Integration of User-Centered Design in the Development of Big Data and Machine Learning-Based Applications: A Systematic Mapping Study
Resumo
Technological advances in the last decades have allowed acquiring, storing, analyzing and manipulating big volumes of data, contributing to the development of increasingly advanced, dynamic and intelligent software. Looking beyond the advantages Big Data and Machine Learning (ML) solutions have brought, it is necessary to consider some of their challenges, such as the complexity of dealing with the data itself and the understanding of how the algorithms used in such solutions work. User-Centered Design (UCD) comprises a set of processes that focus on the users’ perspective when designing solutions, be it through interviews, surveys and evaluations, or even by actively participating in the design activities. Understanding users and their needs, and helping them understand and trust the products they use are key factors when designing Big Data and ML software; fields such as Explainable Artificial Intelligence (XAI) help develop solutions that meet these goals. Thus, this paper presents a systematic mapping covering 54 papers presenting approaches and methods that incorporate UCD into developing Big Data and ML solutions, with results highlighting an interest in usability factors such as perceived utility and learnability. This systematic mapping study sheds light on existing challenges and opportunities for enhancing these processes.
Palavras-chave:
Big Data, Machine Learning, User-Centered Design
Referências
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George I Gavriilidis, Vlasios K Dimitriadis, Marie-Christine Jaulent, and Pantelis Natsiavas. 2021. Identifying actionability as a key factor for the adoption of ‘intelligent’systems for drug safety: lessons learned from a user-centred design approach. Drug Safety 44, 11 (2021), 1165–1178.
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Publicado
07/11/2024
Como Citar
GOMES RAULINO, Natã Lael; DE CASTRO ANDRADE, Rossana Maria; DE SOUSA SANTOS, Ismayle.
Integration of User-Centered Design in the Development of Big Data and Machine Learning-Based Applications: A Systematic Mapping Study. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 23. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 673-684.
