Communications of the ACM
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From Communications of the ACM
Technical Perspective: The Impact of Auditing for Algorithmic Bias
"Actionable Auditing Revisited," by Inioluwa Deborah Raji and Joy Buolamwini, examines how companies producing commercial facial classification software responded...
From Communications of the ACM
Actionable Auditing Revisited: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products
This paper investigates the commercial impact of Gender Shades, the first algorithmic audit of gender- and skin-type performance disparities in commercial facial...
From Communications of the ACM
Sampling Near Neighbors in Search for Fairness
We propose several efficient data structures for the exact and approximate variants of the fair near neighbor problem.
From Communications of the ACM
Technical Perspective: Can Data Structures Treat Us Fairly?
In "Sampling Near Neighbors in Search for Fairness," Aumüller et al. investigate a basic problem in similarity search called near neighbor in the context of fair...
From Communications of the ACM
Technical Perspective: Visualization Search: From Sketching to Natural Language
"Expressive Querying for Accelerating Visual Analytics," by Tarique Siddiqui et al., provides a general abstraction, along with advanced interfaces, focusing on...
From Communications of the ACM
Expressive Querying for Accelerating Visual Analytics
In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching.
From Communications of the ACM
Technical Perspective: Evaluating Sampled Metrics Is Challenging
"On Sampled Metrics for Item Recommendation," by Walid Krichene and Steffen Rendle, exposes a crucial aspect for the evaluation of algorithms and tools: the impact...
From Communications of the ACM
On Sampled Metrics for Item Recommendation
This paper investigates sampled metrics and shows that it is possible to improve the quality of sampled metrics by applying a correction, obtained by minimizing...
From Communications of the ACM
Technical Perspective: Leveraging Social Context for Fake News Detection
In "FANG," the authors focus on a strategy of automatically detecting disinformation campaigns on online media with a new graph-based, contextual technique for...
From Communications of the ACM
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection.
From Communications of the ACM
Technical Perspective: Exploring Cognitive Bias 'In the Wild'
The authors of "Cognitive Biases in Software Development" rightly highlight the need for situated studies that examine cognitive bias 'in the wild' during software...
From Communications of the ACM