary: How data for development impacts gender justice?

Case studies, Strata Business Summit

Brindaalakshmi K (Independent Consultant)

There's a lack of standard for the collection of gender data. Brindaalakshmi K takes a look at the implications of such a lack in the context of a developing country like India, the exclusion of individuals beyond the binary genders of male and female, and how this exclusion permeates beyond the public sector into private sector services. Read more.

11:20am–12:00pm Thursday, September 26, 2019

Location: 1A 12/14

A practical guide to algorithmic bias and explainability in machine learning

Data Science, Machine Learning, & AI

Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)

Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge. Read more.

1:15pm–1:55pm Thursday, September 26, 2019

Location: 1E 10/11

Executive Briefing: Lessons from the front lines—Building a responsible AI/ML program in the enterprise

Executive Briefing and best practices, Strata Business Summit

Keegan Hines (Capital One)

This talk will explore some of the philosophy around the concept of explaining a model given the colloquial definition is partially recursive. It will cover the lens banking regulation places on this philosophical basis and expand into techniques used for these well governed aspects. Read more.

3:45pm–4:25pm Thursday, September 26, 2019

Location: 1E 14

Purposefully designing technology for civic engagement

Law and Ethics, Strata Business Summit

Audrey Lobo-Pulo (Phoensight), Annette Hester (National Energy Board, Canada)

As new digital platforms emerge and governments look at new ways to engage with citizens, there's an increasing awareness of the role these platforms play in shaping public participation and democracy. Audrey Lobo-Pulo, Annette Hester, and Ryan Hum examine the design attributes of civic engagement technologies and their ensuing impacts and an NEB Canada case study. Read more.