between users, data, and values?
What patterns make data flows visible, legible, and actionable—and which ones obscure them? This part translates your data and value insights into interface patterns and sketches. Building on your mapped ecosystem, you’ll identify user touch points and outline interface systems that go beyond usability and efficiency to actively shape understanding, behavior, and agency.
Survey existing patterns and develop new ones that shape how users encounter, interpret, and act on data.
How do interfaces encode priorities through what data they highlight, hide, or make actionable? For example, does a fitness app emphasize competitive metrics or holistic wellness? Does a social feed prioritize engagement signals or content quality indicators?
More specifically:
https://www.apple.com/privacy/labels/ Developers must declare 14 data types across categories, and the interface prioritizes visibility of tracking vs. non-tracking data collection, expressing Apple's stated values around privacy.
https://newsroom.spotify.com/2025-12-05/wrapped-methodology-explained/ Spotify's annual Wrapped feature transforms listening data into shareable visual stories. The interface emphasizes total streams for "Top Songs" but uses weighted stream counts for "Top Artists," revealing the algorithmic rules that shape how users understand their own behavior.
How do interfaces reveal what actions are possible and what happens as a result? How do interfaces surface the sources, flows, and stakeholders behind the data?
Look for patterns that make algorithmic processes visible (e.g., "you're seeing this because..."), clarify data usage ("this information will be shared with..."), or expose system logic ("ranked by..." or "filtered to show...").
This might also include provenance indicators, data lineage visualizations, or disclosure of third-party access and purposes.
How do interfaces help users understand short-term and long-term implications of their choices? Look for patterns like impact previews ("sharing this will..."), consequence warnings, scenario comparisons, or future state simulations.
What mechanisms give users agency over their data and interactions? Examples include granular permission controls, reversible actions, exportable data, customizable parameters, or real-time feedback about how choices affect system behavior.
https://proton.me/blog/new-protonmail-announcement ProtonMail's UI uses visual cues (lock icons, color coding) to clearly indicate when emails are encrypted and secure, giving users immediate feedback about the security status of their communications.
How do interfaces create memory and enable retrospection? Look for patterns like activity logs, decision archives, change histories, or periodic summaries that help users understand their own patterns and hold systems accountable.
https://www.hudsong.dev/spotify-wrapped-2024-data-analysis Services like Spotify (via "Account Privacy") and Google (via Takeout) allow users to download complete archives of their interaction history, enabling external analysis and long-term accountability.
https://docs.github.com/en/account-and-profile/concepts/contributions-on-your-profile GitHub displays a year-long grid showing daily contribution activity, creating a persistent visual trace of development work that supports both personal reflection and public accountability.
Rather than automate or optimize actions: How do interfaces introduce friction, pacing, or prompts that encourage mindful interaction? Examples include confirmation steps before consequential actions, cooling-off periods, batch notifications instead of real-time alerts, or reflective prompts that ask users to articulate intent before proceeding.
https://www.smashingmagazine.com/2018/01/friction-ux-design-tool/
Using Friction As A Feature In Machine Learning Algorithms — Smashing Magazine