Exploring Social Justice, Design,and HCI

The aim of this one-day workshop is to share existing research, discuss common practices, and to develop new strategies and tools for designing for social justice in HCI. This workshop will bring together a set of HCI scholars, designers, and community members to discuss social justice perspectives on interaction design and technology. We will explore theoretical and methodological approaches in and around HCI that can help us generatively consider issues of power, privilege, and access in their complexity. We will discuss the challenges associated with taking a justice approach in HCI, looking toward existing practices we find both productive and problematic. This workshop will bridge current gaps in research and practice by developing concrete strategies for both designing and evaluating social change oriented work in HCI, where agendas are made clear and researchers are held accountable for the outcomes of their work by members of their field site and the research community

Qualitative data collection technologies: A comparison of instant messaging, email, and phone.

ABSTRACT With the growing body of qualitative research on HCI and social computing, it is natural that researchers may choose to conduct that research in a mediated fashion-over telephone or computer networks. In this paper we compare three different qualitative data collection technologies: phone, instant message (IM), and email. We use quantitative analysis techniques to examine the differences between the methods specifically concerning word count and qualitative codes. We find that there are differences between the methods, and that each technology has affordances that impact the data. Although phone interviews contain four times as many words on average as email and IM, we were surprised to discover that there is no significant difference in number of unique qualitative codes expressed between phone and IM

From Creating Spaces for Civic Discourse to Creating Resources for Action

In this paper, we investigate the role of technology to address the concerns of a civil society group carrying out community-level consultation on the allocation of £1 million of community funds. We explore issues of devolved decision-making through the evaluation of a sociodigital system designed to foster deliberative virtues. We describe the ways in which this group used our system in their consultation practices. Our findings highlight how they adopted our technology to privilege specific forms of expression, ascertain issues in their community, make use of and make sense of community data, and create resources for action within their existing practices. Based on related fieldwork we discuss the impacts of structuring and configuring tools for ‘talk-based’ consultation in order to turn attention to the potential pitfalls and prospects for designing civic technologies that create resources for action for civil society

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden

Feminist HCI for real: designing technology in support of a social movement

How are technologies are designed and used tactically by activists? As the HCI community starts to contend with social inequalities, there has been debate about how HCI researchers should address approach this type of research. However, there is little research examining practitioners such as social justice activists who confront social problems, and are using technology, such as mobile phones, blogging, and social media to do so. In this dissertation, I build on this knowledge within the context of a social movement organization working to stop street harassment (harassment towards women and minorities in public) called Hollaback (ihollaback.org). I position myself as an action researcher doing research and building technologies such as mobile apps and a blogging platform to collect stories of harassment and to support activists. The organization has collected over 3000 stories and represents 50 different locales in 17 countries. Through a series of studies, I examined how technology impacts the organization, activists, and those who contribute stories of harassment. I found evidence that the storytelling platform helps participants fundamentally shift their cognitive and emotional orientation towards their experience and informs what activists do on the ground. My results suggest that doing activism using technology can help remove some barriers to participation but can also lower expectations for the amount of work required. I also looked at how different social media tactics can increase the number of followers and how traditional media plays a role in these tactics. My work contributes theoretically to the HCI community by building on social movement theory, feminist HCI, and action research methodology. My investigation also sheds light empirically on how technology plays a role in a social movement organization, and how it impacts those who participate.PhDCommittee Chair: Bruckman, Amy; Committee Member: Bardzell, Shaowen; Committee Member: Bauschspies, Wenda; Committee Member: Gilbert, Eric; Committee Member: Mynatt, Elizabet

Learning from the Crowd: Observational Learning in Crowdsourcing Communities

Crowd work provides solutions to complex problems effectively, efficiently, and at low cost. Previous research showed that feedback, particularly correctness feedback can help crowd workers improve their performance; yet such feedback, particularly when generated by experts, is costly and difficult to scale. In our research we investigate approaches to facilitating continuous observational learning in crowdsourcing communities. In a study conducted with workers on Amazon Mechanical Turk, we asked workers to complete a set of tasks identifying nutritional composition of different meals. We examined workers’ accuracy gains after being exposed to expert-generated feedback and to two types of peer-generated feedback: direct accuracy assessment with explanations of errors, and a comparison with solutions generated by other workers. The study further confirmed that expert-generated feedback is a powerful mechanism for facilitating learning and leads to significant gains in accuracy. However, the study also showed that comparing one’s own solutions with a variety of solutions suggested by others and their comparative frequencies leads to significant gains in accuracy. This solution is particularly attractive because of its low cost, minimal impact on time and cost of job completion, and high potential for adoption by a variety of crowdsourcing platforms.Engineering and Applied Science