TY - GEN
T1 - Towards a Social Artificial Intelligence
AU - Pedreschi, Dino
AU - Dignum, Frank
AU - Morini, Virginia
AU - Pansanella, Valentina
AU - Cornacchia, Giuliano
PY - 2023
Y1 - 2023
N2 - Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.
AB - Artificial Intelligence can both empower individuals to face complex societal challenges and exacerbate problems and vulnerabilities, such as bias, inequalities, and polarization. For scientists, an open challenge is how to shape and regulate human-centered Artificial Intelligence ecosystems that help mitigate harms and foster beneficial outcomes oriented at the social good. In this tutorial, we discuss such an issue from two sides. First, we explore the network effects of Artificial Intelligence and their impact on society by investigating its role in social media, mobility, and economic scenarios. We further provide different strategies that can be used to model, characterize and mitigate the network effects of particular Artificial Intelligence driven individual behavior. Secondly, we promote the use of behavioral models as an addition to the data-based approach to get a further grip on emerging phenomena in society that depend on physical events for which no data are readily available. An example of this is tracking extremist behavior in order to prevent violent events. In the end, we illustrate some case studies in-depth and provide the appropriate tools to get familiar with these concepts.
UR - http://www.scopus.com/inward/record.url?scp=85152533082&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-24349-3_21
DO - 10.1007/978-3-031-24349-3_21
M3 - Conference contribution
SN - 9783031243486
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 415
EP - 428
BT - Human-Centered Artificial Intelligence - Advanced Lectures
A2 - Chetouani, M.
A2 - Dignum, V.
A2 - Lukowicz, P.
A2 - Sierra, C.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th European Advanced Course on Artificial Intelligence, ACAI 2021
Y2 - 11 October 2021 through 15 October 2021
ER -