Social Activism C

会議の名前
CHI 2024
Social Justice in HCI: A Systematic Literature Review
要旨

Given the renewed attention on politics, values, and ethics within our field and the wider cultural milieu, now is the time to take stock of social justice research in HCI. We surveyed 124 papers explicitly pursuing social justice between 2009 and 2022 to better reflect on the current state of justice-oriented work within our discipline. We identified (1) how researchers understood the social justice-relevant harms and benefits, (2) the approaches researchers used to address harm, and (3) the tools that researchers leveraged to pursue justice. Our analysis highlights gaps in social justice work, such as the need for our community to conceptualize benefits, and identifies concrete steps the HCI community can take to pursue just futures. By providing a comprehensive overview of and reflection on HCI's current social justice landscape, we seek to help our research community strategize, collaborate, and collectively act toward justice.

著者
Ishita Chordia
University of Washington, Seattle, Washington, United States
Leya Breanna Baltaxe-Admony
University of California Santa Cruz, Santa Cruz, California, United States
Ashley Boone
Georgia Institute of Technology, Atlanta, Georgia, United States
Alyssa Sheehan
Georgia Institute of Technology, Atlanta, Georgia, United States
Lynn Dombrowski
Indiana University, IUPUI, Indianapolis, Indiana, United States
Christopher A. Le Dantec
Northeastern University, Boston, Massachusetts, United States
Kathryn E.. Ringland
University of California, Santa Cruz, Santa Cruz, California, United States
Angela D. R. Smith
University of Texas at Austin, Austin, Texas, United States
論文URL

doi.org/10.1145/3613904.3642704

動画
Addressing Interpersonal Harm in Online Gaming Communities: The Opportunities and Challenges for a Restorative Justice Approach
要旨

Most social media platforms implement content moderation to address interpersonal harms such as harassment. Content moderation relies on offender-centered, punitive approaches, e.g., bans and content removal. We consider an alternative justice framework, restorative justice, which aids victims in healing, supports offenders in repairing the harm, and engages community members in addressing the harm collectively. To assess the utility of restorative justice in addressing online harm, we interviewed 23 users from Overwatch gaming communities, including moderators, victims, and offenders; such communities are particularly susceptible to harm, with nearly three quarters of all online game players suffering from some form of online abuse. We study how the communities currently handle harm cases through the lens of restorative justice and examine their attitudes toward implementing restorative justice processes. Our analysis reveals that cultural, technical, and resource-related obstacles hinder implementation of restorative justice within the existing punitive framework despite online community needs and existing structures to support it. We discuss how current content moderation systems can embed restorative justice goals and practices and overcome these challenges.

著者
Sijia Xiao
University of California, Berkeley, Berkeley, California, United States
Shagun Jhaver
Rutgers University, New Brunswick, New Jersey, United States
Niloufar Salehi
UC, Berkeley, Berkeley, California, United States
動画
A Human-Centered Review of Algorithms in Homelessness Research
要旨

Homelessness is a humanitarian challenge affecting an estimated 1.6 billion people worldwide. In the face of rising homeless populations in developed nations and a strain on social services, government agencies are increasingly adopting data-driven models to determine one’s risk of experiencing homelessness and assigning scarce resources to those in need. We conducted a systematic literature review of 57 papers to understand the evolution of these decision-making algorithms. We investigated trends in computational methods, predictor variables, and target outcomes used to develop the models using a human-centered lens and found that only 9 papers (15.7%) investigated model fairness and bias. We uncovered tensions between explainability and ecological validity wherein predictive risk models (53.4%) unduly focused on reductive explainability while resource allocation models (25.9%) were dependent on unrealistic assumptions and simulated data that are not useful in practice. Further, we discuss research challenges and opportunities for developing human-centered algorithms in this area.

著者
Erina Seh-Young Moon
University of Toronto, Toronto, Ontario, Canada
Shion Guha
University of Toronto, Toronto, Ontario, Canada
論文URL

doi.org/10.1145/3613904.3642392

動画