Smart Design has practical constraints. The examples below, demonstrate how Design-led product organizations and companies have created enduring guidelines that help builders and designers create clear-eyed guardrails that ensure principled driven customer experiences.
Apple HIG. Machine Learning
Apple guidelines for designing UI and user experience of a machine learning app. Machine learning is a powerful and versatile tool that can help you improve existing experiences and create new ones that people love. In addition to providing familiar features like image recognition and content recommendations, your app can use machine learning to forge deep connections with people and help them accomplish more with less effort.
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Microsoft guidelines for Human-AI interaction
The Guidelines for Human-AI Interaction synthesize more than 20 years of thinking and research in human-AI interaction. Developed in a collaboration between Aether, Microsoft Research, and Office, the guidelines were validated through a rigorous, 4-step process described in the CHI 2019 paper, Guidelines for Human-AI Interaction. They recommend best practices for how AI systems should behave upon initial interaction, during regular interaction, when they’re inevitably wrong, and over time.
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IBM Design for Ai
To design for a relationship with AI, we need to know ourselves first. Our practice is built on IBM’s Principles for the AI Era as a resource for all designers and developers. This shared collection of ethics, guidelines, and resources ensures that IBM products share a unified foundation. A shared collection of ethics, guidelines, and resources for designing a relationship with AI from IBM.
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People + AI Research
People + AI Research (PAIR) is a multidisciplinary team at Google that explores the human side of AI by doing fundamental research, building tools, creating design frameworks, and working with diverse communities. Google believes that for machine learning to achieve its positive potential, it needs to be participatory, involving the communities it affects and guided by a diverse set of citizens, policy-makers, activists, artists and more.
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Spotify principles for designing ML-powered products
Machine Learning (ML) has become an indispensable tool at Spotify for delivering personal music and podcast recommendations to over 248 million listeners across 79 markets and in 24 languages. We believe designers have a vital role in ML-driven initiatives, by bringing a human-centered perspective to a technology that can too easily overlook the end-user. If we don’t apply a human-centered lens to our design process, we risk optimizing for solutions that don’t resonate with users—or worse, completely deliver the wrong solution. In a world where the product uniquely adapts to each user, we’ve found that creating deeply personalized products requires a new type of mindset and approach to design. Equipped with research about our users, and a deep understanding of business goals, we traditionally define detailed flows of a user’s journey, invent and refine interactions, and visually brand the experience—hopefully with some delight.
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Lingua Franca: A Design Language for Human-Centered AI
A set of techniques, frameworks, visuals, messaging, and overall design patterns that apply broadly to different kinds of AI. This document is the sum of our collective imagination about how to build, test, measure, critique, and improve—in short, to design—the AI technology rapidly proliferating around us. In it, we hope you will find a wealth of practical, actionable information, regardless of your education or profession or skill set. This is a design language for AI—a standard set of techniques, frameworks, visuals, messaging, and overall design patterns that apply broadly to different kinds of AI to make it more usable, more trustworthy, and better aligned with people.
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IDEO Principles
A set of principles and activities that IDEO team use today to ensure they’re intentionally designing intelligent systems in service of people
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