[NeurIPS Workshop Keynote] Practical AI Ethics - A Guide Towards Responsible ML

As the impact of data science & engineering reaches increasingly farther and wider, our professional responsibility as researchers and practitioners becomes more critical to society. The production data-driven systems we research, design, build and operate often bring inherent adversities with complex technical, societal and even ethical challenges. The skillsets required to tackle these challenges require us to go beyond the algorithms, and leverage diverse and cross-functional collaboration that often goes beyond a single data scientist or developer. In this talk we will cover practical insights from key core ethics themes in data science & engineering, including Privacy, Equity, Trust and Transparency. We will dive into the importance of these core themes, the growing societal challenges, and how organisations such as The Institute for Ethical AI, The Linux Foundation, the Association for Computer Machinery, NumFocus, the IEEE and other relevant academic and industry organisations are contributing to these through standards, policy advise and open source software initiatives.

Sections:
Introduction & Overview: (0:00)
Responsibility Hierarchies: (1:10)
Going Beyond the Algorithm: (4:52)
Programmatic Governance: (6:33)
AI Ethics & Principles: (8:27)
AI Industry Standards: (14:44)
AI Regulation & Policy: (16:20)
AI Open Source Strategy: (18:48)
Organisational AI Best Practices: (21:44)
Practical AI Ethics Case Study: (24:33)
AI Ethics Wraup-up: (29:27)

[kontramind]

Many thanks for the inspiring talk. Was wondering where one can find the slides and links on the subtopics? Cheers