Data Con LA 2020 Description More and more organizations are embracing AI technology by infusing it in their products and services to to differentiate themselves against their competitors. AI is being utilized in some sensitive areas of human life. In this session let's look at some of principles governing adoption of AI in a responsible manner. Why companies are accelerating adoption of AI? Increasingly organization are accelerating adoption of AI to differentiate their product and services in the market. Outcomes of this digital transformation that we have seen in the areas of optimizing operations, engaging customers, empowering employees and transforming their products and services. *List some of the sensitive use cases where AI is being applied *Why governing AI is important and what are those principles? *How Microsoft is approaching it? Speaker Suresh Paulraj, Microsoft, Principal Cloud Solution Architect Data & AI

1. Responsible AI
Opportunity and Responsibility in the Era of Artificial Intelligence
Suresh Paulraj – Principal Cloud Solution Architect

2. Why responsible AI?
“The more powerful the tool, the greater the benefit
or damage it can cause…Technology innovation is
not going to slow down. The work to manage it
needs to speed up.”
Brad Smith
President and Chief Legal Officer, Microsoft

3. Why responsible AI?
Advancements in AI are different than other technologies because of the pace of innovation, and
its proximity to human intelligence – impacting us at a personal and societal level.
Vision
2016
Object
recognition
human parity
Speech
Recognition
2017
Speech
recognition
human parity
Reading
2018
Reading
comprehension
human parity
Translation
2018
Machine
translation
human parity
Speech
Synthesis
2018
Speech synthesis
near-human
parity
Language
Understanding
2019
General
Language
Understanding
human parity

4. The Opportunities with AI
Healthcare Retail Financial Services Manufacturing

5. Today’s Debate
 Facial Recognition
 Fairness
 Corporate responsibility
 Deepfakes
 Human rights
 Meaningful human control
 Contact tracing
 Consent
 Unintended consequences
 Disproportionate impact
 Model Fragility
 Socio-technical issues
 Algorithmic auditing
 Platform accountability
 Regulation

6. Learn Microsoft's AI principles

7. Putting Responsible AI into
Practice

8. Putting responsible AI into practice
Principles
Fairness
Accountability
Transparency
Inclusiveness
Reliability & Safety
Privacy & Security

9. Putting responsible AI into practice
Practices
Principles
Fairness
Accountability
Transparency
Inclusiveness
Reliability & Safety
Privacy & Security
Human-AI Guidelines
Conversational AI Guidelines
Inclusive Design Guidelines
AI Fairness Checklist
Datasheets for Datasets

10. Putting responsible AI into practice
Tools
Practices
Principles
Fairness
Accountability
Transparency
Inclusiveness
Reliability & Safety
Privacy & Security
Understand
Protect
Control

11. Responsible AI Tooling
UNDERSTAND
Interpret ML Fairlearn
PROTECT
Homomorphic
Encryption
Differential
Privacy
Presidio Confidential ML
CONTROL
MLOPs