That it is necessary to collect a greater quantity and variety of Last Database data; primarily demographic data such as age, biological sex and gender, race and ethnicity, and geographic area of residence. In this sense, it is important to note that, although the Last Database name and surname of the owner are not included, these data, combined with health data, can allow them to be identified –even despite going through data dissociation, anonymization or processes–, and this, in addition to Last Database violating the privacy and intimacy of a person, can have discriminatory effects.
Some of the risks linked to the data used for these systems are Last Database security problems (computer and information), violation of privacy and arbitrary discrimination (known as algorithmic discrimination). In turn, the possible violation of privacy that can emerge from the identification of a person can lead to discrimination based on their health status, as observed in the Last Database report of the National Institute against Discrimination (inadi ) from Argentina referring to the queries received by that body during the first two months of social, preventive and Last Database mandatory isolation ( aspo ), between March and May 202014.
This could be reflected in the increase in the value of the health insurance Last Database premium, in the difficulty in obtaining a bank loan or in finding a job, among other possible consequences.
Challenges for a trustworthy and privacy-respecting artificial intelligence
In a scenario of population growth and aging, the Last Database need to attend to the health of the population as quickly as possible becomes a priority that seems to be resolved, to a greater or lesser extent, through the design and application of new technologies that allow Last Database automate processes that today are carried out manually and in person. The challenges, however, are many and diverse. On the one hand, the need to reduce biases so that the implementation of these techniques does not deepen.