Aaron Halfaker

schoolWikimedia Foundationbar_chart#1297 Global Rank

Total Papers

HCI Rank

#1297

Research Areas

Years Active

Recent Publications

Wikipedia ORES Explorer: Visualizing Trade-offs For Designing Applications With Machine Learning API

With the growing industry applications of Artificial Intelligence (AI) systems, pre-trained models and APIs have emerged and greatly lowered the barrier of building AI-powered products. However, novice AI application designers often struggle to recognize the inherent algorithmic trade-offs and evaluate model fairness…

CSCW '201st AuthorInterpreting and Explaining AI

ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia

Algorithmic systems---from rule-based bots to machine learning classifiers---have a long history of supporting the essential work of content moderation and other curation work in peer production projects. From counter-vandalism to task routing, basic machine prediction has allowed open knowledge projects like Wikiped…

CHI '204th AuthorHonorable MentionContent Moderation & Platform Governance +3

Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems

On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions. However, algorithms can fail to solve the problems they were designed for if they conflict with the values of communities who use them. In this study, we take a Value-Sensitive Algorithm Design approach t…

CHI '182nd AuthorGeospatial & Map Visualization +1

Distance and Attraction: Gravity Models for Geographic Content Production

Volunteered Geographic Information (VGI), such as contributions to OpenStreetMap and geotagged Wikipedia articles, is often assumed to be produced locally. However, recent work has found that peer-produced VGI is frequently contributed by non-locals. , in which contributions flow between regions based on distance, pr…

CSCW '18CorrespondingBots and Online Communities

Bot Detection in Wikidata Using Behavioral and Other Informal Cues

Bots have been important to peer production’s success. Wikipedia, OpenStreetMap, and Wikidata all have taken advantage of automation to perform work at a rate and scale exceeding that of human contributors. Understanding the ways in which humans and bots behave in these communities is an important topic, and one that…

CSCW '18CorrespondingBots and Online Communities

Operationalizing conflict and cooperation between automated software agents in Wikipedia: A replication and expansion of ‘Even Good Bots Fight’

This paper replicates, extends, and refutes conclusions made in a study published in PLoS ONE ("Even Good Bots Fight"), which claimed to identify substantial levels of conflict between automated software agents (or bots) in Wikipedia using purely quantitative methods. By applying an integrative mixed-methods approach…

CSCW '183rd AuthorWikipedia

Value-Sensitive Algorithm Design: Method, Case Study, and Lessons

Most commonly used approaches to developing automated or artificially intelligent algorithmic systems are Big Data-driven and machine learning-based. However, these approaches can fail, for two notable reasons: (1) they may lack critical engagement with users and other stakeholders; (2) they rely largely on historical…

CSCW '184th AuthorFairness and Bias

Simulation Experiments on (the Absence of) Ratings Bias in Reputation Systems

As the gig economy continues to grow and freelance work moves online, five-star reputation systems are becoming more and more common. At the same time, there are increasing accounts of race and gender bias in evaluations of gig workers, with negative impacts for those workers. We report on a series of four Mechanical…

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