P.A. Lloyd
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24 records found
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Reflective AI
A Slow Technology Approach for Design Education
Synthetic users
Insights from designers’ interactions with persona-based chatbots
Does AI need designing?
Does AI Need Designing? Exploring Design in Clinical AI
This Conversation at DRS2024 in Boston, attracting around 30 participants, centered on the evolving role of design within multidisciplinary AI teams, particularly in the context of the development of clinical AI applications. As AI is entering healthcare, questions are being raised if and how designers can contribute to AI-driven clinical solutions and whether they need to develop potential other skills and responsibilities. Drawing from ongoing research and insights from various workshops and interviews, the conversation highlighted the importance of the negotiation of agency between humans and machines in clinical settings, translational design for patients, data hierarchy and its impact on design, and finally the importance of language and storytelling in framing interactions mediated by AI.
Objective Portrait
A practice-based inquiry to explore Al as a reflective design partner
Educational systems face increasingly complex demands, confronting teachers with multidimensional people-centred problems rarely solved by linear or standardised solutions. Nevertheless, teachers must juggle multiple variables simultaneously in their daily work. This can lead to routine and unreflective decisions that do not consider unique situations. Considering that designers’ abductive reasoning could support problem-framing skills, this article discusses how a design thinking approach can contribute to developing reflective teaching practice. This case study explores how 20 Chilean teachers define, frame, and re-frame their pedagogical problems in a design-based teacher professional development programme. Findings revealed three problem-framing triggers that support teachers’ reflection: (a) collaborative discussions, (b) awareness of people and their context, and (c) visualising, making, and testing ideas. Combined, they articulate action and promote reflection, demonstrating the value of a design thinking approach in supporting teachers’ pedagogical decisions.
Constructing design activity in words
Exploring linguistic methods to analyse the design process
Analysing transcripts of design activity typically involve either close reading or manual coding of data, which limits the amount of data that can be analysed. In contrast, we explore a machine-learning based linguistic analysis tool called Empath to identify patterns of reasoning in design talk. The data we use derives from the Design Thinking Research Symposium (DTRS) shared-data workshops which we analyse to look at two contrasting aspects of design talk: the expression of tentativeness, characterising designers' generative thinking; and the articulation of explanations, characterising their deductive or analytical thinking. We show, at the level of speech turns, how tentativeness and explanation relate to, and overlap, each other. Finally, we discuss the limitations of this ‘linguistic analysis at scale’ approach.
Analyzing Storytelling in Design Talk using LIWC
(Linguistic Inquiry and Word Count)
Trust in Clinical AI
Expanding the Unit of Analysis
From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.
Triggered
Using human-ai dialogue for problem understanding in collaborative design
Creative conversation among designers and stakeholders in a design project enables new ideas to naturally originate and evolve. Language allows for the exchange of values, priorities, and past experience whilst keeping solution forms usefully ambiguous. Yet there is a danger that only the language of people directly involved in the design process gets to be heard, limiting how inclusively the problems are interpreted, which in turn can impede how complex design problems are addressed. Recent advances in artificial intelligence (AI) have shown the exclusionary spaces that are often inhabited by designers, engineers, and developers of new artefacts and technologies. On the other hand, text data used to train language models for machine learning applications have the potential to highlight societal biases in ways that designers can utilise. In this paper, we report the results of an exploratory study using AI text generation to synthesize and narrate opinions and experiences that may be unfamiliar to designers. Three pairs of designers were given a complex socio-technical problem to solve. Of these, two pairs interacted with an AI text generator during the task, while one pair acted as a baseline condition. Analysing the conversational exchanges between the designers and the designers & AI, we observe how the use of AI leads to prompting nuanced interpretations of problems and ideas, opening up the objective problem and design lenses and interpretations. Finally, we discuss how the designers (re)assign different roles to the AI to suit their creative purposes.