The increasing adoption of artificial intelligence (AI) in critical domains such as healthcare, law, and defense demands robust mechanisms to ensure transparency and explainability in decision‐making processes. While machine learning and deep learning algorithms have advanced significantly, their growing complexity presents persistent interpretability challenges. Existing maturity frameworks, such as Capability Maturity Model Integration, fall short in addressing the distinct requirements of explainability in AI systems, particularly where ethical compliance and public trust are paramount. To address this gap, we propose the Maturity Model for eXplainable Artificial Intelligence: Analysis and Evaluation (MM4XAI‐AE), a domain‐agnostic maturity model tailored to assess and guide the practical deployment of explainability in AI‐based applications. The model integrates two complementary components: an analysis model and an evaluation model, structured across four maturity levels—operational, justified, formalized, and managed. It evaluates explainability across three critical dimensions: technical foundations, structured design, and human‐centered explainability. MM4XAI‐AE is grounded in the PAG‐XAI framework, emphasizing the interrelated dimensions of practicality, auditability, and governance, thereby aligning with current reflections on responsible and trustworthy AI. The MM4XAI‐AE model is empirically validated through a structured evaluation of thirteen published AI applications from diverse sectors, analyzing their design and deployment practices. The results show a wide distribution across maturity levels, underscoring the model’s capacity to identify strengths, gaps, and actionable pathways for improving explainability. This work offers a structured and scalable framework to standardize explainability practices and supports researchers, developers, and policymakers in fostering more transparent, ethical, and trustworthy AI systems.

We present the results of the BCNStreet-lab project, a co-creation process of a prototype that "died before it even existed" and that has led us to reflect on the creative power that participatory design can have among experts. Examining the high variability that the design acquired with the co-creation sessions, we realized how an open co-creation process can generate overflows in the design. It makes innovation possible but can also lead to failure, as is this case, because of how risky participatory design of “fluid technologies” and how ineffective reproducing the Quadruple helix innovation model in small projects can be. We tried to account for the fruitful relationship between co-creation and the design of electronic technologies, which happens in the “lobby” of the technical design of the prototype. It generates negotiations and interactions that lead to progress in the democratization of design practices in electronic engineering, computing, and telecommunications. At this point, co-creation is conceived as a space in which institutional practices, social needs, technological imaginaries, economic interests, techno-scientific interests, etc., are brought together, translated and inscribed into the design of technologies.

Artificial intelligence (AI) is increasingly being investigated to support decision-making and process improvement in copper mineral processing and extractive metallurgy; however, the available evidence remains fragmented across operational units, methods, sustainability dimensions, and geographical contexts. This systematic mapping review, complemented by qualitative cross-study synthesis, analysed publications from 2014 to 2025 retrieved from IEEE Xplore, Scopus, and Google Scholar. Of the 410 records identified before screening, 71 studies were classified according to four predefined research questions addressing copper-processing operations, AI domains, sustainability contributions, and geographical distribution. The findings show an uneven distribution of research across the processing flowsheet. Comminution and froth flotation received the greatest attention, whereas lixiviation, solvent extraction, electrowinning, and electrorefining were less represented. Machine learning was the dominant AI domain, particularly supervised approaches based on historical operational data; however, no algorithm was universally superior, as reported performance depended on the dataset, target variable, operational context, and validation procedure. Most studies focused on prediction, monitoring, fault diagnosis, and operational optimisation, while reinforcement learning, hybrid mechanistic–data-driven models, adaptive control, and sustained industrial deployment remained comparatively limited. Sustainability assessments emphasised operational and economic outcomes more frequently than social, ethical, circular-economy, and broader environmental implications. Geographical results reflected the locations assigned to the reviewed studies and industrial cases rather than regional AI maturity. The review identifies data availability, heterogeneous validation practices, model transferability, and limited evidence of sustained industrial deployment as recurring challenges. It proposes a staged research agenda to develop more reliable, transferable, and human-supervised AI applications across copper-processing operations.

Se presenta la experiencia de tres procesos de innovación de base tecnológica aplicando técnicas de diseño y desarrollo participativo de tecnologías, técnicas de co-creación y agilidad. El análisis de los tres procesos da cuenta del papel que pueden tener los investigadores sociales en el escalamiento y la articulación social de los productos obtenidos, y el papel de los obstáculos en los procesos de desarrollo. Se ponen en relieve las potencialidades que tienen las metodologías de diseño participativo de tecnologías en la creación de comunidades de usuarios, la experimentación en métodos con técnicas de co-creación y lo limitadas que resultan en algunos casos las metodologías ágiles para crear articulaciones sociales, lo cual le resta posibilidad de éxito una vez finalizado el proceso de desarrollo. The experience of three technology-based innovation processes is presented, applying design techniques and participatory development of technologies, co-creation techniques and agility. The analysis of the three processes accounts for the role that social researchers can play in the scaling and social articulation of the products obtained. And the role that obstacles play in development processes. It highlights the potentialities that Participatory Technology Design methodologies have in the creation of user communities, experimentation in methods with co-creation techniques and how limited agile methodologies are in some cases to create social articulations, which it reduces the possibility of success once the development process is finished.