12-01, 08:30–09:00 (UTC), Talk Track I
For enterprises to adopt and embrace AI into their transformational journey, it is imperative to build Trustworthy AI- so that AI products and solutions that are built, delivered, and acquired are responsible enough to drive trust and wider adoption. We look at AI Trust as a function of 4 key constructs which include Reliability, Safety, Transparency, Responsibility and Accountability. These core constructs are pillars of driving AI trust in our products and solutions. In this talk, I will explain how to enable each core construct and will articulate how they can be measured in some real-world use cases.
After several iterations, rigorous research and based on our collective experiences – we established 4 pillars of trust:
• Any AI system should be strong on performance expectations on a given use case
• Any decisions from the model are explainable and traceable
• Any biased decisions towards a specific group of population need to be safeguarded
• Lastly, the model needs to be secured from any malicious or non-malicious attacks
These pillars of AI trust are further broken down into several dimensions and metrics that are quantifiable.
This talk will be delivered in three parts.
Part 1: Need of Trust in AI with real-life examples – 5 minutes
Part 2: Construct, dimensions, and influencing factors of trust in AI – 10 minutes
Part 3: Framework for AI trust calculator leveraging real-life use-cases – 15 minutes
No previous knowledge expected
Shashank is Data Sciences leader with diverse experience across verticals including Telecom, CPG, Retail, Hitech and E-commerce domains. He is currently heading the Artificial Intelligence Labs at Subex. In the past, he has worked in VMware, Amazon, Flipkart and Target and has been involved in solving various complex business problems using Machine Learning and Deep Learning. He has been part of the program committee of several international conferences like ICDM and MLDM and was selected as a mentor in Global Datathon 2018 organized by Data Sciences Society. He has multiple patents and publications in the field of artificial intelligence, machine learning, deep learning and image recognition in several international journals of repute to his credit. He has spoken at many summits and conferences like PyData Global, APAC Data Innovation Summit, Big Data Lake Summit, PlugIn etc. He has also published three open-source libraries on Python and is an active contributor to the global Python community.