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of 15
pro vyhledávání: '"Sühr, Tom"'
Machine Learning (ML) models are increasingly used to support or substitute decision making. In applications where skilled experts are a limited resource, it is crucial to reduce their burden and automate decisions when the performance of an ML model
Externí odkaz:
http://arxiv.org/abs/2409.20489
Machine learning (ML) models are increasingly used in various applications, from recommendation systems in e-commerce to diagnosis prediction in healthcare. In this paper, we present a novel dynamic framework for thinking about the deployment of ML m
Externí odkaz:
http://arxiv.org/abs/2405.13753
With large language models (LLMs) like GPT-4 appearing to behave increasingly human-like in text-based interactions, it has become popular to attempt to evaluate personality traits of LLMs using questionnaires originally developed for humans. While r
Externí odkaz:
http://arxiv.org/abs/2311.05297
In this report we provide an improvement of the significance adjustment from the FA*IR algorithm of Zehlike et al., which did not work for very short rankings in combination with a low minimum proportion $p$ for the protected group. We show how the m
Externí odkaz:
http://arxiv.org/abs/2012.12795
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable bias
Externí odkaz:
http://arxiv.org/abs/2012.00423
Publikováno v:
Companion Proceedings of the Web Conference 2020 (WWW '20 Companion), April 20--24, 2020, Taipei, Taiwan
Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine c
Externí odkaz:
http://arxiv.org/abs/1905.13134
Autor:
Katzenbach, Christian, Kopps, Adrian, Magalhães, João Carlos, Redeker, Dennis, Sühr, Tom, Wunderlich, Larissa
Publikováno v:
34
Platform policies contain the spelled out rules about what is allowed and prohibited on a service. As such, they constitute both a normative framework as well as a means of public communication by platforms. Studying the evolution of the increasingly
Externí odkaz:
https://www.ssoar.info/ssoar/handle/document/89155
Autor:
Zehlike, Meike, Sühr, Tom, Baeza-Yates, Ricardo, Bonchi, Francesco, Castillo, Carlos, Hajian, Sara
Publikováno v:
In Information Processing and Management January 2022 59(1)
Akademický článek
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Autor:
Magalhães, João Carlos, Katzenbach, Christian, Redeker, Dennis, Sühr, Tom, Wunderlich, Larissa, Kopps, Adrian
This is the original PGA dataset. Data has been collected retrospectively by a combination of automated and manual approaches., building on Internet Archive’s Wayback Machine. It includes policies by four major platforms ranging back to their found
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bca6cdddc2be06448b147387d4c641d6