Publications

@conference{Loukis2019c,
title = {Economic Crisis Policy Analytics Based on Artificial Intelligence},
author = {Euripidis Loukis, Manolis Maragoudakis, Niki Kyriakou},
url = {https://link.springer.com/chapter/10.1007/978-3-030-27325-5_20},
year = {2019},
date = {2019-07-31},
publisher = {Springer, Cham},
abstract = {An important trend in the area of digital government is its expansion beyond the support of internal processes and operations, as well as transactions and consultations with citizens and firms, which were the main objectives of its first generations, towards the support of higher-level functions of government agencies, with main emphasis on public policy making. This gives rise to the gradual development of policy analytics. Another important trend in the area of digital government is the increasing exploitation of artificial intelligence techniques by government agencies, mainly for the automation, support and enhancement of operational tasks and lower-level decision making, but only to a very limited extent for the support of higher-level functions, and especially policy making. Our paper contributes towards the advancement and the combination of these two important trends: it proposes a policy analytics methodology for the exploitation of existing public and private sector data, using a big data oriented artificial intelligence technique, feature selection, in order to support policy making concerning one of the most serious problems that governments face, the economic crises. In particular, we present a methodology for exploiting existing data of taxation authorities, statistical agencies, and also of private sector business information and consulting firms, in order to identify characteristics of a firm (e.g. with respect to strategic directions, resources, capabilities, practices, etc.) as well as its external environment (e.g. with respect to competition, dynamism, etc.) that affect (positively or negatively) its resilience to the crisis with respect to sales revenue; for this purpose an advanced artificial intelligence feature selection algorithm, the Boruta ‘all-relevant’ variables identification one, is used. Furthermore, an application of the proposed economic crisis policy analytics methodology is presented, which provides a first validation of the usefulness of our methodology.},
keywords = {Artificial Intelligence, Crisis, Feature selection, Policy analytics, Policy informatics},
pubstate = {published},
tppubtype = {conference}
}

@conference{Alexopoulos2019,
title = {How Machine Learning is Changing e-Government},
author = {Charalampos Alexopoulos and Zoi Lachana and Aggeliki Androutsopoulou and Vasiliki Diamantopoulou and Yannis Charalabidis and Michalis Avgerinos Loutsaris},
url = {http://www.icsd.aegean.gr/publication_files/Conference/583212650.pdf},
year = {2019},
date = {2019-04-01},
pages = {10},
abstract = {Big Data is, clearly, an integral part of modern information societies. A vast amount of data is, daily, produced and it is estimated that, for the years to come, this number will grow dramatically. In order for transforming this hidden provided information into a useful one, the use of advanced technologies, such as Machine Learning is deemed appropriate. Over the last years, Machine Learning has grown a great effort considering the given opportunities its usage provides. Furthermore, Machine Learning is a technology that can handle Big Data classification for statistical or even more complex purposes such as decision making. At the same time the new generation of government, Government 3.0, explores all the new opportunities to tackle any challenge faced by contemporary societies by utilizing new technologies for data driven decision making. Taking into account the opportunities Machine Learning can provide, more and more governments participate in the development of such applications in different governmental domains. But is the Machine Learning only beneficial for public sectors? Although there is a huge number of researches in the literature there is no a comprehensive study towards the analysis of this technology. Our research moves towards this question conducting a comprehensive analysis of the use of Machine Learning from Governments. Through the analysis all benefits and barriers are indicated from the public sectors' perspective pinpointing, also, a number of Machine Learning applications where governments are involved.},
keywords = {Artificial Intelligence, Big Data, government 3.0, Government services, Machine Learning},
pubstate = {published},
tppubtype = {conference}
}

@conference{Kankanhalli2019,
title = {IoT and AI for Smart Government: A Research Agenda},
author = {Atreyi Kankanhalli, Yannis Charalabidis & Sehl Mellouli},
doi = {https://doi.org/10.1016/j.giq.2019.02.003},
year = {2019},
date = {2019-03-15},
publisher = {Elsevier Inc},
abstract = {The Internet of things (IoT) is the network of objects/things that contain electronics, software, sensors, and actuators, which allows these things to connect, interact, and exchange data. The users, sensors, and networks generate huge amounts of data from which governments can develop applications and gain knowledge using Artificial Intelligence (AI) techniques. Thus, IoT and AI can enable the development of valuable services for citizens, businesses, and public agencies, in multiple domains, such as transportation, energy, healthcare, education, and public safety. This guest editorial for the special issue on IoT and AI for Smart Government, identifies the challenges involved in implementing and adopting these technologies in the public sector, and proposes a comprehensive research framework, which includes both IoT and AI elements for smart government transformation. Subsequently, the editorial provides a brief introduction of the six papers in this special issue. Finally, an agenda for future research on IoT and AI for smart government is presented, based on the proposed framework and gaps in existing literature, supported by the papers that were submitted to this special issue. The agenda comprises four directions i.e., conducting domain-specific studies, going beyond adoption studies to examine implementation and evaluation of these technologies, focusing on specific challenges and thus quick wins, and expanding the existing set of research methods and theoretical foundations used.},
keywords = {Artificial Intelligence, Internet of things, Iot, Smart Government},
pubstate = {published},
tppubtype = {conference}
}

@article{Androutsopoulou2018,
title = {Transforming the communication between citizens and government through AI-guided chatbots},
author = {Aggeliki Androutsopoulou, Nikos Karacapilidis, Euripidis Loukis, YannisCharalabidis},
url = {https://www.sciencedirect.com/science/article/pii/S0740624X17304008},
doi = {https://doi.org/10.1016/j.giq.2018.10.001},
year = {2018},
date = {2018-10-12},
journal = {Government Information Quarterly},
pages = {10},
abstract = {Driven by ‘success stories’ reported by private sector firms, government agencies have also started adopting various Artificial Intelligence (AI) technologies in diverse domains (e.g. health, taxation, and education); however, extensive research is required in order to exploit the full potential of AI in the public sector, and leverage various AI technologies to address important problems/needs. This paper makes a contribution in this direction: it presents a novel approach, as well as the architecture of an ICT platform supporting it, for the advanced exploitation of a specific AI technology, namely chatbots, in the public sector in order to address a crucial issue: the improvement of communication between government and citizens (which has for long time been problematic). The proposed approach builds on natural language processing, machine learning and data mining technologies, and leverages existing data of various forms (such as documents containing legislation and directives, structured data from government agencies' operational systems, social media data, etc.), in order to develop a new digital channel of communication between citizens and government. Making use of appropriately structured and semantically annotated data, this channel enables ‘richer’ and more expressive interaction of citizens with government in everyday language, facilitating and advancing both information seeking and conducting of transactions. Compared to existing digital channels, the proposed approach is appropriate for a wider range of citizens' interactions, with higher levels of complexity, ambiguity and uncertainty. In close co-operation with three Greek government agencies (the Ministry of Finance, a social security organization, and a big local government organization), this approach has been validated through a series of application scenarios.},
keywords = {Artificial Intelligence, chatbots, Digital transformation},
pubstate = {published},
tppubtype = {article}
}