Human-Machine Learning: Using Feedback to Design Systems that Serve Humanity
About this audiobook
...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
Forget to design a solution once and for all - with Machine Learning, it simply doesn’t work!
Since learning is inherently dynamic, designers must 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.
With Machine Learning, designers are crucial 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.
'Human-Machine learning’ is a design paradigm that enables humans and machines to learn and adapt.
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.
Six things you will find in this book:
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
About the author
Author of “Human-Machine Learning“, Corinne is currently driving UX Research and Design at Amazon Web Services.
With a background in Psychology and Information Technology she has always been drawn to the intersection between Design and Machine Learning, collecting expertise in the area of 7 years.
Her past experience ranges from working as a UX & Service Designer at NTTDATA to being a web designer for small agencies.
Her true love in each position has been listening to customers, diving into their needs to inform product vision, strategy, ideation, and interaction iteration.
My journey as an author began when I moved from Italy to Ireland, and while unpacking, I took in my hands my Master's degree thesis on AI for Music Recommendation and Generation. Reading some pages, I realized how my studies and work experience were bringing me to write this book.
Human-Machine Learning is the result of 10 years of learning, started during my university studies, and I hope it can help other Researchers and Designers to work in the ML field.
