arXiv subjects

J. Nathan Matias

Publications and source records attributed to J. Nathan Matias.

9 recordsLinked to original sources

arXiv2025

Measuring the Mental Health of Content Reviewers, a Systematic Review

Artificial intelligence and social computing rely on hundreds of thousands of content reviewers to classify high volumes of harmful and forbidden content. Many workers report long-term, potentially irreversible psychological harm. This work is similar to activities that cause psychological harm to other kinds of helping professionals even after small doses of exposure. Yet researchers struggle to measure the mental health of content reviewers well enough to inform diagnoses, evaluate workplace improvements, hold employers accountable, or advance scientific understanding. This systematic review summarizes psychological measures from other professions and relates them to the experiences of content reviewers. After identifying 1,673 potential papers, we reviewed 143 that validate measures in related occupations. We summarize the uses of psychological measurement for content reviewing, differences between clinical and research measures, and 12 measures that are adaptable to content reviewing. We find serious gaps in measurement validity in regions where content review labor is common. Overall, we argue for reliable measures of content reviewer mental health that match the nature of the work and are culturally-relevant.

cs.CY↗

arXiv2024

Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability

In July 2023, New York City became the first jurisdiction globally to mandate bias audits for commercial algorithmic systems, specifically for automated employment decisions systems (AEDTs) used in hiring and promotion. Local Law 144 (LL 144) requires AEDTs to be independently audited annually for race and gender bias, and the audit report must be publicly posted. Additionally, employers are obligated to post a transparency notice with the job listing. In this study, 155 student investigators recorded 391 employers' compliance with LL 144 and the user experience for prospective job applicants. Among these employers, 18 posted audit reports and 13 posted transparency notices. These rates could potentially be explained by a significant limitation in the accountability mechanisms enacted by LL 144. Since the law grants employers substantial discretion over whether their system is in scope of the law, a null result cannot be said to indicate non-compliance, a condition we call ``null compliance." Employer discretion may also explain our finding that nearly all audits reported an impact factor over 0.8, a rule of thumb often used in employment discrimination cases. We also find that the benefit of LL 144 to ordinary job seekers is limited due to shortcomings in accessibility and usability. Our findings offer important lessons for policy-makers as they consider regulating algorithmic systems, particularly the degree of discretion to grant to regulated parties and the limitations of relying on transparency and end-user accountability.

cs.CY↗

arXiv2021

Software-Supported Audits of Decision-Making Systems: Testing Google and Facebook's Political Advertising Policies

How can society understand and hold accountable complex human and algorithmic decision-making systems whose systematic errors are opaque to the public? These systems routinely make decisions on individual rights and well-being, and on protecting society and the democratic process. Practical and statistical constraints on external audits--such as dimensional complexity--can lead researchers and regulators to miss important sources of error in these complex decision-making systems. In this paper, we design and implement a software-supported approach to audit studies that auto-generates audit materials and coordinates volunteer activity. We implemented this software in the case of political advertising policies enacted by Facebook and Google during the 2018 U.S. election. Guided by this software, a team of volunteers posted 477 auto-generated ads and analyzed the companies' actions, finding systematic errors in how companies enforced policies. We find that software can overcome some common constraints of audit studies, within limitations related to sample size and volunteer capacity.

cs.HC↗

arXiv2021

Adapting Security Warnings to Counter Online Disinformation

Disinformation is proliferating on the internet, and platforms are responding by attaching warnings to content. There is little evidence, however, that these warnings help users identify or avoid disinformation. In this work, we adapt methods and results from the information security warning literature in order to design and evaluate effective disinformation warnings. In an initial laboratory study, we used a simulated search task to examine contextual and interstitial disinformation warning designs. We found that users routinely ignore contextual warnings, but users notice interstitial warnings -- and respond by seeking information from alternative sources. We then conducted a follow-on crowdworker study with eight interstitial warning designs. We confirmed a significant impact on user information-seeking behavior, and we found that a warning's design could effectively inform users or convey a risk of harm. We also found, however, that neither user comprehension nor fear of harm moderated behavioral effects. Our work provides evidence that disinformation warnings can -- when designed well -- help users identify and avoid disinformation. We show a path forward for designing effective warnings, and we contribute repeatable methods for evaluating behavioral effects. We also surface a possible dilemma: disinformation warnings might be able to inform users and guide behavior, but the behavioral effects might result from user experience friction, not informed decision making.

cs.HC↗

arXiv2018

Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus

As researchers use computational methods to study complex social behaviors at scale, the validity of this computational social science depends on the integrity of the data. On July 2, 2015, Jason Baumgartner published a dataset advertised to include ``every publicly available Reddit comment'' which was quickly shared on Bittorrent and the Internet Archive. This data quickly became the basis of many academic papers on topics including machine learning, social behavior, politics, breaking news, and hate speech. We have discovered substantial gaps and limitations in this dataset which may contribute to bias in the findings of that research. In this paper, we document the dataset, substantial missing observations in the dataset, and the risks to research validity from those gaps. In summary, we identify strong risks to research that considers user histories or network analysis, moderate risks to research that compares counts of participation, and lesser risk to machine learning research that avoids making representative claims about behavior and participation on Reddit.

cs.SI↗

arXiv2016

Networks of Gratitude: Structures of Thanks and User Expectations in Workplace Appreciation Systems

Appreciation systems--platforms for users to exchange thanks and praise--are becoming common in the workplace, where employees share appreciation, managers are notified, and aggregate scores are sometimes made visible. Who do people thank on these systems, and what do they expect from each other and their managers? After introducing the design affordances of 13 appreciation systems, we discuss a system we call Gratia, in use at a large multinational company for over four years. Using logs of 422,000 appreciation messages and user surveys, we explore the social dynamics of use and ask if use of the system addresses the recognition problem. We find that while thanks is mostly exchanged among employees at the same level and different parts of the company, addressing the recognition problem, managers do not always act on that recognition in ways that employees expect.

cs.CY↗

arXiv2015

Actually, It's About Ethics in Computational Social Science: A Multi-party Risk-Benefit Framework for Online Community Research

Managers regularly face a complex ethical dilemma over how to best govern online communities by evaluating the effectiveness of different social or technical strategies. What ethical considerations should guide researchers and managers when they employ causal research methods that make different community members bear different risks and benefits, under different levels of consent? We introduce a structural framework for evaluating the flows of risks and benefits in social systems with multiple interacting parties. This framework has implications for understanding the governmentality of managing socio-technical systems, for making research ethics discussions more commensurable, and for enumerating alternative goals researchers might pursue with interventions.

cs.CY↗

arXiv2015

NewsPad: Designing for Collaborative Storytelling in Neighborhoods

This paper introduces design explorations in neighborhood collaborative storytelling. We focus on blogs and citizen journalism, which have been celebrated as a means to meet the reporting needs of small local communities. These bloggers have limited capacity and social media feeds seldom have the context or readability of news stories. We present NewsPad, a content editor that helps communities create structured stories, collaborate in real time, recruit contributors, and syndicate the editing process. We evaluate NewsPad in four pilot deployments and find that the design elicits collaborative story creation.

cs.HC↗

arXiv2015

Reporting, Reviewing, and Responding to Harassment on Twitter

When people experience harassment online, from individual threats or invective to coordinated campaigns of harassment, they have the option to report the harassers and content to the platform where the harassment has occurred. Platforms then evaluate harassment reports against terms of use and other policies to decide whether to remove content or take action against the alleged harasser--or not. On Twitter, harassing accounts can be deleted entirely, suspended (with content made unavailable pending appeal or specific changes), or sent a warning. Some platforms, including Twitter and YouTube, grant authorized reporters or trusted flaggers special privileges to identify and report inappropriate content on behalf of others. In November 2014, Twitter granted Women, Action, and the Media (WAM!) this authorized reporter status. In three weeks, WAM! reviewers assessed 811 incoming reports of harassment and escalated 161 reports to Twitter, ultimately seeing Twitter carry out 70 account suspensions, 18 warnings, and one deleted account. This document presents findings from this three-week project; it draws on both quantitative and qualitative methods. Findings focus on the people reporting and receiving harassment, the kinds of harassment that were reported, Twitter's response to harassment reports, the process of reviewing harassment reports, and challenges for harassment reporting processes.

cs.SI↗