Making Machine Learning Tangible for UX Designers

There is considerable current research interest in the relationship between machine learning (ML) and user experience design (UX). This comes both from design researchers within the human- computer interaction (HCI) community, who have sought ways for UX designers to work with ML, and data scientists in new types of collaborative practice. The need for a shared language between designers and data scientists has emerged as a key factor, with the creation of boundary objects in the form of sensitising concepts seen as a useful approach. This paper presents original research that responds to the call for such concepts by working directly with UX designers to model aspects of ML technologies in physical form. Our intention is to position designerly abstractions as examples of the type of boundary object able to bridge the domains of UX design and data science and open up new possibilities for the design of ML-driven digital products

Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine Learning

Understanding artificial intelligence (AI) and machine learning (ML) approaches is becoming increasingly important for people with a wide range of professional backgrounds. However, it is unclear how ML concepts can be effectively explained as part of human-centred and multidisciplinary design processes. We provide a qualitative account of how AI researchers explained and non-experts perceived ML concepts as part of a co-design project that aimed to inform the design of ML applications for diabetes self-care. We identify benefits and challenges of explaining ML concepts with analogical narratives, information visualisations, and publicly available videos. Co-design participants reported not only gaining an improved understanding of ML concepts but also highlighted challenges of understanding ML explanations, including misalignments between scientific models and their lived self-care experiences and individual information needs. We frame our findings through the lens of Stars and Griesemer’s concept of boundary objects to discuss how the presentation of user-centred ML explanations could strike a balance between being plastic and robust enough to support design objectives and people’s individual information needs

Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models

Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with people’s needs and expectations. We present a co-design study investigating the benefits and challenges of using computational notebooks to inform ML models with end user groups. We used a computational notebook to engage young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through codesign workshops and retrospective interviews, we found that participants particularly valued using the interactive data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML model lifecycles

Computational Notebooks as Co-Design Tools:Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models

Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with people's needs and expectations. We present a co-design study investigating the benefits and challenges of using computational notebooks to inform ML models with end user groups. We used a computational notebook to engage young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through co-design workshops and retrospective interviews, we found that participants particularly valued using the interactive data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML model lifecycles

Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context

This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0

Inteligência Artificial no Design de Comunicação em Portugal: Panorama e Perspetivas

A tecnologia sempre foi sinónimo de inovação na área do design originando inclusivamente mudanças na sua prática projetual. Atualmente, a Inteligência Artificial (IA) está a alterar profundamente a forma como trabalhamos e comunicamos, tendo impacto em inúmeros setores de atividade. No entanto, não há uma investigação considerável sobre a integração da IA no campo do design. Assim sendo, o principal objetivo desta dissertação é compreender como é que a IA é percecionada e tem vindo a ser aplicada no design de comunicação em Portugal. De modo a atingir este objetivo analisámos a perspetiva de um grupo de designers sobre a IA, tentámos perceber como algumas empresas portuguesas de design utilizam a IA, e se os futuros designers, hoje estudantes, têm conhecimento e estão prontos para trabalhar com IA na sua prática profissional. O método qualitativo de investigação utilizado foi o estudo de caso. A recolha de dados foi feita a partir de entrevistas e questionários conduzidos a designers de 10 empresas portuguesas de design de comunicação. Os websites das empresas também foram analisados com o intuito de perceber se existiria algum envolvimento com a IA. Para tentar compreender a perceção dos estudantes foi feito inclusivamente um questionário a 26 alunos de design de comunicação e multimédia da Universidade da Beira Interior, Universidade do Algarve e Universidade de Coimbra. A investigação concluiu que os designers possuem fundamentos básicos de IA, conhecendo as principais vantagens e algumas desvantagens da mesma. No geral, expressam sentimentos positivos quanto à sua utilização no trabalho quotidiano, considerando que será importante para o futuro do design. Aqueles que a utilizam fazem-no durante as verificações de qualidade e na análise de insights dos utilizadores, nas últimas fases do processo de design. Contudo, a maioria dos profissionais opta por não a utilizar devido a diversos fatores, nomeadamente o preço alto dos softwares e hardware, o foco internacional e grande dispersão das ferramentas, ou porque acreditam não fornece a componente empática e humana própria do design. Os entrevistados relataram ainda que sabiam muito pouco sobre uma série de outros tópicos, incluindo o estado dos recursos de IA em Portugal e as competências necessárias para operar com a IA no trabalho quotidiano. Por seu lado, os estudantes de design mostraram ter consideravelmente menos noções de IA do que os profissionais. Uma percentagem considerável dos estudantes afirma nunca ter aprendido conteúdos de IA durante os seus estudos de licenciatura. Dos que aprenderam, nem todos consideram que os conhecimentos adquiridos sobre IA podem ser úteis para o seu futuro trabalho como designers. Esta investigação sugere que os designers experientes valorizam mais a IA do que os estudantes, para além de terem mais conhecimentos sobre o tema. Esta investigação representa um passo em frente no estudo da dicotomia IA e design de comunicação em Portugal.Technology has always been synonymous with innovation in the design field, including changes in its projectual practice. Today, Artificial Intelligence (AI) is profoundly changing the way we work and communicate, impacting numerous industries. However, there’s not considerable research on the integration of AI into the field of design. Therefore, the main goal of this dissertation is to understand how AI is perceived and has been applied in the communication design sector in Portugal. In order to achieve this goal, we analyzed the perspective of a group of designers about AI, we tried to understand how Portuguese design companies use AI, and if future designers, today students, are aware and ready to work with AI in their professional practice. The qualitative research method used was the case study. Data was collected from interviews and questionnaires conducted with designers from 10 Portuguese communication design companies. The companies' websites were also analyzed to understand if there was any mention of AI. To try to understand the perceptions of students, a questionnaire was even given to 26 pupils of communication and multimedia design from the University of Beira Interior, University of Algarve and University of Coimbra. The research concluded that designers have basic fundamentals of AI, knowing the main advantages and some disadvantages of it. In general, they express positive feelings about its use in their daily work, considering it to be important for the future of design. Those who use it do so during quality checks and in the analysis of user insights in the last stages of the design process. However, most professionals choose not to use it due to several facts, namely the high price of software and hardware, the international focus and wide dispersion of tools, or because they believe it does not provide the empathetic and human component proper to design. Respondents also reported that they knew very little about several other topics, including the state of AI resources in Portugal and the skills needed to operate with AI in everyday work. On the other hand, design students were shown to have considerably less notions of AI than professionals. A large percentage of the students affirm having never learned AI contents during their undergraduate studies. Those who have learned, not all consider that the knowledge gained about AI can be useful for their future work as designers. This research suggests that experienced designers value AI more than students, in addition to having more knowledge about the topic. This research represents a step forward in the study of the AI and communication design dichotomy in Portugal