Abstract:In the recommender systems literature, it has been shown that, in addition to improving system effectiveness, explaining recommendations may increase user satisfaction, trust, persuasion and loyalty. In general, explanations focus on the filtering algorithms or the users and items involved in the generation of recommendations. However, on certain domains that are rich on user-generated textual content, it would be valuable to provide justifications of recommendations according to arguments that are explicit, u… Show more
“…Paper ID F-measure [44,45,47,48,57,58,60,64,66,73,77,80,82,86] Precision [43,45,57,60,62,66,67,73,[76][77][78]80,82,86] Accuracy [43,44,46,62,63,69,70,73,[76][77][78]80,82] Recall [45,52,57,60,62,66,73,77,78,80,82,…”
Section: Discussionmentioning
confidence: 99%
“…Paper ID Deliberative [42,48,55,61,64,65,69,71,79,81,86] Democratic [41,44,50,54,65,81,85] Civic Engagement [41,44,47,[49][50][51][53][54][55]58,60,63,65,66,[68][69][70]74,76,[78][79][80][81][82][83]85,86]…”
Section: Effectsmentioning
confidence: 99%
“…In some studies, these evaluations are about "Openness and Transparency" that the implementation of a new technology [77] or a new platform [60] offers while others document problems that are caused by the lack of Openness and Transparency in existing platforms [79]. [41,60,65,76] As for "quantity", the literature almost never mentions a particularly high increase in participation. Divergent outcomes follow the implementation of digital platforms for policymaking.…”
Electronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not.
“…Paper ID F-measure [44,45,47,48,57,58,60,64,66,73,77,80,82,86] Precision [43,45,57,60,62,66,67,73,[76][77][78]80,82,86] Accuracy [43,44,46,62,63,69,70,73,[76][77][78]80,82] Recall [45,52,57,60,62,66,73,77,78,80,82,…”
Section: Discussionmentioning
confidence: 99%
“…Paper ID Deliberative [42,48,55,61,64,65,69,71,79,81,86] Democratic [41,44,50,54,65,81,85] Civic Engagement [41,44,47,[49][50][51][53][54][55]58,60,63,65,66,[68][69][70]74,76,[78][79][80][81][82][83]85,86]…”
Section: Effectsmentioning
confidence: 99%
“…In some studies, these evaluations are about "Openness and Transparency" that the implementation of a new technology [77] or a new platform [60] offers while others document problems that are caused by the lack of Openness and Transparency in existing platforms [79]. [41,60,65,76] As for "quantity", the literature almost never mentions a particularly high increase in participation. Divergent outcomes follow the implementation of digital platforms for policymaking.…”
Electronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not.
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