Big Data, Information, and Ethical Issues in Artificial Reasoning
In my lectures, I will present, initially, topics of information theory grounded on the hypotheses of (a) Shannon & Weaver’s Mathematical Theory of Communication; (b) Dretske’s Knowledge and the Flow of Information; (c) Peirce’s semiotic approach to information; and (d) principles of information processing in complex decentralized systems, proposed by Mitchell in her paper “Complex Systems: Network Thinking”. In the second part of the lectures, emphasis is going to be given to considerations on ethical issues related to uses of Big Data analysis and Artificial Intelligence modelling in contemporary science. The following question will guide the lectures: It is well known that statistical dependency does not necessarily imply temporal causality, but is there a need for a renewed ethical framework to legitimize the widening of the science frontiers of what is technically acceptable? Considering the volume, velocity, and variety characteristics of Big Data, among others, there is no guarantee that spurious correlations are not present in the form of false statistical dependence. We claim that, in Big Data-based scientific research, given the volume of unstructured data and its automated analytical use, considerations of possible ethical consequences could be investigated by means of abductive reasoning and hypothetical counterfactual inferences. These kinds of inferences might help in discovering and exposing the possible oral implications that the pursuit and completion of research projects grounded upon artificial reasoning could bring to civil society.