We present a novel de-centralised service for proving the provenance of online digital identity, exposed as an assistive tool to help non-expert users make better decisions about whom to trust online. Our service harnesses the digital personhood (DP); the longitudinal and multi-modal signals created through users’ lifelong digital interactions, as a basis for evidencing the provenance of identity. We describe how users may exchange trust evidence derived from their DP, in a granular and privacy-preserving manner, with other users in order to demonstrate coherence and longevity in their behaviour online. This is enabled through a novel secure infrastructure combining hybrid on- and off-chain storage combined with deep learning for DP analytics and visualization. We show how our tools enable users to make more effective decisions on whether to trust unknown third parties online, and also to spot behavioural deviations in their own social media footprints indicative of account hijacking.

In this paper we report our study involving an early prototype of TAPESTRY, a service to support people and businesses to connect safely online through the use of a Machine Learning generated visualization. Establishing the veracity of the person or business behind a pseudonomized identity, online, is a challenge for many people. In the burgeoning digital economy, finding ways to support good decision-making in potentially risky online exchanges is of vital importance. In this paper, we propose a Machine Learning method to extract temporal patterns from data on individuals' behavioral norms in their online activity. This monitors and communicates the coherence of these activities to others, especially those who are about to disclose personal information to the individual, in a visualization. We report findings from a user trial that examined how people accessed and interpreted the TAPESTRY visualization to inform their decisions on who to back in a mock crowdfunding campaign to evaluate its efficacy. The study proved the protocol of the Machine Learning method and qualitative insights are informing iterations of the visualization design to enhance user experience and support understanding.

This paper reports a qualitative study of thriving older people and illustrates the findings with design fiction. Design research has been criticized as "solutionist" i.e. solving problems that don't exist or providing "quick fixes" for complex social, political and environmental problems. We respond to this critique by presenting a "solutionist" board game used to generate design concepts. Players are given data cards and technology dice, they move around the board by pitching concepts that would support positive aging. We argue that framing concept design as a solutionist game explicitly foregrounds play, irony and the limitations of technological intervention. Three of the game concepts are presented as design fictions in the form of advertisements for products and services that do not exist. The paper argues that design fiction can help create a space for design beyond solutionism.