Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

AI Privacy and Ethical Compliance Toolkit

Iman Saleh (Intel), Cory Ilo (Intel), Cindy Tseng (Intel)
9:00am12:30pm Tuesday, March 26, 2019
Secondary topics:  Ethics, Security and Privacy

Who is this presentation for?

Developers, data scientists, and solutions architects

Level

Intermediate

Prerequisite knowledge

Basic machine learning concepts. Reading and understanding Python code. Basic knowledge with deep learning.

Materials or downloads needed in advance

A laptop with Python Interpreter. Audience can also only follow the code as it is provided in a step-by-step tutorial format.

What you'll learn

Best practices for ensuring fairness and privacy compliance for machine learning applications. Tools that data scientists can use to ensure privacy and fairness of their models Guidance on privacy by design concepts

Description

New applications of machine learning are raising ethical concerns about a host of issues, including bias, transparency, and privacy. In this tutorial, we will demonstrate tools and capabilities that can help data scientists address these concerns. The tools help bridge the gap between ethicists and regulators on one side, and machine learning practitioners on the other side. Namely, we will present 3 tools:

(1) Privacy-Preserving Face Landmarks Detection: We will show how to design for privacy preservation in a face detection framework. This design approach enables the extraction of facial features and does not compromise the user’s identity.

(2) Vehicle Data Assurance (VEDA): Autonomous Vehicles are characterized by the collection of huge amount of sensor data used to train ML models. We provide a solution, VEDA, to ensure compliance with strict privacy regulations regarding the use and handling of this data, and to increase trust in the collected data and its management lifecycle.

(3) Bias Detection and Remediation: It has been shown that computer vision algorithms can be biased to certain age, race or gender based on the training datasets. We will show by example how to detect these biases and how tools can be used to rebalance a biased dataset.

Photo of Iman Saleh

Iman Saleh

Intel

Iman Saleh is a Research Scientist with the Automotive Solutions group. She holds a Ph.D. from the Computer Science department at Virginia Tech, a Master degree in Computer Science from Alexandria University, Egypt. And, a Master degree in Software Engineering from Virginia Tech. Dr. Saleh has 30+ technical publications in the areas of big data, formal data specification, service-oriented computing and privacy-preserving data mining. Her research interests include ethical AI, machine learning, privacy-preserving solutions, software engineering, data modeling, Web services, formal methods and cryptography.

Photo of Cory Ilo

Cory Ilo

Intel

Cory Ilo is a computer vision engineer in the Automotive Solutions group at Intel. He helps prototype and research the feasibility of various computer vision solutions in relation to privacy, ethics, deep learning, and autonomous vehicles. In his spare time, Cory focuses on his passion for fitness, video games, and wanderlust, in addition to finding ways on how they tie into computer vision.

Photo of Cindy Tseng

Cindy Tseng

Intel

Cindy Tseng is a Research Scientist with the Intel Applied Research in Automotive Driving group. She holds a Master degree from the Electrical and Computer Engineering department at Carnegie Mellon University, and a bachelor’s degree in Electrical Engineering and Computer Science from University of Michigan at Ann Arbor. Cindy is currently enrolled as a part time student in the Masters in Data Science program in Computer Science from University of Illinois at Urbana Champaign. Cindy Tseng has worked in the space of high throughput computing and deep learning hardware accelerators. Her recent work in the Applied Research group covers bias detection in convolution neural nets.

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