Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Christoph Heitz"'
Publikováno v:
Journal of Advanced Transportation, Vol 2023 (2023)
A novel model approach is proposed to estimate the spatiotemporal distribution of demand for free-floating carsharing. The proposed model is based on a Poisson regression model for right-censored data and estimates possibly time-varying demand rates
Externí odkaz:
https://doaj.org/article/c9196fb50e3b4578b90b2bd1dea60653
Autor:
Matthias Templ, Christoph Heitz
Publikováno v:
Austrian Journal of Statistics, Vol 49, Iss 2 (2020)
For redistribution and operating bikes in a free-floating systems, two measures are of highest priority. First, the information about the expected number of rentals on a day is an important measure for service providers for management and service of
Externí odkaz:
https://doaj.org/article/91e9cdbf1356432fbff54e645a54db32
Autor:
Michele Loi, Christoph Heitz
In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the machine learning literature: 1) calibration between groups and 2) equality of false positive and false negative rates between groups. In our paper, we f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00b4ff5efc6d850de8899a13f288d480
https://hdl.handle.net/11475/27395
https://hdl.handle.net/11475/27395
Autor:
Christoph Heitz, Corinna Hertweck
Publikováno v:
2021 8th Swiss Conference on Data Science (SDS).
While the field of algorithmic fairness has brought forth many ways to measure and improve the fairness of machine learning models, these findings are still not widely used in practice. We suspect that one reason for this is that the field of algorit
Publikováno v:
FAccT
The final publication is available in the ACM Digital Library via https://dl.acm.org/doi/10.1145/3442188.3445936.
A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric
A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric
Autor:
Jürg Meierhofer, Christoph Heitz
Publikováno v:
SDS
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::707cd9d489154df076841d9920e69b33
This paper is a reply to "On Statistical Criteria of Algorithmic Fairness," by Brian Hedden. We question the significance of arguing that many group fairness criteria discussed in the machine learning literature are not necessary conditions for the f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19a7d8f100530ae77396780f79e7256f
https://www.zora.uzh.ch/id/eprint/214801/
https://www.zora.uzh.ch/id/eprint/214801/
Publikováno v:
SDS
Up to date, more than 80 codes exist for handling ethical risks of artificial intelligence and big data. In this paper, we analyse where those codes converge and where they differ. Based on an in-depth analysis of 20 guidelines, we identify three pro
Autor:
Christoph Heitz, Matthias Templ
Publikováno v:
Austrian Journal of Statistics, Vol 49, Iss 2 (2020)
For redistribution and operating bikes in a free-floating systems, two measures are of highest priority. First, the information about the expected number of rentals on a day is an important measure for service providers for management and service of
Publikováno v:
Smart Service Systems, Operations Management, and Analytics ISBN: 9783030309664
Value co-creation requires a system that links actors together for mutual value creation. In our paper, we describe the development of such a system in the context of the new free-floating e-bike-sharing system (BSS) in Zurich, Switzerland. This BSS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ecb4d2abde20c56991d5c548bb04e0e
https://doi.org/10.1007/978-3-030-30967-1_11
https://doi.org/10.1007/978-3-030-30967-1_11