5 research outputs found
A Method for Anticipation of Undesirable Interactions in Software for a Digital Society informed by a Thematic Analysis of Discovery Practice
This research explores current user experience design practice in the IT sector through empirical studies with practitioners. The focus is how interactions that are undesirable are identified, because they are contrary to the interests of the users. The practice area of interest is the discovery stage when designers are working to understand the user’s aims and identifying opportunities to achieve the desired outcomes.
Two research questions are explored: what methods are used in current software design practice to identify undesirable interactions during discovery activities, and how can designers be helped to structure their work in a way that assists them in identifying undesirable interactions.
Three empirical studies were conducted with user experience practitioners. The first used Ketso workshops to gather data on discovery goals, practices, and challenges. These informed the second study, which used interviews to gather data on attitudes and practices. Reflexive thematic analysis was used to analyse findings. Using findings from the first two studies and lessons from the existing literature, I developed a new method of anticipating undesirable interactions by identifying ethical properties that the design should preserve and considering how they might be lost. This Jeopardy Analysis method was evaluated in the third study through remote workshops with user experience design practitioners who were asked to apply it to an unfamiliar scenario and provide feedback on its use.
Findings about current practice from the first two studies indicate that user experience practitioners favour methods that build a shared understanding, but select them to suit the context. They tailor their approach, and actively explore and experiment with new methods. There was some recognition of the need to anticipate problems, but no methods were applied at the discovery stage, instead relying on usability testing.
The evaluation of the Jeopardy Analysis method found that it helped to challenge assumptions. Practitioners found framing the problem in ethical terms unfamiliar and difficult, but felt they could use it by themselves with more practice. The generic properties used for the evaluation were found to be too abstract, so the method step tailoring them for the domain would be an important part of its application.
The research contributes insights into the goals practitioners have for their discovery activities, and their current approaches to identifying undesirable interactions. It identifies practitioner interest in recent ‘consequence scanning’ approaches to anticipating problems that differ from current practice, and are associated with a more risk averse mindset. It contributes a novel Jeopardy Analysis method, and reports encouraging results from its initial evaluation.
Further work is needed to refine Jeopardy Analysis for use in industry, and to evaluate practitioner selection of ethical properties tailored to their domain and product. Its natural domain of use is seen as software applications supporting life in our increasingly digital society, where the general public are co-opted into our designs, and the ethical case for intervention is most compelling. Extension of Jeopardy Analysis to involve prospective users in co-analysis and design would further address the potential imbalances of power in current practices. It is suggested that teaching Jeopardy Analysis in higher education settings would contribute to learning outcomes in inclusive design, societal impact, the making of ethical choices, risk management, and the recognition of responsibilities
Design Discovery Practices: Engaging professional design communities with Ketso
Decisions and assumptions made during design sessions, when teams are formulating their design objectives and their understanding of the problem they intend to solve, can be essential to the outcome as they fundamentally shape and direct the design of the product or service that is delivered. Current practice in these crucial design discovery activities is under-explored in the academic literature. To address that shortfall, and answer the research question of how UX practitioners approach and perform discovery, we used the Ketso workshop format to explore the design discovery process and its challenges with 12 user researchers and designers from a university and a large retail organisation. Our thematic analysis of the workshop outputs showed that practitioners valued an empirical data-led approach, where they could have confidence in the coverage and validity of the data, and achieve a shared understanding of the user research findings across the organisation. Key challenges included the mindset of stakeholders, with whom practitioners wanted deeper engagement, and constraints on time which may require HCI research to develop practical solutions
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BRAND: A platform for closed-loop experiments with deep network models.
Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.Significance.By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments
Genome-wide association study of renal cell carcinoma identifies two susceptibility loci on 2p21 and 11q13.3
Contains fulltext :
97937.pdf (publisher's version ) (Closed access)We conducted a two-stage genome-wide association study of renal cell carcinoma (RCC) in 3,772 affected individuals (cases) and 8,505 controls of European background from 11 studies and followed up 6 SNPs in 3 replication studies of 2,198 cases and 4,918 controls. Two loci on the regions of 2p21 and 11q13.3 were associated with RCC susceptibility below genome-wide significance. Two correlated variants (r(2) = 0.99 in controls), rs11894252 (P = 1.8 x 10) and rs7579899 (P = 2.3 x 10), map to EPAS1 on 2p21, which encodes hypoxia-inducible-factor-2 alpha, a transcription factor previously implicated in RCC. The second locus, rs7105934, at 11q13.3, contains no characterized genes (P = 7.8 x 10(1)). In addition, we observed a promising association on 12q24.31 for rs4765623, which maps to SCARB1, the scavenger receptor class B, member 1 gene (P = 2.6 x 10). Our study reports previously unidentified genomic regions associated with RCC risk that may lead to new etiological insights