Observed Signal · Apr 24, 2026 · Analysis / Guidance Review · Source: UX Collective · Impact: 2/5 · Sentiment: Positive
Rulebook for Designing AI Experiences
Three major technology organisations—Microsoft, Google and IBM—have published practical, pattern-based resources for designing Human-AI Interaction (HAI). Microsoft’s HAX Toolkit centres on 18 guidelines and a HAX Design Library with filterable design patterns; Google’s People + AI Guidebook is organised into six thematic chapters and (in its 2021 second edition) 23 practical design patterns and worksheets; IBM pairs AI Design Ethics with an AI Essentials Framework of five pillars and at CHI 2024 presented six design principles tailored to generative AI. The frameworks align on core concerns such as transparency, user control, feedback loops and graceful failure, but the author notes gaps around diversity, non-discrimination, fairness and environmental/social impacts. The piece recommends treating these resources as practical thinking tools rather than prescriptive checklists.
Practical guidance from major tech organisations helps product and UX teams design safer, more usable AI features; convergence across resources signals shared best practices, while identified gaps (fairness, diversity, environmental impact, generative-AI specifics) are actionable risks for product teams.
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Key Takeaways & Evidence Grounding
- Microsoft published the HAX Toolkit centred on 18 Human-AI Interaction guidelines and a filterable HAX Design Library.
- Google’s People + AI Guidebook (maintained by the PAIR team) is organised into six chapters and the 2021 second edition added 23 design patterns and worksheets.
- IBM’s AI Essentials Framework defines five pillars: intent, data, understanding, reasoning, and knowledge; IBM Research presented six generative-AI design principles at CHI 2024.
- The three frameworks converge on transparency, user control/correction, feedback loops, and graceful failure but are relatively thinner on diversity, fairness, and environmental/social well-being.
- Traffic to Google’s People + AI Guidebook increased by 560% between February and August 2023 as generative-AI products proliferated.
Connected Companies & Entities
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Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Translating Ethical Design into Machine-Readable Rules
The article argues that ethical principles for AI-assisted design must be translated into concrete, machine-readable rules (for example, in plain-text DESIGN.md files) before AI can reliably follow them. It describes how teams already use DESIGN.md files to encode visual and technical standards and cites Productic’s guidance to include accessibility (WCAG AA). The author proposes an "Ethical Interface Design" framework with five pillars — Inclusion, Autonomy, Transparency, Privacy, and Well-being — and explains the difficulty of turning broad ethical values into measurable rules comparable to accessibility standards like WCAG. The piece calls for defining specific, testable rules and consensus on who writes them before delegating ethical decisions to machines.
Mapping AI Presence to User Intent
Bradly Zavakos published a product-design guide (2026-04-27) proposing an "AI presence framework" that helps teams decide how and when AI should surface in user experiences. The framework defines discrete involvement levels—Level 1 (Shoulder tap), Level 2 (Back-and-forth discussion), Level 3 (Let me help), and Level 0 (Take over control as a safety constraint)—and pairs them with a confidence continuum for action: high confidence (act directly), moderate (clarify), low (ask before generating), and very low (light nudge). Zavakos ties the approach to existing design guidance (Google PAIR, Microsoft Human-AI Interaction), recommends separating core decision logic from project-specific mappings, and published a companion GitHub repo with an example implementation. The article emphasizes choosing when to step back as the central design decision for trustworthy AI experiences.
Microsoft drafts 'Humanist AI' Code of Conduct
Microsoft AI has published a draft of its 'Humanist AI Code of Conduct', a comprehensive governance framework for future AI models. The code aims to ensure human control, define absolute constraints to prevent harmful actions like weapons development and cyberattacks, and prohibit deceptive emotional manipulation. It is currently in a six-week public consultation phase and will be revised by year-end to influence model development from 2027. The code also addresses over-cautious AI behavior, requiring a balance between safety and usefulness, and extends to autonomous agents and multi-agent systems. Microsoft positions the document as a 'living document' addressing open questions such as recursive self-improvement and AI collusion.
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