Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

The exponential growth of AI and its applications in different areas of society, such as the financial, agricultural, telecommunications, or health sectors, poses new challenges for the government's public sector, mainly in regulating these systems. Governments and entities in general address these challenges by formulating soft laws such as manuals or guidelines. They seek full transparency, privacy, and bias reduction when implementing an AI-based system, including its life cycle and respective data management or governance. These tools and documents aim to develop an ethical AI that addresses or solves the aforementioned ethical implications. The revision of 22 documents within frameworks, guides, articles, toolkits, and manuals proposed by different governments and entities are examined in detail. Analyses include a general summary, the main objective, characteristics to be highlighted, advantages and disadvantages if any, and possible improvements.