Saiph Savage

Botivist: Calling Volunteers to Action using Online Bots

To help activists call new volunteers to action, we present Botivist: a platform that uses Twitter bots to find potential volunteers and request contributions. By leveraging different Twitter accounts, Botivist employs different strategies to encourage participation. We explore how people respond to bots calling them to action using a test case about corruption in Latin America. Our results show that the majority of volunteers (80\%) who responded to Botivists calls to action contributed relevant proposals to address the assigned social problem. Different strategies produced differences in the quantity and relevance of contributions. Some strategies that work well offline and face-to-face appeared to hinder peoples participation when used by an online bot. We analyze user behavior in response to being approached by bots with an activist purpose. We also provide strong evidence for the value of this type of civic media, and derive design implications.

Daemo: A Self-Governed Crowdsourcing Marketplace

Crowdsourcing marketplaces provide opportunities for autonomous and collaborative professional work as well as social engagement. However, in these marketplaces, workers feel disrespected due to unreasonable rejections and low payments, whereas requesters do not trust the results they receive. The lack of trust and uneven distribution of power among workers and requesters have raised serious concerns about sustainability of these marketplaces. To address the challenges of trust and power, this paper introduces Daemo, a self-governed crowdsourcing marketplace. We propose a prototype task to improve the work quality and open-governance model to achieve equitable representation. We envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.

Subcontracting Microwork

Mainstream crowdwork platforms treat microtasks as indivisible units; however, in this article, we propose that there is value in re-examining this assumption. We argue that crowdwork platforms can improve their value proposition for all stakeholders by supporting subcontracting within microtasks. After describing the value proposition of subcontracting, we then define three models for microtask subcontracting: real-time assistance, task management, and task improvement, and reflect on potential use cases and implementation considerations associated with each. Finally, we describe the outcome of two tasks on Mechanical Turk meant to simulate aspects of subcontracting. We reflect on the implications of these findings for the design of future crowd work platforms that effectively harness the potential of subcontracting workflows.

Automated Assistants to Identify and Prompt Action on Visual News Bias

Bias is a common problem in todays media, appearing frequently in text and in visual imagery. Users on social media websites such as Twitter need better methods for identifying bias. Additionally, activists --those who are motivated to effect change related to some topic, need better methods to identify and counteract bias that is contrary to their mission. With both of these use cases in mind, in this paper we propose a novel tool called UnbiasedCrowd that supports identification of, and action on bias in visual news media. In particular, it addresses the following key challenges (1) identification of bias; (2) aggregation and presentation of evidence to users; (3) enabling activists to inform the public of bias and take action by engaging people in conversation with bots. We describe a preliminary study on the Twitter platform that explores the impressions that activists had of our tool, and how people reacted and engaged with online bots that exposed visual bias. We conclude by discussing design and implication of our findings for creating future systems to identify and counteract the effects of news bias.

Bring on Board New Enthusiasts! A Case Study of Impact of Wikipedia Art + Feminism Edit-A-Thon Events on Newcomers

Success of online production communities such as Wikipedia highly relies on a continuous stream of newcomers to replace the inevitable high turnover and to bring on board new sources of ideas and labor. However, these communities have been struggling with attracting newcomers, especially from a diverse population of users. In this work, we conducted a case study on how organizing an offline co-located event over a short period of time contributes to involving newcomers in the online community. We present results of our multiple-source quantitative analysis of Wikipedia Art+Feminism edit-a-thon as a case of such events. The results of our analysis shows that such offline events are successful in attracting a large number of newcomers; however, retention of the newcomers stays as a challenge.

Tag Me Maybe: Perceptions of Public Targeted Sharing on Facebook

Social network sites allow users to publicly tag people in their posts. These tagged posts allow users to share to both the general public and a targeted audience, dynamically assembled via notifications that alert the people mentioned. We investigate peoples perceptions of this mixed sharing mode through a qualitative study with 120 participants. We found that individuals like this sharing modality as they believe it strengthens their relationships. Individuals also report using tags to have more control of Facebooks ranking algorithm, and to expose one another to novel information and people. This work helps us understand peoples complex relationships with the algorithms that mediate their interactions with each another. We conclude by discussing the design implications of these findings.

Crowdsourcing the Installation and Maintenance of Indoor Localization Infrastructure to Support Blind Navigation

Indoor navigation systems can make unfamiliar buildings more accessible for people with vision impairments, but their adoption is hampered by the effort of installing infrastructure and maintaining it over time. Most solutions in this space require augmenting the environment with add-ons, such as Bluetooth beacons. Installing and calibrating such infrastructure requires time and expertise. Once installed, localization accuracy often degrades over time as batteries die, beacons go missing, or otherwise stop working. Even localization systems installed by experts can become unreliable weeks, months, or years after the installation. To address this problem, we created LuzDeploy: a physical crowdsourcing system that organizes non-experts for the installation and long-term maintenance of a Bluetooth-based navigation system. LuzDeploy simplifies the tasks required to install and maintain the localization infrastructure, thus making a crowdsourcing approach feasible for non-experts. We report on a field deployment where 127 participants installed and maintained a blind navigation system over several months in a 7-story building, completing 455 tasks in total. We compare the accuracy of the system installed by participants to an installation completed by experts with specialized equipment. LuzDeploy aims to improve the sustainability of indoor navigation systems to encourage widespread adoption outside of research settings.

CHI-nnabis: Implications of Marijuana Legalization for and from Human-Computer Interaction

The consumption of cannabis has substantial implications for medicine, popular culture, and technology use, yet discussion of it is almost entirely absent in the HCI literature. Taking advantage of CHI 2017s location in one of the first jurisdictions to legalize recreational use of marijuana in the U.S., this panel will discuss its socio-technical implications, identify HCI research themes relevant to policy and public health debates, and outline a research agenda.

LuzDeploy: A Collective Action System for Installing Navigation Infrastructure for Blind People

Providing navigation assistance to people with visual impairments often requires augmenting the environment with after-market technology. However, installing navigation infrastructure in large environments requires a critical mass of trained personnel. Recruiting, training and managing participants for such a task is difficult. LuzDeploy is a computational method to recruit, instruct and orchestrate volunteers to perform physical crowdsourcing tasks. We use LuzDeploy to orchestrate volunteers to install physical infrastructure for the navigation assistance of people with visual impairments. Our system provides on-the-go enrollment so that volunteers can participate to the collective action whenever they have time, coming and leaving as needed. Providing automated instructions also allows to avoid instructing participants directly, so experts do not need to be available on-site.

Innovations in autonomous systems: Challenges and opportunities for human-agent collaboration

Autonomous agents are rapidly becoming collaborative partners in addressing diverse industry and social problem domains. With this shift brings a number of challenges and opportunities in understanding and designing for the dynamics involved in human and agents working collectively. This session will facilitate a wider discussion on the current socio-technical challenges for collaborative agent systems and human-agent interaction across a range of disciplines, and how we move forward to meet those challenges.