@article{SirbuBeckerCaminitietal.2015,
  author    = {S{\^i}rbu, Alina and Becker, Martin and Caminiti, Saverio and De Baets, Bernard and Elen, Bart and Francis, Louise and Gravino, Pietro and Hotho, Andreas and Ingarra, Stefano and Loreto, Vittorio and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Servedio, Vito D.P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca and Van den Bossche, Joris},
  title     = {Participatory Patterns in an International Air Quality Monitoring Initiative},
  series = {PLoS ONE},
  volume    = {10},
  journal   = {PLoS ONE},
  number    = {8},
  doi       = {10.1371/journal. pone.0136763},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151379},
  pages     = {e0136763},
  year      = {2015},
  abstract  = {The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.},
  language  = {en}
}
@article{WienrichCarolusMarkusetal.2023,
  author    = {Wienrich, Carolin and Carolus, Astrid and Markus, Andr{\´e} and Augustin, Yannik and Pfister, Jan and Hotho, Andreas},
  title     = {Long-term effects of perceived friendship with intelligent voice assistants on usage behavior, user experience, and social perceptions},
  series = {Computers},
  volume    = {12},
  journal   = {Computers},
  number    = {4},
  issn      = {2073-431X},
  doi       = {10.3390/computers12040077},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313552},
  year      = {2023},
  abstract  = {Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human-AI interactions.},
  language  = {en}
}
@article{WienrichCarolusRothIsigkeitetal.2022,
  author    = {Wienrich, Carolin and Carolus, Astrid and Roth-Isigkeit, David and Hotho, Andreas},
  title     = {Inhibitors and enablers to explainable AI success: a systematic examination of explanation complexity and individual characteristics},
  series = {Multimodal Technologies and Interaction},
  volume    = {6},
  journal   = {Multimodal Technologies and Interaction},
  number    = {12},
  issn      = {2414-4088},
  doi       = {10.3390/mti6120106},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297288},
  year      = {2022},
  abstract  = {With the increasing adaptability and complexity of advisory artificial intelligence (AI)-based agents, the topics of explainable AI and human-centered AI are moving close together. Variations in the explanation itself have been widely studied, with some contradictory results. These could be due to users' individual differences, which have rarely been systematically studied regarding their inhibiting or enabling effect on the fulfillment of explanation objectives (such as trust, understanding, or workload). This paper aims to shed light on the significance of human dimensions (gender, age, trust disposition, need for cognition, affinity for technology, self-efficacy, attitudes, and mind attribution) as well as their interplay with different explanation modes (no, simple, or complex explanation). Participants played the game Deal or No Deal while interacting with an AI-based agent. The agent gave advice to the participants on whether they should accept or reject the deals offered to them. As expected, giving an explanation had a positive influence on the explanation objectives. However, the users' individual characteristics particularly reinforced the fulfillment of the objectives. The strongest predictor of objective fulfillment was the degree of attribution of human characteristics. The more human characteristics were attributed, the more trust was placed in the agent, advice was more likely to be accepted and understood, and important needs were satisfied during the interaction. Thus, the current work contributes to a better understanding of the design of explanations of an AI-based agent system that takes into account individual characteristics and meets the demand for both explainable and human-centered agent systems.},
  language  = {en}
}