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Algorithmic Decision Making and the Cost of Fairness
TLDR
This work reformulate algorithmic fairness as constrained optimization: the objective is to maximize public safety while satisfying formal fairness constraints designed to reduce racial disparities, and also to human decision makers carrying out structured decision rules.
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
TLDR
It is argued that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce, rather than requiring that algorithms satisfy popular mathematical formalizations of fairness.
The Structural Virality of Online Diffusion
TLDR
This work proposes a formal measure of what it label “structural virality” that interpolates between two conceptual extremes: content that gains its popularity through a single, large broadcast and that which grows through multiple generations with any one individual directly responsible for only a fraction of the total adoption.
Filter Bubbles, Echo Chambers, and Online News Consumption
Online publishing, social networks, and web search have dramatically lowered the costs of producing, distributing, and discovering news articles. Some scholars argue that such technological changes
The structure of online diffusion networks
TLDR
This work describes the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing, and finds strikingly similar patterns across all domains.
Assessing respondent-driven sampling
TLDR
Investigating the performance of RDS by simulating sampling from 85 known, network populations finds that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow.
Predicting consumer behavior with Web search
TLDR
This work uses search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes.
Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machine learning and
Respondent‐driven sampling as Markov chain Monte Carlo
TLDR
This paper presents RDS as Markov chain Monte Carlo importance sampling, and examines the effects of community structure and the recruitment procedure on the variance of RDS estimates, showing that variance is inflated by a common design feature in which the sample members are encouraged to recruit multiple future sample members.
Racial disparities in automated speech recognition
TLDR
Analysis of a large corpus of sociolinguistic interviews with white and African American speakers demonstrates large racial disparities in the performance of five popular commercial ASR systems, and proposes strategies to reduce these performance differences and ensure speech recognition technology is inclusive.
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