@article {1428,
	title = {Bias in data-driven artificial intelligence systems{\textemdash}An introductory survey},
	journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	volume = {10},
	number = {3},
	year = {2020},
	pages = {e1356},
	abstract = {Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.},
	doi = {https://doi.org/10.1002/widm.1356},
	url = {https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1356},
	author = {Ntoutsi, Eirini and Fafalios, Pavlos and Gadiraju, Ujwal and Iosifidis, Vasileios and Nejdl, Wolfgang and Vidal, Maria-Esther and Salvatore Ruggieri and Franco Turini and Papadopoulos, Symeon and Krasanakis, Emmanouil and others}
}
@article {1302,
	title = {(So) Big Data and the transformation of the city},
	journal = {International Journal of Data Science and Analytics},
	year = {2020},
	abstract = {The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the {\textquotedblleft}City of Citizens{\textquotedblright} thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.},
	doi = {https://doi.org/10.1007/s41060-020-00207-3},
	url = {https://link.springer.com/article/10.1007/s41060-020-00207-3},
	author = {Andrienko, Gennady and Andrienko, Natalia and Boldrini, Chiara and Caldarelli, Guido and Paolo Cintia and Cresci, Stefano and Facchini, Angelo and Fosca Giannotti and Gionis, Aristides and Riccardo Guidotti and others}
}
