R.I.P.
π»
Ghosted
On Measuring Bias in Online Information
April 19, 2017 Β· Declared Dead Β· π SGMD
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Evaggelia Pitoura, Panayiotis Tsaparas, Giorgos Flouris, Irini Fundulaki, Panagiotis Papadakos, Serge Abiteboul, Gerhard Weikum
arXiv ID
1704.05730
Category
cs.DB: Databases
Cross-listed
cs.CY
Citations
58
Venue
SGMD
Last Checked
5 months ago
Abstract
Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Databases
R.I.P.
π»
Ghosted
Untangling Blockchain: A Data Processing View of Blockchain Systems
R.I.P.
π»
Ghosted
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
R.I.P.
π»
Ghosted
BLOCKBENCH: A Framework for Analyzing Private Blockchains
R.I.P.
π»
Ghosted
Data Synthesis based on Generative Adversarial Networks
R.I.P.
π»
Ghosted
HoloClean: Holistic Data Repairs with Probabilistic Inference
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted