Schillizzi’s “Human-Machine Learning” maps the intersection of design and ML through the lens of an AWS veteran. Tailored for rising designers, this book explores AI ethics, surveys system thinking, and offers actionable tactics for aligning with engineering and product teams in the tech realm. Its engaging narrative provides a roadmap for iterative “designing in loops” product development in today’s AI-driven industry.John Maeda, Author of How To Speak Machine

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About the book

Learning is inherently dynamic, Machine Learning can’t be designed statically.

Designers need to harness feedback loops to create solutions that adapt to changing environments and data. Discover how to work backward from humans, partner with ML field experts, build effective feedback loop mechanisms and design data-aware interactions.

‘Human-Machine learning’  is a design paradigm that enables humans and machines to learn and adapt.

Designers hold a crucial role in keeping humans and society at the center. The book guides the reader in understanding the challenges and peculiarities of designing these systems. It provides methods and tools to apply a human-centered approach to problem-framing and solving.

Shifting our perspective from a growth to an adaptive mindset, the book presents the Human-machine Learning paradigm as a way to tackle complex problems and drive positive change systemically.

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Six things you will find in it:

  1. The role of feedback in shaping human and machine learning
  2. The role of designers in working backward from human needs in ML projects
  3. How to design with and for data
  4. How to design feedback loops at three levels of interactions: individual, organizational, and societal
  5. A systemic perspective on designing with ML with a humanity-centered approach
  6. How to design for Human-Machine Continual learning

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