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Technical Perspective: The Impact of Auditing for Algorithmic Bias
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...

Actionable Auditing Revisited
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...

Sampling Near Neighbors in Search for Fairness
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.

Technical Perspective: Can Data Structures Treat Us Fairly?
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...

Technical Perspective: Visualization Search: From Sketching to Natural Language
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...

Expressive Querying for Accelerating Visual Analytics
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.

Technical Perspective: Evaluating Sampled Metrics Is Challenging
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...

On Sampled Metrics for Item Recommendation
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...

Technical Perspective: Leveraging Social Context for Fake News Detection
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...

FANG
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.

Technical Perspective: Exploring Cognitive Bias 'In the Wild'
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...

Cognitive Biases in Software Development
From Communications of the ACM

Cognitive Biases in Software Development

We conducted a two-part field study to examine the extent to which cognitive biases occur, the consequences of these biases on developer behavior, and the practices...
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