A framework for human-AI integration in the enterprise
Who is this presentation for?
- C-level executives, product managers, AI architects, and data scientists
Recent developments in ML and DL have provided remarkable advances in the predictive capabilities of AI. However, the black box nature of the modern ML and DL models creates challenges for enterprises looking to adopt these techniques and has raised awareness toward human-AI hybrid systems. Three factors, namely regulations (e.g., GDPR), risk (e.g., business criticality), and cost, determine if and how an enterprise product should integrate human and machine intelligence.
Bahman Bahmani details the shortcomings of existing machine learning interpretability approaches and provides architectural blueprints (e.g., parallel versus serial patterns) for human-AI integration. Bahman also introduces the notion of modifiability, a particular form of debuggability, where human operators can not only audit but also modify the behavior of a machine learning system, irrespective of how (e.g., due to which combination of input features) the system decided on its course of behavior. You’ll learn three design ideals for modifiability in critical applications, namely immediacy, determinism, and simplicity (of the modifications), as well as the role of user interfaces (for the humans in the loop) in achieving these design goals.
You’ll see examples and case studies from real-world, operational, large-scale AI systems for applications such as product categorization, information extraction from unstructured documents, and online ad auctioning, and you’ll gain an actionable framework for how to approach human-AI integration for your own enterprise workflows, products, and services.
- Familiarity with AI
What you'll learn
- Gain a framework for integration of human intelligence and machine intelligence in the enterprise
Bahman Bahmani is the vice president of data science and engineering at Rakuten (the seventh-largest internet company in the world), managing an AI organization with engineering and data science managers, data scientists, machine learning engineers, and data engineers globally distributed across three continents, and he’s in charge of the end-to-end AI systems behind the Rakuten Intelligence suite of products. Previously, Bahman built and managed engineering and data science teams across industry, academia, and the public sector in areas including digital advertising, consumer web, cybersecurity, and nonprofit fundraising, where he consistently delivered substantial business value. He also designed and taught courses, led an interdisciplinary research lab, and advised theses in the Computer Science Department at Stanford University, where he also did his own PhD focused on large-scale algorithms and machine learning, topics on which he’s a published author.
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