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Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system (``Brilliant Doctor'') and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as ``a doctor's AI assistant'' to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.
Churches have historically played an important role in Black American communities, catalyzing the pursuit of aims such as social justice, community organization, and health promotion. However, researchers have rarely examined how technology can support an assets-based approach to these efforts, nor the implications of race, traditions, and history when creating such systems. Addressing this gap, we conducted research with two predominantly Black churches to explore health promotion design opportunities. We used photovoice, a research method where participants led their own data collection and analysis. Participants provided nuanced descriptions of the racial and ethnic identities of their communities, and how church history and aspirations for the future impacted these identities. Our findings characterize tensions between tradition and ‘modernization,' implications for technology design, and the need for a temporal approach to understanding communities. We conclude with broader implications for studying the intersection of race and religion in community technology design.
With the surge in literature focusing on the assessment and mitigation of unfair outcomes in algorithms, several open source `fairness toolkits' recently emerged to make such methods widely accessible. However, little studied are the differences in approach and capabilities of existing fairness toolkits, and their fit-for-purpose in commercial contexts. Towards this, this paper identifies the gaps between the existing open source fairness toolkit capabilities and the industry practitioners' needs. Specifically, we undertake a comparative assessment of the strengths and weaknesses of six prominent open source fairness toolkits, and investigate the current landscape and gaps in fairness toolkits through an exploratory focus group, a semi-structured interview, and an anonymous survey of data science / machine learning (ML) practitioners. We identify several gaps between the toolkits' capabilities and practitioner needs, highlighting areas requiring attention and future directions towards tooling that better support "fairness in practice."
Front-line workers in global development are often responsible for data collection and record-keeping about their own work. The authenticity of such data and the role of mid-level supervisors, however, remains understudied. We report on the case of immunization in Pakistan, where, through interviews with 30 mid-level vaccination managers in Punjab district, we find that data falsification by vaccinators is common, though not necessarily rampant. Because of an intricate protocol for record-keeping, supervisors can detect data falsification, and we find they have devised an array of methods, broadly classifiable into four types: triangulation, supplementary data collection, anomaly detection, and interrogation. We also find that the strategies that supervisors use to detect falsification seem linked to their style of management, with authoritarian supervisors preferring supplementary data collection and spot checks, while supportive supervisors use triangulation. Our findings lead to recommendations for designing technologies intended to monitor and manage front-line data.
Recent advances in Artificial Intelligence (AI) suggest that AI applications could transform healthcare delivery in the Global South. However, as researchers and technology companies rush to develop AI applications that aid the health of marginalized communities, it is critical to consider the needs and perceptions of the community health workers (CHWs) who will have to integrate these AI applications into the essential healthcare services they provide to rural communities. We describe a qualitative study examining CHWs' perceptions of an AI application for automated disease diagnosis. Drawing on data from 21 interviews with CHWs in rural India, we characterize (1) CHWs' knowledge, perceptions, and understandings of AI; and (2) the benefits and challenges that CHWs anticipate as AI applications are integrated into their workflows, including their opinions on automation of their work, possible misdiagnosis and errors, data access and surveillance issues, security and privacy challenges, and questions concerning trust. We conclude by discussing the implications of our work for HCI and AI research in low-resource environments.
User-driven intervention tools such as self-tracking help users to self-regulate problematic smartphone usage. These tools basically assume active user engagement, but prior studies warned a lack of user engagement over time. This paper proposes GoldenTime, a mobile app that promotes self-regulated usage behavior via system-driven proactive timeboxing and micro-financial incentives framed as gain or loss for behavioral reinforcement. We conducted a large-scale user study (n = 210) to explore how our proactive timeboxing and micro-financial incentives influence users' smartphone usage behaviors. Our findings show that GoldenTime's timeboxing based micro-financial incentives are effective in self-regulating smartphone usage, and incentive framing has a significant impact on user behavior. We provide practical design guidelines for persuasive technology design related to promoting digital wellbeing.
Persuasive systems are effective at motivating behaviour change using various persuasive strategies. Research shows that tailoring these systems increases their effectiveness. However, there is little knowledge on how PS can be tailored to people’s Stages of Change (SoC). We conduct a large-scale study of 568 participants to investigate how individuals at different SoC respond to various strategies. We also explore why the strategies motivate behaviour change using the ARCS motivation model. Our results show that people’s SoC plays a significant role in the perceived persuasiveness of different strategies and that the strategies motivate for different reasons. For instance, people at the precontemplation stage tend to be strongly motivated by self-monitoring strategy because it raises their consciousness or self-awareness. Our work is the first to link research on the theory of SoC with the theory of motivation and Persuasive Systems Design (PSD) model to develop practical guidelines to inform the tailoring of persuasive systems.
Many 10-14 year olds are at the early stages of using social media, habits they develop on popular platforms can have lasting effects on their socio-emotional wellbeing. We led a remote innovation workshop with 23 middle schoolers on digital wellbeing, identity exploration, and computational concepts related to social computing. This workshop was a unique opportunity to reflect on emergent habits, discuss them with peers, and imagine oneself as an ICT innovator. Resulting themes related to participants’ social wellbeing online included a) sense of belonging to communities of interest, friends, and family, b) self-care and social support strategies involving managing risks, control, and empathy, and c) experimentation while building self-confidence and bravely exploring audience reactions. Participants iteratively designed and tested a sandbox social network website, resulting in Social Sketch. Reflecting on our study, we describe the process for conceptualizing Social Sketch, and challenges in social media innovation with teenagers.
Reflection, a process that organizes information into a structure that incorporates both own and others’ perspectives, was previously believed to function mainly as an antecedent of political knowledge. In this paper, we first design a simple interface nudge to encourage users to reflect on their views on political issues. Second, we use an experimental study to show that reflection works in a way more than leading to political knowledge. Results from a between-subjects online experiment (N = 168) covering one crucial public issue in Singapore (i.e., fertility) showed that (a) reflection interacts with information access to influence perceived issue knowledge; (b) reflection enhances perceived attitude certainty, including perceived attitude clarity and perceived attitude correctness; (c) reflection promotes willingness to express opinions in private settings.
In this paper, we describe and analyze a workshop developed for a work training program called DataWorks. In this workshop, data workers chose a topic of their interest, sourced and processed data on that topic, and used that data to create presentations. Drawing from discourses of data literacy; epistemic agency and lived experience; and critical race theory, we analyze the workshops’ activities and outcomes. Through this analysis, three themes emerge: the tensions between epistemic agency and the context of work, encountering the ordinariness of racism through data work, and understanding the personal as communal and intersectional. Finally, critical race theory also prompts us to consider the very notions of data literacy that undergird our workshop activities. From this analysis, we offer a series of suggestions for approaching designing data literacy activities, taking into account critical race theory.
Women in the global south often seek justice to their online harassment through unveiling the harassers and the screenshots of their sent harassment texts and visual contents before the relevant authorities. Nevertheless, such evidence is often challenged for their authenticity. Our survey (n=91) and interview (n=43) with Bangladeshi online gender harassment victims revealed the depth of the problem, and we set design goals to collect evidence from Facebook Messenger with ensured authenticity. Building on the `shame-based model’ of gender justice \cite{blackwell2017classification}, we designed `Unmochon’, a tool that captures authentic evidence and shares with victims’ intended group. Our user-study (n=48) revealed that diminishing authenticity problem may still leave the victim and online gender justice entangled with mob-sentiment, hegemonic legal consciousness, and several privacy aspects. Our findings open up a new discussion on how HCI-design should address online gender justice in such a complex social setting.
There is growing evidence that digital peer-support networks can have a positive influence on behaviour change and wellbeing outcomes for people who harm themselves and others. However, making and sustaining such networks are subject to ethical and pragmatic challenges, particularly for perpetrators of domestic violence who pose unique risks when brought together. In this work, we report on a ten-month study where we worked with six support workers and eighteen perpetrators in the design and deployment of Fragments of the Past; a socio-material system that connects audio messages with tangible artefacts. We share how crafting digitally-augmented artefacts - ‘fragments’ - of experiences of desisting from violence can translate messages for motivation and rapport between peers, without subjecting the process to risks inherent with direct interpersonal communication. These insights provide the basis for practical considerations for future network design with challenging populations.
This paper investigates the use of immersive virtual reconstructions as an aid for jurors during a courtroom trial. The findings of a between-participant user study on memory and decision-making are presented in the context of viewing a simulated hit-run-death scenario. Participants listened to the opening statement of a prosecutor and a defence attorney before viewing the crime scene in Virtual Reality (VR) or as still images. We compare the effects on cognition and usability of using VR over images presented on a screen. We found several significant improvements, including that VR led to more consistent decision-making among participants. This shows that VR could provide a promising solution for the court to present crime scenes when site visitations are not possible.
Traditional face-to-face health consultation-based systems largely failed to attract teenagers to get reproductive and sexual health supports from doctors and practitioners in Bangladesh as 'sex' or 'adolescent' related issues are considered social taboos and are rarely discussed openly with anyone. This has damaging implications for the physiological and mental well-being of a large group of people. In this paper, we study chatbot's effectiveness to assist adolescents in seeking reproductive and sexual health supports by analyzing the responses from 256 participants, including adolescents and medical personnel from six different regions of Bangladesh. We prototype an interactive chatbot, namely AdolescentBot, and analyzed users' communication patterns, feelings, and contexts of use as the first point of support for getting adolescence related health advice. Our analysis finds that a chatbot can satisfy most of the users' queries, and the majority of the queries are associated with wrong-beliefs. Finally, we discuss ethical and societal issues with chatbot usage and recommend a set of design propositions for the AdolescentBot.