@article{AdnanSrsicVenticichetal.2020,
  author    = {Adnan, Hassan Sami and Srsic, Amanda and Venticich, Pete Milos and Townend, David M.R.},
  title     = {Using AI for mental health analysis and prediction in school surveys},
  series = {European journal of public health},
  volume    = {30},
  journal   = {European journal of public health},
  publisher = {Oxford Univ. Press},
  address   = {Oxford [u.a.]},
  issn      = {1101-1262},
  doi       = {10.1093/eurpub/ckaa165.336},
  pages     = {V125 -- V125},
  year      = {2020},
  abstract  = {Background: Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods: For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results: We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions: This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects.},
  language  = {en}
}
@article{HofeditzMirbabaieOrtmann2023,
  author    = {Hofeditz, Lennart and Mirbabaie, Milad and Ortmann, Mara},
  title     = {Ethical challenges for human-agent interaction in virtual collaboration at work},
  series = {International journal of human computer interaction},
  journal   = {International journal of human computer interaction},
  publisher = {Taylor \& Francis},
  address   = {New York, NY},
  issn      = {1044-7318},
  doi       = {10.1080/10447318.2023.2279400},
  pages     = {17},
  year      = {2023},
  abstract  = {In virtual collaboration at the workplace, a growing number of teams apply supportive conversational agents (CAs). They take on different work-related tasks for teams and single users such as scheduling meetings or stimulating creativity. Previous research merely focused on these positive aspects of introducing CAs at the workplace, omitting ethical challenges faced by teams using these often artificial intelligence (AI)-enabled technologies. Thus, on the one hand, CAs can present themselves as benevolent teammates, but on the other hand, they can collect user data, reduce worker autonomy, or foster social isolation by their service. In this work, we conducted 15 expert interviews with senior researchers from the fields of ethics, collaboration, and computer science in order to derive ethical guidelines for introducing CAs in virtual team collaboration. We derived 14 guidelines and seven research questions to pave the way for future research on the dark sides of human-agent interaction in organizations.},
  language  = {en}
}
@article{Wiemann2021,
  author    = {Wiemann, Dirk},
  title     = {Being Taught Something World-Sized},
  series = {The Work of World Literature},
  journal   = {The Work of World Literature},
  editor    = {Robinson, Benjamin Lewis},
  publisher = {ICI Press},
  address   = {Berlin},
  issn      = {2627-728X},
  doi       = {10.37050/ci-19_07},
  pages     = {149 -- 172},
  year      = {2021},
  abstract  = {This paper reads 'The Detainee's Tale as told to Ali Smith' (2016) as an exemplary demonstration of the work of world literature. Smith's story articulates an ethics of reading that is grounded in the recipient's openness to the singular, unpredictable, and unverifiable text of the other. More specifically, Smith's account enables the very event that it painstakingly stages: the encounter with alterity and newness, which is both the theme of the narrative and the effect of the text on the reader. At the same time, however, the text urges to move from an ethics of literature understood as the responsible reception of the other by an individual reader to a more explicitly convivial and political ethics of commitment beyond the scene of reading.},
  language  = {en}
}