User experience (UX) design practices have seen a fundamental shift as more and more software products incorporate machine learning (ML) components and artificial intelligence (AI) algorithms at their core. This book will probe into UX design's role in making technologies inclusive and enabling user collaboration with AI.

AI/ML-based systems have changed the way of traditional UX design. Instead of programming a method to do a specific action, creators of these systems provide data and nurture them to curate outcomes based on inputs. These systems are dynamic and while AI systems change over time, their user experience, in many cases, does not adapt to this dynamic nature.

Applied UX Design for Artificial Intelligence will explore this problem, addressing the challenges and opportunities in UX design for AI/ML systems, look at best practices for designers, managers, and product creators and showcase how individuals from a non-technical background can collaborate effectively with AI and Machine learning teams.

You Will Learn:

• Best practices in UX design when building human-centric AI products or featuresAbility to spot opportunities for applying AI in their organizations
• Advantages and limitations of AI when building software products
• Ability to collaborate and communicate effectively with AI/ML tech teams . UX design for different modalities(voice, speech, text, etc.)
• Designing ethical AI system

Autorentext

Akshay Kore is a senior product designer working on Human-computer interaction problems. He previously worked with the AI and Research group at Microsoft and currently works with Observe.ai (YC S18) designing AI products and systems. He holds a degree in Interaction Design from IIT Bombay. Akshay regularly contributes to various publications like Hackernoon, The Startup, UX Planet, UX Design magazine on user experience, AI, and voice interfaces. He also frequently speaks at industry conferences, design schools, organizations, and podcasts.

Inhalt

Chapter 1: On intelligence.- Chapter 2: Do you need AI in your product?.- Chapter 3: Mental models.-Chapter 4: The Reward function.- Chapter 5: Automation vs. Augmentation.- Chapter 6: Human in the loop.- Chapter 7: Mapping workflows.- Chapter 9: Building trust.- Chapter 10: Designing feedback loops.- Chapter 11: Handling errors and failure states.- Chapter 12: Prototyping and testing.- Chapter 13: Working effectively with AI tech teams.- Chapter 14: Modalities.- Chapter 15: AI ethics.- Chapter 16: Future.