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Advances in bias and fairness in inf...

(1998 :)

Advances in bias and fairness in information retrievalsecond International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in bias and fairness in information retrievaledited by Ludovico Boratto ... [et al.].
其他題名:	second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings /
其他題名:	BIAS 2021
其他作者:	Boratto, Ludovico.
團體作者:
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	x, 171 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Information retrievalCongresses.
電子資源:	https://doi.org/10.1007/978-3-030-78818-6
ISBN:	9783030788186$q(electronic bk.)

Advances in bias and fairness in information retrievalsecond International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings /
Advances in bias and fairness in information retrievalsecond International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings /[electronic resource] :BIAS 2021edited by Ludovico Boratto ... [et al.]. - Cham :Springer International Publishing :2021. - x, 171 p. :ill., digital ;24 cm. - Communications in computer and information science,14181865-0929 ;. - Communications in computer and information science;1114..

Towards Fairness-Aware Ranking by Defining Latent Groups Using Inferred Features -- Media Bias Everywhere? A Vision for Dealing with the Manipulation of Public Opinion -- Users' Perception of Search-Engine Biases and Satisfaction -- Preliminary Experiments to Examine the Stability of Bias-Aware Techniques -- Detecting Race and Gender Bias in Visual Representation of AI on Web Search Engines -- Equality of Opportunity in Ranking: A Fair-Distributive Model -- Incentives for Item Duplication under Fair Ranking Policies -- Quantification of the Impact of Popularity Bias in Multi-Stakeholder and Time-Aware Environment -- When is a Recommendation Model Wrong? A Model-Agnostic Tree-Based Approach to Detecting Biases in Recommendations -- Evaluating Video Recommendation Bias on YouTube -- An Information-Theoretic Measure for Enabling Category Exemptions with an Application to Filter Bubbles -- Perception-Aware Bias Detection for Query Suggestions -- Crucial Challenges in Large-Scale Black Box Analyses -- New Performance Metrics for Offline Content-based TV Recommender Systems.

This book constitutes refereed proceedings of the Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, held in April, 2021. Due to the COVID-19 pandemic BIAS 2021 was held virtually. The 11 full papers and 3 short papers were carefully reviewed and selected from 37 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web.

ISBN: 9783030788186$q(electronic bk.)

Standard No.: 10.1007/978-3-030-78818-6doiSubjects--Topical Terms:

384500
Information retrieval
--Congresses.

LC Class. No.: ZA3075 / .I48 2021

Dewey Class. No.: 025.04

Advances in bias and fairness in information retrievalsecond International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings /
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