Despite having set the theoretical ground for explainable systems decades ago, the information system scholars have given little attention to new developments in the decision-making with humans-in-the-loop in real-world problems. We take the sociotechnical system lenses and employ mixed-method analysis of a field intervention to study the machine-learning informed decision-making with interpreted models' outputs. Contrary to theory, our results suggest a small positive effect of explanations on confidence in the final decision, and a negligible effect on the decisions' quality. We uncover complex dynamic interactions between humans and algorithms, and the interplay of algorithmic aversion, trust, experts' heuristic, and changing uncertainty-resolving condititions.

This paper aims to assess the techno-economic suitability of different marine areas for the
nearshore and offshore wave energy exploration in the Madeira Islands, while avoiding conflict with any technical,
environmental, legal or other use restriction. A wave energy device is also
evaluated, which could constitute the best energetic performance in such specific sea states. The compatibility evaluation of different wave energy conversion technologies is made through the computation of a novel index over the area of study. The location suitability assessment is made in a GIS environment by discarding different locations from the masking process. Later, a multicriteria decision making methodology is carried out over the available areas. The results aim to provide stakeholders and decision-makers with meaningful information on the most suitable locations and wave converters for the potential deployment of a wave energy exploration facility in the Madeira Islands.