Ethics Machine-LearningSee in schedule
As the impact of software increasingly reaches farther and wider, our professional responsibility as developers becomes more critical to society. The production systems we design, build and maintain 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 require cross-functional collaboration that often goes beyond a single developer. In this talk we introduce intuitive and practical insights from a few of the core ethics themes in software including Privacy, Equity, Trust and Transparency. We cover their importance, 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 the Python Software Foundation are contributing to these critical themes through standards, policy advise and open source software initiatives. We finally will wrap up the talk with practical steps that any individual can take to get involved and contribute to some of these great open initiatives, and contribute to these critical ongoing discussions.
Type: Talk (45 mins); Python level: Beginner; Domain level: Beginner
Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads the development of industry standards on machine learning bias, adversarial attacks and differential privacy. Alejandro is also the Director of Machine Learning Engineering at Seldon Technologies, where he leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has delivered multi-national projects with top tier investment banks, magic circle law-firms and global insurance companies. He has a strong track record building cross-functional departments of software engineers from scratch, and leading the delivery of large-scale machine learning systems across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).