Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Bajardi Paolo"'
The interaction between social norms and gender roles prescribes gender-specific behaviors that influence moral judgments. Here, we study how moral judgments are biased by the gender of the protagonist of a story. Using data from r/AITA, a Reddit com
Externí odkaz:
http://arxiv.org/abs/2408.12872
Publikováno v:
2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), Shenzhen, China, 2022, pp. 1-10
Several explainable AI methods allow a Machine Learning user to get insights on the classification process of a black-box model in the form of local linear explanations. With such information, the user can judge which features are locally relevant fo
Externí odkaz:
http://arxiv.org/abs/2302.07760
Publikováno v:
Transactions on Machine Learning Research, 2023, ISSN 2835-8856
This paper addresses a gap in the current state of the art by providing a solution for modeling causal relationships that evolve over time and occur at different time scales. Specifically, we introduce the multiscale non-stationary directed acyclic g
Externí odkaz:
http://arxiv.org/abs/2208.14989
Autor:
Battiston, Alice, Napoli, Ludovico, Bajardi, Paolo, Panisson, André, Perotti, Alan, Szell, Michael, Schifanella, Rossano
Publikováno v:
EPJ Data Science 12, 9 (2023)
Cycling is an outdoor activity with massive health benefits, and an effective solution towards sustainable urban transport. Despite these benefits and the recent rising popularity of cycling, most countries still have a negligible uptake. This uptake
Externí odkaz:
http://arxiv.org/abs/2203.09378
Most methods for explaining black-box classifiers (e.g. on tabular data, images, or time series) rely on measuring the impact that removing/perturbing features has on the model output. This forces the explanation language to match the classifier's fe
Externí odkaz:
http://arxiv.org/abs/2202.08815
Publikováno v:
ACM International Conference on AI in Finance, 2021
In recent years, multi-factor strategies have gained increasing popularity in the financial industry, as they allow investors to have a better understanding of the risk drivers underlying their portfolios. Moreover, such strategies promise to promote
Externí odkaz:
http://arxiv.org/abs/2111.05072
Publikováno v:
PeerJ Computer Science 7:e479 (2021)
The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective explanations for black-box classifiers. The existing literature lists many desirable properties for explanations to be useful, but there is no consensus on how to
Externí odkaz:
http://arxiv.org/abs/2106.00461
Publikováno v:
J Med Internet Res 2021;23(1):e21212
The complex unfolding of the US opioid epidemic in the last 20 years has been the subject of a large body of medical and pharmacological research, and it has sparked a multidisciplinary discussion on how to implement interventions and policies to eff
Externí odkaz:
http://arxiv.org/abs/2102.11235
The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of biased models is a very delicate task which sh
Externí odkaz:
http://arxiv.org/abs/2011.04049
Autor:
Tizzoni Michele, Bajardi Paolo, Poletto Chiara, Ramasco José J, Balcan Duygu, Gonçalves Bruno, Perra Nicola, Colizza Vittoria, Vespignani Alessandro
Publikováno v:
BMC Medicine, Vol 10, Iss 1, p 165 (2012)
Abstract Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven
Externí odkaz:
https://doaj.org/article/2d304c62ea1549b1920f791790ec8e72
