@inproceedings{DBLP:conf/atal/DellAnnaMDGJOY24,
  author    = {Davide Dell'Anna and
               Pradeep K. Murukannaiah and
               Bernd Dudzik and
               Davide Grossi and
               Catholijn M. Jonker and
               Catharine Oertel and
               Pinar Yolum},
  title     = {Toward a Quality Model for Hybrid Intelligence Teams},
  booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents
               and MultiAgent Systems, {AAMAS} 2024},
  pages     = {434--443},
  year = {2024},
  url_Link = {https://dl.acm.org/doi/10.5555/3635637.3662893},
  url_Paper = {2024_AAMAS/AAMAS24_DellAnnaMDGJOY.pdf},
  url_Poster = {2024_AAMAS/AAMAS24_DellAnnaMDGJOY_Poster.pdf},
  url_Supplement = {https://doi.org/10.5281/zenodo.10593358},
  doi       = {10.5555/3635637.3662893},
  keywords  = {Hybrid Intelligence, Quality model, Human-agent teamwork, Sociotechnical systems, Team Diagnostic Survey},
  abstract = {Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. Our study, featuring the insights of 50 HI researchers, shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Our study also reveals limitations in applying certain human team properties, such as coaching, rewards, and recognition, to HI systems due to the inherent human-AI asymmetry.}
}

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M.","Oertel, C.","Yolum, P."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Davide"],"propositions":[],"lastnames":["Dell'Anna"],"suffixes":[]},{"firstnames":["Pradeep","K."],"propositions":[],"lastnames":["Murukannaiah"],"suffixes":[]},{"firstnames":["Bernd"],"propositions":[],"lastnames":["Dudzik"],"suffixes":[]},{"firstnames":["Davide"],"propositions":[],"lastnames":["Grossi"],"suffixes":[]},{"firstnames":["Catholijn","M."],"propositions":[],"lastnames":["Jonker"],"suffixes":[]},{"firstnames":["Catharine"],"propositions":[],"lastnames":["Oertel"],"suffixes":[]},{"firstnames":["Pinar"],"propositions":[],"lastnames":["Yolum"],"suffixes":[]}],"title":"Toward a Quality Model for Hybrid Intelligence Teams","booktitle":"Proceedings of the 23rd International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2024","pages":"434–443","year":"2024","url_link":"https://dl.acm.org/doi/10.5555/3635637.3662893","url_paper":"2024_AAMAS/AAMAS24_DellAnnaMDGJOY.pdf","url_poster":"2024_AAMAS/AAMAS24_DellAnnaMDGJOY_Poster.pdf","url_supplement":"https://doi.org/10.5281/zenodo.10593358","doi":"10.5555/3635637.3662893","keywords":"Hybrid Intelligence, Quality model, Human-agent teamwork, Sociotechnical systems, Team Diagnostic Survey","abstract":"Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. Our study, featuring the insights of 50 HI researchers, shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Our study also reveals limitations in applying certain human team properties, such as coaching, rewards, and recognition, to HI systems due to the inherent human-AI asymmetry.","bibtex":"@inproceedings{DBLP:conf/atal/DellAnnaMDGJOY24,\n author = {Davide Dell'Anna and\n Pradeep K. Murukannaiah and\n Bernd Dudzik and\n Davide Grossi and\n Catholijn M. Jonker and\n Catharine Oertel and\n Pinar Yolum},\n title = {Toward a Quality Model for Hybrid Intelligence Teams},\n booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents\n and MultiAgent Systems, {AAMAS} 2024},\n pages = {434--443},\n year = {2024},\n url_Link = {https://dl.acm.org/doi/10.5555/3635637.3662893},\n url_Paper = {2024_AAMAS/AAMAS24_DellAnnaMDGJOY.pdf},\n url_Poster = {2024_AAMAS/AAMAS24_DellAnnaMDGJOY_Poster.pdf},\n url_Supplement = {https://doi.org/10.5281/zenodo.10593358},\n doi = {10.5555/3635637.3662893},\n keywords = {Hybrid Intelligence, Quality model, Human-agent teamwork, Sociotechnical systems, Team Diagnostic Survey},\n abstract = {Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. Our study, featuring the insights of 50 HI researchers, shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Our study also reveals limitations in applying certain human team properties, such as coaching, rewards, and recognition, to HI systems due to the inherent human-AI asymmetry.}\n}\n\n","author_short":["Dell'Anna, D.","Murukannaiah, P. K.","Dudzik, B.","Grossi, D.","Jonker, C. 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