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008			210624s2021 sz | o |||| 0|eng d
020			_a9783030788186
024	7		_a10.1007/978-3-030-78818-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aZA3075 _b2021 EB
245	1	0	_aAdvances in Bias and Fairness in Information Retrieval _bSecond International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021, Proceedings _cedited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (X, 171 páginas) _b40 ilustraciones, 34 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aCommunications in Computer and Information Science _x1865-0937 _v1418
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_aTowards 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.
520	3		_aThis 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. .
988			_aSpringer_Computer_2021
650		7	_2embne _aSistemas de información _9147360
700	1		_aBoratto, Ludovico _eeditor literario _0(orcid)0000-0002-6053-3015 _1https://orcid.org/0000-0002-6053-3015 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFaralli, Stefano _eeditor literario _0(orcid)0000-0003-3684-8815 _1https://orcid.org/0000-0003-3684-8815 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMarras, Mirko _eeditor literario _0(orcid)0000-0003-1989-6057 _1https://orcid.org/0000-0003-1989-6057 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aStilo, Giovanni _eeditor literario _0(orcid)0000-0002-2092-0213 _1https://orcid.org/0000-0002-2092-0213 _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030788179
776	0	8	_iPrinted edition: _z9783030788193
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-78818-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _eh _zSI