Contributions published at People and Computing Lab (Elaine May Huang)

In the non-profit sharing economy context, an increasing number of resource sharing collectives and organizations (e.g., libraries of things) and peer-to-peer grassroots sharing initiatives leverage underutilized household resources (e.g., tools) to optimize their shared use for the benefit of their local communities. However, a number of social-technical challenges prevent the endurance and growth of such initiatives. Prior research highlighted the specific difficulties related to poor visibility of members’ activities and often high social barriers that hinder interactions among neighbors and strangers. In our prior work, stemming from our continuous engagement with one local sharing community in Switzerland over several years, through fieldwork, interviews, and co-creation studies, we elicited a set of design opportunities to address the emergent community’s challenges. Based on these design considerations, we developed Züri teilt, a mobile application to facilitate resource sharing practices among neighbors aligning with the slow, temporal, and gradual nature of their relationships.

There has been a growing interest in Human-Computer Interaction (HCI), and Computer-Supported Cooperative Work (CSCW) in research on marginalized communities and women’s health and well-being. Important work has been done considering domestic violence (DV), intimate partner violence (IPV), and technologies to address these problems, but little research thus far has looked at the issue of forced marriage. In this paper, we present a study investigating the experiences of individuals affected by forced marriage from various cultures, ethnicities and backgrounds. We also examine the processes and challenges for helping organizations that provide assistance to people in forced marriage situations and explore opportunities for the design of technologies to support individuals affected by forced marriages. Through in-depth interviews and participatory design exercises with people affected by forced marriage and help organization staff members, we offer a rich account of the experiences surrounding forced marriage and identify avenues via which the HCI and CSCW research communities can leverage their expertise to address the problem of forced marriage, potentially contributing to the reduction or elimination of this harmful practice.

This study examined the portrayal of artificial intelligence (AI) in video games and explored the potential correlation between video game exposure and individuals’ level of technological acceptance of AI virtual assistants. A Qualitative Content Analysis (QCA) of several popular video games is conducted to analyze their depiction of AI characters, their roles, and interactions within game narratives. Additionally, this study used a previously validated survey instrument based on the Technological Acceptance Model (TAM) to assess how certain video game playing habits and trust of AI virtual assistants are correlated. We found that portrayal of AI characters in the games analyzed show that AI is often portrayed as humanized and more advanced than its real-world counterpart, and that it is often hostile to humans. The analysis of the survey results found that there is a moderate positive correlation between playing video games featuring AI and willingness to use AI virtual assistant technologies. The findings of this study will contribute to the growing field of AI portrayals in popular media and provide insights into the influence of video games on individuals’ perceptions and acceptance of AI technology.

Since artificial intelligence (AI) has already penetrated large parts of our daily lives and is likely to become even more prominent in the future, it is not surprising that AIs are also increasingly used in movies, books, series, video games, and so on. This work focuses specifically on media for children because, on the one hand, they can be influenced by the media, and on the other hand, there is little known about the depiction of AIs in this media. Still, at the same time, there is a chance to give children a differentiated and critical image of AIs at an early age. For this purpose, a total of 13 media were analyzed using a framework created and mappings were assembled that also included currently used AIs from the industry in order to compare them with each other. Through this analysis, the following categories of AI characters in children's media could be identified: "Side-Kick", "Big Bad Evil", "Virtual Assistant" and "Alternative Human".

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Type 1 diabetes (T1D) is a chronic autoimmune disease that results in the body’s inability to produce the insulin required to maintain healthy blood glucose levels. As T1D commonly develops during childhood, people with T1D require lifelong and intensive self-management to avoid life-threatening health complications. Self-management of T1D is a complex process that involves multiple interdependent factors, frequent anticipation of future blood glucose changes and complex decision-making processes. Recent advances in technology, such as continuous blood glucose monitors or hybrid closed-loop systems have facilitated this process. Nonetheless, self-management remains time-intensive and poses a high burden. Recent approaches use machine-learning-based algorithms to generate blood glucose predictions to facilitate the complex decision-making processes involved in self-management. However, there is a lack of research addressing the individualized and human-centered needs and expectations of people with T1D regarding blood glucose predictions. In the following thesis, we set out to capture and analyze the lived experience of people with T1D including their needs and expectations of apps supporting blood glucose predictions. To this end we designed and developed a prototype MOON-T1D supporting blood glucose predictions, on the basis of a systematic literature review. Subsequently, we conducted an Experience Sampling Method study coupled with semi-structured interviews with three individuals with T1D who used MOON-T1D over the course of five days. Finally, we used three case-studies and conducted a reflexive thematic analysis, which resulted in four distinct themes, to report on design opportunities and challenges of blood glucose predictions to effectively and individually support people with T1D.

The rapid development of the “sharing economy” enables the effective and efficient coordination, acquisition, distribution, and sharing of many kinds of different resources. Beyond the well-known sharing economy services such as Airbnb and Uber, an increasing number of local sharing initiatives have established online platforms and services to facilitate access to the shared resources within their local communities. With the automation and complexity of digital tools and platforms, and the specific challenges of online sharing communities, users’ trust and reliance become increasingly critical for successful use and adoption. In our qualitative study in collaboration with two industry partners (a local sharing community and a large infrastructure provider in Switzerland), we unpack various perspectives of interpersonal trust in the community and the systems trust of the supporting technologies. On this basis, we elicited a set of design opportunities for future platforms in the context of sharing economy.

People with visual impairments use assistive technology, e.g., screen readers, to navigate and read PDFs. However, such screen readers need extra information about the logical structure of the PDF, such as the reading order, header levels, and mathematical formulas, described in readable form to navigate the document in a meaningful way. This logical structure can be added to a PDF with tags. Creating tags for a PDF is time-consuming, and requires awareness and expert knowledge. Hence, most PDFs are left untagged, and as a result, they are poorly readable or unreadable for people who rely on screen readers. STEM documents are particularly problematic with their complex document structure and complicated mathematical formulae. These inaccessible PDFs present a major barrier for people with visual impairments wishing to pursue studies or careers in STEM fields, who cannot easily read studies and publications from their field. The goal of this Ph.D. is to apply artificial intelligence for document analysis to reasonably automate the remediation process of PDFs and present a solution for large mathematical formulae accessibility in PDFs. With these new methods, the Ph.D. research aims to lower barriers to creating accessible scientific PDFs, by reducing the time, effort, and expertise necessary to do so, ultimately facilitating greater access to scientific documents for people with visual impairments.

One unsolved sub-task of document analysis is mathematical formula detection (MFD). Research by ourselves and others has shown that existing MFD datasets with inline and display formula labels are small and have insufficient labeling quality. There is therefore an urgent need for datasets with better quality labeling for future research in the MFD field, as they have a high impact on the performance of the models trained on them. We present an advanced labeling pipeline and a new dataset called FormulaNet in this paper. At over 45k pages, we believe that FormulaNet is the largest MFD dataset with inline formula labels. Our experiments demonstrate substantially improved labeling quality for inline and display formulae detection over existing datasets. Additionally, we provide a math formula detection baseline for FormulaNet with an mAP of 0.754. Our dataset is intended to help address the MFD task and may enable the development of new applications, such as making mathematical formulae accessible in PDFs for visually impaired screen reader users.

People with bipolar disorder struggle to maintain stable moods and experience extreme highs, mania, and extreme lows, depression. Mood prediction would be a useful tool to help give warnings about upcoming highs and lows calculated using common mood factors such as previous moods, sleep, and medication compliance. Bipolar Buddy, a start-up company creating a mobile app meant for tracking moods and other factors, aims to achieve just that. They have developed a prediction algorithm that considers mood entries including motivation and anxiety scores, sleep entries, and medication compliance entries to predict one week of mood scores for an individual. However, before this is released to the public it is important to study whether this technology is beneficial and what kinds of impacts it may have on those with mood disorders.This study focused on studying the impact of viewing mood predictions on one’s mood, motivation, anxiety and sleep data. It also looked at the current trust level towards mood predictions and factors contributing to that trust. It was found that mood predictions as they are today in Bipolar Buddy have little to no perceived impact on one’s mood, motivation, anxiety, or sleep levels. It was also found that mood predictions in their current state are not yet fully trusted and would need more data giving the users more accurate predictions in order to build their trust in the technology.

Nowadays, the necessity of being aware of and understanding modern artificial intelligence (AI) spans across age groups. For children, it is especially challenging to recognize its existence and comprehend the effects of modern AI on their lives. We explore the use of an age-appropriate instrument, storytelling, to foster early awareness of modern AI technologies and concepts. For this reason, we have created and iterated upon a set of guidelines aimed at children’s book authors who would like to depict AI in their stories. Using prototype stories created using these guidelines, we solicit input from children and experts from different fields to learn about the benefits and challenges of our approach. Our findings suggest there are advantages to using stories to communicate AI concepts due to their engaging nature. The experts’ feedback also reflects the persistent challenges in defining a conceptually sound and meaningful depiction of modern AI.

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Non-profit approaches in the sharing economy are proliferating in everyday life (e.g., tool libraries, food sharing collectives). They focus on promoting social, economic, and ecological sustainability within local communities. However, prior research uncovered emergent challenges of these communities that prevent their endurance and growth. Despite growing academic interest in studying these communities, designers found it difficult to effectively adopt newly generated knowledge in practice. Addressing the research-practice gap, we developed a translational resource for design practitioners to conceptualize new value-added features for supporting technologies in the sharing economy context. Specifically, we synthesized emergent challenges in non-profit sharing economy communities and key social and environmental sustainability efforts in design research into a familiar format for designers – the card-based toolkit to support generative design activities. Beyond introducing the toolkit, we offer a “how-to” guide for its adoption in collaborative settings.

Over the past decade, more and more business models have been established that rely on re-using and sharing resources within their user community. These sharing economies rely on technological mediation, such as online platforms, to enable access, distribution, and coordination of different types of shared resources. In such collaborative environments, trust was identified to play an important role within the community by prior research. However, obtaining trusting peers as well as trusting the supporting service poses a challenge to effective interactions in sharing economies. In our qualitative study, we employed surveys and follow-up interviews to conceptualize interpersonal and system trust in one Swiss sharing economy community, which recently introduced an impersonal sharing process over smart lockers. Further, we investigate how this new impersonal sharing option is perceived by the members when it comes to the trust relationships within the community. This thesis proposes, based on the gathered findings, a series of design considerations to improve interpersonal and system trust within online sharing economy communities.

Aleksic, Slavisa, Colonna, Liane, Dantas, Carina, Fedosov, Anton, Florez-Revuelta, Francisco, Fosch-Villaronga, Eduard, Jevremovic, Aleksandar, Msakniç, Hajer Gahbiche, Ravi, Siddharth, Rexha, Blerim, & Tamò-Larrieux, Aurelia. (2022).

Speech and language therapy patients need to exercise regularly to improve their condition. To reach enough repetitions, they are required to do many of these at home. There are potential problems with that: the patients need to remember details about the exercises and they do not get feedback if they make mistakes. For exercises to be helpful they have to be done correctly. Furthermore, it can sometimes be hard for patients to find enough motivation at home to even start exercising. To address these problems, LogoHub has been created. It is a website which allows therapists to create exercises, containing instructions in different media formats, and assign them to their patients. Patients can access LogoHub from home and support their exercise with the prepared instructions. During a qualitative study, three speech and language therapy patients were questioned about their exercise practices and two of them used LogoHub for two weeks. Their experiences showed its potential in motivating them to exercise and helping them to do it correctly.

While scalability and growth are key concerns for mainstream, venture-backed digital platforms, local and location-oriented collaborative economies are diverse in their approaches to evolving and achieving social change. Their aims and tactics differ when it comes to broadening their activities across contexts, spreading their concept, or seeking to make a bigger impact by promoting co-operation. This paper draws on three pairs of European, community-centred initiatives which reveal alternative views on scale, growth, and impact. We argue thatproliferation -- a concept that emphasises how something gets started and then travels in perhaps unexpected ways -- offers an alternative toscaling, which we understand as the use of digital networks in a monocultural way to capture an ever-growing number of participants. Considering proliferation is, thus, a way to reorient and enrich discussions on impact, ambitions, modes of organising, and the use of collaborative technologies. In illustrating how these aspects relate inprocesses of proliferation, we offer CSCW an alternative vision of technology use and development that can help us make sense of the impact of sharing and collaborative economies, and design socio-technical infrastructures to support their flourishing.

Ambient assisted living (AAL) technologies are increasingly presented and sold as essential smart additions to daily life and home environments that will radically transform the healthcare and wellness markets of the future. An ethical approach and a thorough understanding of all ethics in surveillance/monitoring architectures are therefore pressing. AAL poses many ethical challenges raising questions that will affect immediate acceptance and long-term usage. Furthermore, ethical issues emerge from social inequalities and their potential exacerbation by AAL, accentuating the existing access gap between high-income countries (HIC) and low and middle-income countries (LMIC). Legal aspects mainly refer to the adherence to existing legal frameworks and cover issues related to product safety, data protection, cybersecurity, intellectual property, and access to data by public, private, and government bodies. Successful privacy-friendly AAL applications are needed, as the pressure to bring Internet of Things (IoT) devices and ones equipped with artificial intelligence (AI) quickly to market cannot overlook the fact that the environments in which AAL will operate are mostly private (e.g., the home). The social issues focus on the impact of AAL technologies before and after their adoption. Future AAL technologies need to consider all aspects of equality such as gender, race, age and social disadvantages and avoid increasing loneliness and isolation among, e.g. older and frail people. Finally, the current power asymmetries between the target and general populations should not be underestimated nor should the discrepant needs and motivations of the target group and those developing and deploying AAL systems. Whilst AAL technologies provide promising solutions for the health and social care challenges, they are not exempt from ethical, legal and social issues (ELSI). A set of ELSI guidelines is needed to integrate these factors at the research and development stage.