This paper discusses some philosophical aspects related to the recent publication of the experimental results of the 2017 black hole experiment, namely the first image of the supermassive black hole at the center of galaxy M87. In this paper I present a philosophical analysis of the 2017 Event Horizon Telescope (EHT) black hole experiment. I first present Hacking’s philosophy of experimentation. Hacking gives his taxonomy of elements of laboratory science and distinguishes a list of elements. I show that the EHT (...) experiment conforms to major elements from Hacking’s list. I then describe with the help of Galison’s Philosophy of the Shadow how the EHT Collaboration created the famous black hole image. Galison outlines three stages for the reconstruction of the black hole image: Socio-Epistemology, Mechanical Objectivity, after which there is an additional SocioEpistemology stage. I subsequently present my own interpretation of the reconstruction of the black hole image and I discuss model fitting to data. I suggest that the main method used by the EHT Collaboration to assure trust in the results of the EHT experiment is what philosophers call the Argument from Coincidence. I show that using this method for the above purpose is problematic. I present two versions of the Argument from Coincidence: Hacking’s Coincidence and Cartwright’s Reproducibility by which I analyse the EHT experiment. The same estimation of the mass of the black hole is reproduced in four different procedures. The EHT Collaboration concludes: the value we have converged upon is robust. I analyse the mass measurements of the black hole with the help of Cartwright’s notion of robustness. I show that the EHT Collaboration construe Coincidence/Reproducibility as Technological Agnosticism and I contrast this interpretation with van Fraassen’s scientific agnosticism. (shrink)

Replies to commentaries on my 2012 article ‘Synthetic Biology Needs a Synthetic Bioethics,’ note that I do not, in fact, call for some ‘new’ kind of ethics. The focus then and now is on integrating questions that relate to distributive justice and environmental quality more faithfully into the topics that have come to preoccupy mainstream bioethics as institutionalized in medical schools and medical research institutions.

The classic approach to science is dominated by the belief that science is a form of cognitive activity that focuses on constructing theories to describe and explain the phenomena and processes found in the world. Due to the fulfilment of the criteria of intersubjective communicability and controllability, theories are considered to be objective products of research activity that do not bear social responsibility for their applications. In this paper, the issue of social responsibility of science is addressed both from the (...) classic perspective and from the non-classic viewpoint represented by laboratory sciences, i.e. those sciences that are predominantly concerned with creating phenomena rather than discovering them. It is argued that whereas in the former case the problem of social responsibility of science does not provoke serious controversy, in the latter case such controversies do exist and, furthermore, they are justified. (shrink)

This article sketches the main features of traditional philosophical models of evidence, indicating idealizations in such models that it regards as doing more harm than good. It then proceeds to elaborate on an alternative model of evidence that is functionalist, complex, dynamic, and contextual, a view the author calls dynamic evidential functionalism (DEF). This alternative builds on insights from philosophy of scientific practice, Kuhnian philosophy of science, pragmatist epistemology, philosophy of experimentation, and functionalist philosophy of mind. Along the way, the (...) article raises concerns about the total evidence condition, requirements of certainty or incorrigibility on evidence, and accounts that restrict the type of things that can serve as evidence (to, for example, sense data, facts about particulars). DEF can also help us see the special value of novel predictions and experiments as evidence, as well as help us think about how to critically evaluate the putative evidence to determine whether it is evidence. (shrink)