value-sensitive design
In plain wordsA way of designing technology that deliberately identifies the human values at stake, such as privacy, fairness or autonomy, and builds them in from the start instead of patching problems later.
A methodology for designing technology that treats the human values at stake, such as privacy, autonomy and fairness, as explicit design criteria from the earliest stage onward. It combines three iterative kinds of investigation: conceptual (which values are implicated, for which direct and indirect stakeholders, and how they trade off), empirical (how stakeholders actually experience and prioritize those values, studied with interviews, surveys and observation) and technical (how particular architectures and features support or hinder them). Its tools include stakeholder analysis, value scenarios and envisioning cards. In AI it underpins efforts to build fairness, privacy, transparency and human control into systems from the requirements stage, and it informs ethics-by-design guidance.
Using value-sensitive design, the team interviewed people who would never use the app but whose data it would process.
Origin
Developed by Batya Friedman with Peter Kahn and colleagues, chiefly at the University of Washington, from the early 1990s; Friedman's 1996 article 'Value-sensitive design' in Interactions was an early statement, and Friedman and David Hendry's book Value Sensitive Design: Shaping Technology with Moral Imagination (MIT Press, 2019) consolidated it.
Usage note
Critics ask whose values count and how conflicts between them are resolved; proponents reply that the method makes such choices explicit instead of leaving them buried in engineering defaults.
Related
- AI ethicsAI ethics, n.: The study of what is right and wrong in how AI is built and used: who it may harm, who is responsible when it fails, and which values it should respect.
- responsible AIresponsible AI, n.: An umbrella name for the practices organizations use to keep their AI systems fair, safe, private and accountable, from the first design meeting to everyday use.
- human-centered AIhuman-centered AI, n.: An approach to AI that puts people's needs, abilities and control at the center: systems designed to help humans do things better rather than to replace or overrule them.
- algorithmic fairnessalgorithmic fairness, n.: The effort to make automated decisions fair: to say in numbers what fair treatment means, measure whether a system achieves it, and correct the system if it does not.
- AI transparencyAI transparency, n.: Openness about AI: telling people when they are dealing with one, how a system was built and tested, and why it reached a particular decision.
Sources
- Friedman, B. (1996). Value-sensitive design. Interactions 3(6), 16–23.
- Friedman, B. & Hendry, D. G. (2019). Value Sensitive Design: Shaping Technology with Moral Imagination. MIT Press.
Mentioned in
AI ethicsAI ethics, n.: The study of what is right and wrong in how AI is built and used: who it may harm, who is responsible when it fails, and which values it should respect., human-centered AIhuman-centered AI, n.: An approach to AI that puts people's needs, abilities and control at the center: systems designed to help humans do things better rather than to replace or overrule them., techno-solutionismtechno-solutionism, n.: The belief that technology, such as a clever app or algorithm, can fix complicated social problems on its own, overlooking their political, economic and human causes., value alignmentvalue alignment, n.: Building AI whose goals reflect human values — what people care about and consider right — rather than one that merely follows orders or chases a narrow target.