Questioning the ethical reasoning behind ways of attributing value to lives impacts philosophical dilemmas encountered in policy making and innovation in AI. For instance, this sort of reasoning requires us to determine how self-driving cars should behave when encountering real-life dilemmas such as inevitably crashing into one person as opposed to a group of people. -/- This issue will be examined with the Rocks Case, a case of conflict of interest where all the relevant parties are strangers, and we can (...) either save one person or a group of five. The two courses of action which will be discussed in this paper are: 1) “Ought to Save the Many” (OSM) and 2) “Permitted to Save the Few” (PSF). -/- The position of PSF using a weighted lottery will be argued for, rather than acting according to utilitarian or contractualist OSM reasoning, on the grounds that it would give all persons involved a fair opportunity of survival. This is judged to be a more important criterion than merely maximising the number of people to rescue. (shrink)
This essay aims to evaluate each element of the Kalam Cosmological Argument. I first examine premise (2) and one popular objection to it. I then do the same for premise (1). Second, I propose an original objection to premise (1), and then refute the objection. In my self-refutation, I share two insights into the nature of philosophical inquiry. I finish with an overview of the Argument’s conclusion.
It is a common intuition in scientific practice that positive instances confirm. This confirmation, at least based purely on syntactic considerations, is what Nelson Goodman’s ‘Grue Problem’, and more generally the ‘New Riddle’ of Induction, attempt to defeat. One treatment of the Grue Problem has been made along Bayesian lines, wherein the riddle reduces to a question of probability assignments. In this paper, I consider this so-called Bayesian Grue Problem and evaluate how one might proffer a solution to this problem (...) utilizing what I call a phenomenological approach. I argue that this approach to the problem can be successful on the Bayesian framework. (shrink)