Next QConSF Conference: Applied AI for Developers QCon.ai April 2019

Speaker: Animesh Singh

STSM, AI and Machine Learning @IBM

Animesh Singh is an STSM and lead for IBM Watson and Cloud Platform, currently leading Machine Learning and Deep Learning initiatives on IBM Cloud. He has been with IBM for more than a decade and is currently working with communities and customers to design and implement Deep Learning, Machine Learning and Cloud Computing frameworks. He has been leading cutting edge projects for IBM enterprise customers in Telco, Banking, and Healthcare Industries, around cloud and virtualization technologies. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. He also led the design and development first IBM public cloud offering and was the lead architect for Bluemix Local. Find Animesh on Twitter @AnimeshSingh.

Talk : Create a Fair & transparent AI Pipeline with AI Fairness 360

Track : Sponsored Solutions Track II
Location : Marina
Day of week : Monday
Duration : 11:50am - 12:40pm

One of the most critical and controversial topics around artificial intelligence centers around bias. As more apps come to market that rely on artificial intelligence, software developers and data scientists can unwittingly (or perhaps even knowingly) inject their personal biases into these solutions.

Because flaws and biases may not be easy to detect without the right tool, we have launched AI Fairness 360, an open source library to help detect and remove bias in machine learning models and data sets.

The AI Fairness 360 Python package includes a comprehensive set of metrics for data sets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in data sets and models. The research community worked together to create 30 fairness metrics and nine state-of-the-art bias mitigation algorithms.

We will share lessons learned while using AI Fairness 360 and demonstrate how to leverage it to detect and de bias models during pre-processing, in-processing, and post-processing. We will explain how to take these practices and apply them on training in on a more robust environment using Fabric for Deep Learning (FfDL, pronounced “fiddle”) which provides a consistent way to run various scalable deep learning frameworks as a service on Kubernetes.

Other talks from track Sponsored Solutions Track II

Move Fast, Follow Everything & Focus on What Matters Most
Senior Sales Engineer @AppDynamics
Jun Ding
NoSQL + SQL = MySQL
MySQL Principal Sales Consultant
Nicolas DeRico
Fullstack Graph Applications With Neo4j
Software Engineer @neo4j
William Lyon
Create a Fair & transparent AI Pipeline with AI Fairness 360
Software developer @IBM, committer to Apache Bahir and contributor to Jupyter Enterprise Gateway
Christian Kadner
Efficient and Sustainable Unit Testing
Director of Development @Parasoft
Nathan Jakubiak
Modern Authentication Demystified
Full-stack developer @Auth0
Ado Kukic

Tracks

Monday, 5 November

Tuesday, 6 November

Wednesday, 7 November

This Year's Schedule

The all-new QCon app!

Available on iOS and Android

The new QCon app helps you make the most of your conference experience. Easily browse and follow the conference schedule, star the talks you want to attend, and keep tabs on your personal itinerary. Download the app now for free on iOS and Android.