Oct 2026· Adjunct Proceedings of the 14th Nordic Conference on Human-Computer Interaction· 0 citations· 10 references
Abstract
Interacting with digital systems today increasingly involves being required to demonstrate that one is human. While such requirements are often framed as technical solutions to problems of security and verification, they also raise broader questions about how humanness is defined, operationalized, and recognized within contemporary digital environments. Rather than attempting to define what a human is, this paper explores how humanness is produced, recognized, and negotiated in relation to digital systems. The analysis builds on a situated vignette and a dialogical exchange between four researchers who read and discussed Karin Hansson’s 2022 essay “I’m not a robot”. Hansson’s text reflects the discussion about AI years ago, and is based on an investigation of how “humanity” is extracted in various digital systems, commodifying human expressions, relationships, and cognition. In addition, the paper draws on a brief, situated account from a workplace interaction involving AI-mediated communication, used as an analytical fragment to further explore how recognition as human unfolds in practice. The conversation serves as a generative space for exploring how different conceptualizations of the human emerge across domains such as work, education, and digital communication. The aim is not to resolve the question of the human as such, but to problematize how it is continuously reconfigured within sociotechnical contexts. Rather than treating the human as a predefined category, this paper shows how humanness is continuously produced through systems of human recognition that define what can be seen, measured, and accepted as human.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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