Professionals' Perceptions of the Interaction between User Experience and Machine Learning - Costa, Ricardo Luiz Hentges; Soares, Tales Schifelbein; Lunardi, Gabriel Machado; Valle, Pedro Henrique Dias; Silva, Williamson (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2024: Anais do XX Simpósio Brasileiro de Sistemas de Informação, 2024)
Context: Users connect significantly with Information Systems (ISs) daily. The software industry is increasingly dedicated to meeting User Experience (UX) needs. Despite this, there is little evidence that UX experts use techniques to automate processes, such as employing Machine Learning (ML) techniques. The convergence of UX and ML is gaining relevance for its potential to enhance UX quality. Problem: This work arises from the need to understand perceptions, challenges, and opportunities in the intersection of UX and ML. The central issue is to gain insights enriching the understanding of ML’s transformative role in UX research, identifying integration advantages and challenges. Method: Semi-structured interviews were conducted with six professionals experienced in the intersection of UX and ML. A qualitative analysis collected detailed perceptions, generating relevant categories to understand professionals’ views on UX and ML integration. IS Theory: This work was conceived under the auspices of the community of practice theory, aiming to help community members interact to share ideas and thoughts and expand their knowledge. Results Summary: The interview analysis identified nine main categories, covering ML professionals’ difficulties in integrating UX into positive perceptions about ML adoption, providing a comprehensive view of respondents’ opinions. Contributions and Impact on the IS Area: The results aim to significantly contribute to the ISs community, offering valuable insights into professionals’ perceived advantages, disadvantages, and challenges in UX and ML convergence. The study enhances understanding of ML’s role in UX, supporting future practices and related research and positively impacting systems’ development and improvement.