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Responsible AI

The document discusses the importance of responsible AI amidst rapid technological advancements and its significant societal and personal impacts. It outlines challenges such as fairness, corporate responsibility, and unintended consequences, while emphasizing Microsoft's AI principles and practices aimed at fostering ethical AI development. The document also highlights various sectors where AI can create opportunities, including healthcare, retail, and financial services.

Data & Analytics◦
Responsible AI Opportunity and Responsibility in the Era of Artificial Intelligence Suresh Paulraj – Principal Cloud Solution Architect
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
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
The Opportunities with AI Healthcare Retail Financial Services Manufacturing
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
Learn Microsoft's AI principles
Putting Responsible AI into Practice
Putting responsible AI into practice Principles Fairness Accountability Transparency Inclusiveness Reliability & Safety Privacy & Security
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
Putting responsible AI into practice Tools Practices Principles Fairness Accountability Transparency Inclusiveness Reliability & Safety Privacy & Security Understand Protect Control
Responsible AI Tooling UNDERSTAND Interpret ML Fairlearn PROTECT Homomorphic Encryption Differential Privacy Presidio Confidential ML CONTROL MLOPs