Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Preethi Lahoti"'
Publikováno v:
Machine Learning
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces the Risk Advisor, a novel post-hoc meta-learner for estimating failure risks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89365a57fe4604f0d535294decb1fb93
https://doi.org/10.1007/s10994-022-06248-y
https://doi.org/10.1007/s10994-022-06248-y
Autor:
Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D. Ekstrand, Adam Roegiest, Aldo Lipani, Alex Beutel, Ana Lucic, Ana-Andreea Stoica, Anubrata Das, Asia Biega, Bart Voorn, Claudia Hauff, Damiano Spina, David Lewis, Douglas W. Oard, Emine Yilmaz, Faegheh Hasibi, Gabriella Kazai, Graham McDonald, Hinda Haned, Iadh Ounis, Ilse van der Linden, Joris Baan, Kamuela N. Lau, Krisztian Balog, Mahmoud Sayed, Maria Panteli, Mark Sanderson, Matthew Lease, Preethi Lahoti, Toshihiro Kamishima
Publikováno v:
ACM SIGIR Forum. 53:20-43
The purpose of the SIGIR 2019 workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety (FACTS-IR) was to explore challenges in responsible information retrieval system development and deployment. To this end, the workshop aimed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fffe7feba93e2348b6198f2e06f91d11
https://doi.org/10.1145/3458553.3458556
https://doi.org/10.1145/3458553.3458556
Publikováno v:
AIES '21
AIES
AIES
Traditional approaches to ensure group fairness in algorithmic decision making aim to equalize ``total'' error rates for different subgroups in the population. In contrast, we argue that the fairness approaches should instead focus only on equalizing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b789d1bdb47bdcd88999588d280b747d
http://arxiv.org/abs/2105.04249
http://arxiv.org/abs/2105.04249
Publikováno v:
ICDE 2019
ICDE
2019 IEEE 35th International Conference on Data Engineering (ICDE)
ICDE
2019 IEEE 35th International Conference on Data Engineering (ICDE)
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::555539d08b4253c76bca830dba8b3d24
https://hdl.handle.net/21.11116/0000-0009-CE2C-F21.11116/0000-0005-F745-7
https://hdl.handle.net/21.11116/0000-0009-CE2C-F21.11116/0000-0005-F745-7
Publikováno v:
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
WSDM
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining-WSDM 18
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining -WSDM '18
WSDM
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining-WSDM 18
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining -WSDM '18
People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption promotes content that confirms the users' existing point of view. This phenomenon has led to polarizat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4f3551aa5c836136934e478bb9d582c
https://aaltodoc.aalto.fi/handle/123456789/36242
https://aaltodoc.aalto.fi/handle/123456789/36242
Publikováno v:
Proceedings of the VLDB Endowment
Proceedings of the 45h International Conference on Very Large Data Bases
Proceedings of the 45h International Conference on Very Large Data Bases
We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ce38481ec10f90c38d4876a0a6e42d7
Publikováno v:
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017-ASONAM 17
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 -ASONAM '17
ASONAM
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017-ASONAM 17
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 -ASONAM '17
ASONAM
openaire: EC/H2020/654024/EU//SoBigData Finding topical experts on micro-blogging sites, such as Twitter, is an essential information-seeking task. In this paper, we introduce an expert-finding algorithm for Twitter, which can be generalized to find
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::363ad19a02b93de9ae175717c611d32b