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

THE PARTICIPATION OF UX DESIGNERS IN ARTIFICIAL INTELLIGENCE PROJECTS: RECOMMENDER SYSTEMS

Com o avanço tecnológico, a inteligência artificial ganhou protagonismo e cada vez mais encontramos produtos inovadores no mercado, trazendo-a como diferencial. Um exemplo são sistemas de recomendação baseados em aprendizado de máquina, que filtram o conteúdo de maneira personalizada para cada usuário, poupando tempo e esforço cognitivo do usuário. Como toda novidade, o mercado profissional ainda está em amadurecimento e os usuários estão aprendendo a interagir com as interfaces. A atuação do UX designer torna-se relevante para garantir uma boa experiência de uso. Por isso, é importante investigar o mercado para mapear o envolvimento do UX designer no desenvolvimento de produtos que utilizem sistema de recomendação. Com esse propósito, esta pesquisa envolveu profissionais de mercado em entrevistas, para entender o envolvimento do UX Designer no processo de desenvolvimento de sistemas de recomendação baseados em aprendizado de máquina e identificou diversas questões relevantes que apontam para a necessidade de dar foco aos fatores humanos. Também indica a capacitação dos profissionais, a geração de conteúdo no tema de AI/ML voltado para designers e trabalhar a cultura das empresas.With technological advances, artificial intelligence has gained prominence and we increasingly find innovative products on the market, bringing it as a differential. One example is machine learning-based recommender systems, which filter content in a personalized way for each user, saving the user's time and cognitive effort. Like any novelty, the professional market is still maturing, and users are learning to interact with interfaces. The performance of a UX designer becomes relevant to ensure a good user experience. Therefore, it is important to investigate the market to map the participation of the UX designer in the development of products that use a recommendation system. For this purpose, this research involved market professionals in interviews to understand the UX Designer's participation in developing recommendation systems based on machine learning and identified several relevant issues that point to the need to focus on human factors. It also indicates the training of professionals, the generation of content on the theme of AI/ML aimed at designers and working on the culture of companies

Evaluating the Effect of Feedback from Different Computer Vision Processing Stages: A Comparative Lab Study

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applicationstargeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our fndings indicate that the stage of processing from which feedback is derived plays an important role in users’ ability to develop coherent and correct understandings of a system’s operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms

Data as a design material: An analysis on the challenges of working with “big data” related technologies in an industrial context

In recent years, the ability to collect, store and analyse large datasets by private companies and government agencies has increased to the point where the term “big data” has been coined to describe the phenomena. Alongside “big data”, several data processing technologies are becoming more widespread due to their effectiveness and success in everyday products and services; these are artificial intelligence, with its subsets machine learning and deep learning, and data analytics amongst others. This study investigated the challenges designers face when working with new information and communication technologies in an industrial context. More specifically, it deals with “big data” and new data processing technologies and how designers engage with them as a design material when envisioning new products and services. The research questions were (1) what challenges are designers facing when working with “big data” in a data-rich industrial context? (2) how is working with “big data” and new data collecting and processing technologies different from other design materials? (3) how can designers overcome some of the challenges of working with data? This thesis adopted a research through design approach and data was collected between June 2015 and January 2016. Furthermore, a review of the material-centred design literature was used as a theoretical framework. To answer the research questions, this thesis investigated a six-month design project done for the energy company Vattenfall. Vattenfall was at the time going through a digitalisation phase and was interested in evaluating the possibility of combining their internal data with other data sources to explore new products and services. During the six-month period, I worked in Vattenfall’s Helsinki offices, designing different concepts under the supervision of the product development team and their programme manager as my direct supervisor. Data was gathered using different qualitative methods and focusing on three areas: the design practice, the design outcomes, and the interactions with the team and stakeholders. The key findings demonstrate how the practice of design in this new technological landscape faces multiple challenges. The main challenges being (a) the high level of complexity of these technologies, (b) the lack of education/experience of the designer to work in this context, (c) the lack of competence in the organization and (d) the missing frameworks and tools for collaboration between data experts and designers. Furthermore, it was also found and validated against the literature that these new technologies present different properties not comparable with previously well-studied ones like haptics, Bluetooth and RFID. Making existing frameworks and traditional approaches to exploring new digital materials hard to replicate. The results further suggest the need for developing novel concepts and frameworks to support new ways of understanding, describing and working with “big data” and its related technologies

A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VISis needed. In this paper, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?" This survey reveals seven main processes where the employment of ML techniques can benefit visualizations:Data Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table

User experience in cross-cultural contexts

This dissertation discusses how interdisciplinary UX teams can consider culturally sensitive design elements during the UX design process. It contributes a state-of-the-art meta review on UX evaluation methods, two software tool artifacts for cross-functional UX teams, and empirical insights in the differing usage behaviors of a website plug-in of French, German and Italian users, website design preferences of Vietnamese and German users, as well as learnings from a field trip that focused on studying privacy and personalization in Mumbai, India. Finally, based on these empirical insights, this work introduces the concept culturally sensitive design that goes beyond traditional cross-cultural design considerations in HCI that do not compare different approaches to consider culturally sensitive product aspects in user research

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

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1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen687. GESTIONE, PRODUZIONE E DESIGNnoopenArcoraci, Andre