Abstract
Our lives have been increasingly filled with technologies that use Artificial Intelligence (AI), whether at home, in public spaces, in social organizations, or in services. Like other technologies, adopting this emerging technology also requires society's attention to the challenges that may arise from it. The media brought to the public some unexpected results from using these technologies, for example, the unfairness case in the COMPAS system. It became more evident that these technologies can have unintended consequences. In particular, in the public interest domain, these unintended consequences and their origin are a challenge for public policies, governance, and responsible AI. This work aims to identify the technological and ethical risks in data-driven decision systems based on AI and conduct a diagnosis of these risks and their perception. To do that, we use a triangulation of methods. In the first stage, a search on Web of Science has been performed. We consider all the 412 papers. The second stage corresponds to a analysis of experts. The papers have been classified according to the relevance to the topic by the experts. In the third stage, we use the survey method and include risk insights from stage two in our questions. We found 24 concerns which arise from the perspective of the ethical and technological risk perspective. The perception of participants regarding the level of concern they have with the risks of a data-driven system based on AI is high than their perception of society's concern. Fairness is considered the risk whose perception is more severe. Fairness, Bias, Accountability, Interpretability, and Explainability are considered the most relevant concepts for a responsible AI. Consequently, also the most relevant for responsible governance of AI. © 2022 ACM.

Abstract
Multi-label stream classification is the task of classifying instances in two or more classes simultaneously, with instances flowing continuously in high speed. This task imposes difficult challenges, such as the detection of concept drifts, where the distributions of the instances in the stream change with time, and infinitely delayed labels, when the ground truth labels of the instances are never available to help updating the classifiers. To solve such task, the methods from the literature use the problem transformation approach, which divides the multi-label problem into different sub-problems, associating one classification model for each class. In this paper, we propose a method based on self-organizing maps that, different from the literature, uses only one model to deal with all classes simultaneously. By using the algorithm adaptation approach, our proposal better considers label dependencies, improving the results over its counterparts. Experiments using different synthetic and real-world datasets showed that our proposal obtained the overall best performance when compared to different methods from the literature.

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The high levels of degradation characteristic of ancient DNA molecules severely hinder the recovery of endogenous DNA fragments and the discovery of genetic variation, limiting downstream population analyses. Optimization of read mapping strategies for ancient DNA is therefore essential to maximize the information we are able to recover from archaeological specimens. In this paper we assess Burrows-Wheeler Aligner (BWA) effectiveness for mapping of ancient DNA sequence data, comparing different sets of parameters and their effect on the number of endogenous sequences mapped and variants called. We also consider different filtering options for SNP calling, which include minimum values for depth of coverage and base quality in addition to mapping quality. Considering our results, as well as those of previous studies, we conclude that BWA-MEM is a good alternative to the current standard BWA-backtrack strategy for ancient DNA studies, especially when the computational resources are limited and time is a constraint.