Doxastic distinctions help us define the basis and biases in belief–formation. Empirical and extra-empirical justification play an important role in determining doxastic distinctions. When we distinguish the different types of beliefs, we understand that there are basically three kinds of beliefs, namely, verifiable, falsifiable, and unfalsifiable beliefs. Empirical justification provides the basis for establishing the veracity of verifiable and falsifiable beliefs. Extra-empirical justification provides the basis for establishing the veracity of unfalsifiable or irrefutable beliefs. Verifiable or falsifiable beliefs that are (...) reductive require the mandatory acceptance of their truth. However, unfalsifiable beliefs which are non-reductive require the volitional acceptance of their truth. Because there is both empirical and extra-empirical justification in belief-formation, we can accept beliefs with or without, against, or regardless of empirical evidence. Unfalsifiable beliefs do not mean that these beliefs are unjustifiable; it simply means that these beliefs are not empirically justifiable. Understanding the basis and biases of belief-formation is to be aware of how we come to know what we believe. As empirical basis allows us to hold beliefs based on either the principle of confirmation or falsification. Extra-empirical basis, namely phenomenological conjectures, allows us to hold beliefs founded on existential assertions. The intent of this paper is to present doxastic distinctions to help us understand the basis and biases associated with belief-formation. As long as epistemic claims are accepted, and their content is considered believable, the means used to arrive at those beliefs must also be respected. Every doxastic distinction not only provides the basis for belief-formation but also defines the role and limits of both empirical and extra-empirical justification in belief-formation. When we recognize the different types and different ways of justifying beliefs: we understand why we can accept beliefs with or without evidence; against or regardless of empirical justification, and know when to define beliefs as Plato argued as justified true belief and when to define beliefs as justified belief-decisions. Keywords: beliefs, justified true belief; justified belief-decisions, empirical and extra-empirical. (shrink)

The digital transfer of the mind to a computer system requires representing the mind as a finite sequence of bits. The classic “stored-program computer” paradigm, in turn, implies the equivalence between program and data, so that the sequence of bits themselves can be interpreted as a program, which will be algorithmically executed in the receiving device. Now, according to a previous proof, on which this paper is based, a computational or algorithmic machine, however complex, cannot be free. Consequently, a finite (...) sequence of bits cannot adequately represent a free mind and, therefore, a free mind cannot be digitally transferred, quod erat demonstrandum. The impossibility of making this transfer, as demonstrated here, should be a concern especially for those who wish to achieve it. Since we intend this to be a rigorous demonstration, we must give precise definitions and conditions of validity. The most important part of the paper is devoted to explaining the meaning and reasonableness of these definitions and conditions. Special attention is paid, also, to the philosophical implications of the demonstration. Finally, this thesis is distinguished from other closely related issues. (shrink)

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating… and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set (...) of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely “following a moral code”. In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society. (shrink)