Announcing the WAIM Doctoral Research Fellows!

The  is delighted to announce the five WAIM Doctoral Research Fellows for the 2021-22 academic year. The Fellows were selected from a highly competitive pool of applicants, which was systematically reviewed by an interdisciplinary panel of judges. Each Fellow will be supported with a $50,000 stipend to support the advancement of their dissertation research. Over the coming year, Fellows will participate as key actors in the WAIM network by sharing their progress at a series of public talks and as part of the final WAIM Convergence Conference in the summer of 2022.

, University of Texas Austin

Connecting artificial intelligence literacy and human-AI decision making outcomes in organizational hiring

Artificial intelligence (AI)-based technologies are quickly becoming a part of many decision-making processes across industries. To further explore human-AI decision-making, this dissertation will explore the connection between decision makers’ AI literacy and their use of AI-based recommendations to assess job applicants. The primary goals of this two-phase dissertation research are to 1) develop a scale measure for perceptions and understanding of AI, or AI literacy, and 2) test the scale within a hiring scenario experiment. In addressing these goals, the research will examine whether people’s AI literacy impacts their evaluation of job applicants when working with an AI-based hiring tool.

, Georgia Institute of Technology

Towards equitable futures in frontline health: Design of intelligent systems for supporting (gendered) care work in resource-constrained settings

This research aims to inform the design of data-driven and automated systems in frontline health, particularly for women workers in low-level and precarious roles in the Global South. Frontline health workers are increasingly using such systems, or collecting data for intelligent systems in their everyday work. Taking a participatory approach, the researcher will investigate how data-driven systems might be designed to support workers by selectively automating routine workflows and enabling better distribution of work. In addition to contributing towards eventual improved health outcomes for care-seekers from underserved communities, this research targets improved futures for health workers and robust healthcare ecologies overall.

, Carnegie-Mellon University

(Co-)Designing a More Inclusive Future of Work

This research focuses on (co-)designing a more inclusive future of work for minority workers from diverse genders, ethnic and racial identities, and socio-economic statuses. To do so, the researcher creates peer-to-peer support systems which leverage both human and machine intelligence to scaffold skill, identity, and career development among individuals engaged in non-traditional, alternative working arrangements. Overall, the dissertation research aims to inform new design methods for an inclusive future of work for vulnerable workers and contributes two open-source software systems which introduce novel algorithms and interaction design paradigms to support human-machine collaboration.

, University of California Santa Barbara

Anticipating intelligent technologies: Nonlinear dynamics in the digital transformation of essential water infrastructure

This dissertation research is a multi-site ethnography of digital transformation in water infrastructure. As many water agencies across the United States are adopting similarly complex sets of intelligent, autonomous digital technologies, many different actors collaborate during the leadup to implementation. The researcher is most interested in how the anticipation of the future affects work, technology and organizing long before the effects of digital transformation become apparent. In addition to ethnographic data, the project also includes social network data to track how patterns of interaction and advice seeking shift in the early stages of selecting and adopting new digital technologies.

, University of Texas Dallas

How to save a life: Design and implementation of a data-driven alert system for early detection of sepsis

This research focuses on the design and implementation of a data-driven clinical decision support tool in the form of an automated alert system for a prevalent, deadly, and costly condition: sepsis. The designed alert system simultaneously considers two aspects that together determine the efficacy of alerts in practice: (i) personalized prediction of sepsis risk for each patient, (ii) timely response of alert users---caregivers---in following care protocols, i.e., caregivers’ compliance behavior. The findings of this research provide guidelines for the development of effective alert systems that can ultimately improve the quality of care in various healthcare settings.