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This paper aims to discuss the role of information and communication technologies (ICTs) in the effective assessment of the sustainable development goals (SDGs) related to smart and sustainable city initiatives.

The study is based on a systematic review of the literature within the Web of Science and Scopus databases, comprising the studies about ICTs related to smart and sustainable city initiatives and sustainable assessment.

The main results point out that there are several different ways of assessing SDGs performance related to ICTs use in smart and sustainable city initiatives. However, the effectiveness of these assessments can be questioned. The intensive use of technology understood as the core of smart and sustainable cities does not imply an improvement in sustainability unless these technologies are strategically addressed to underpin those objectives. Moreover, not all SDGs have considered the use of ICTs in their targets.

The scope of the study is limited to “how” the information is used and managed, rather than analysing the sustainable performance itself. As a limitation, the findings and conclusions do not consider other sources of studies, such as grey literature.

This study suggests some requirements for providing better and more reliable sustainable assessment, making smart and sustainable city initiatives more correlated with the SDGs.

By acknowledging the difficulties associated with SDGs assessment, concerning the municipal level, the study offers valuable insights into the effectiveness of public policies and public management. Besides, the findings shed some light on if and how the use of ICTs can effectively enhance sustainable development issues.

This study offers valuable contributions to the literature by providing a collection of insights regarding how the ICTs may genuinely lead to a sound assessment of sustainable development, especially regarding the SDGs.

This paper aims to investigate whether professional data analysts’ trust of black-box systems is increased by explainability artifacts.

The study was developed in two phases. First a black-box prediction model was estimated using artificial neural networks, and local explainability artifacts were estimated using local interpretable model-agnostic explanations (LIME) algorithms. In the second phase, the model and explainability outcomes were presented to a sample of data analysts from the financial market and their trust of the models was measured. Finally, interviews were conducted in order to understand their perceptions regarding black-box models.

The data suggest that users’ trust of black-box systems is high and explainability artifacts do not influence this behavior. The interviews reveal that the nature and complexity of the problem a black-box model addresses influences the users’ perceptions, trust being reduced in situations that represent a threat (e.g. autonomous cars). Concerns about the models’ ethics were also mentioned by the interviewees.

The study considered a small sample of professional analysts from the financial market, which traditionally employs data analysis techniques for credit and risk analysis. Research with personnel in other sectors might reveal different perceptions.

Other studies regarding trust in black-box models and explainability artifacts have focused on ordinary users, with little or no knowledge of data analysis. The present research focuses on expert users, which provides a different perspective and shows that, for them, trust is related to the quality of data and the nature of the problem being solved, as well as the practical consequences. Explanation of the algorithm mechanics itself is not significantly relevant.