mlsanityData to 14 Oct 2026

On Measuring Bias in Online Information

arXiv 1704.05730 ↗First posted 19 Apr 2017cs.DB cs.CYcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Apr 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1; 2025: 3; 2026: 1.

8 papers.