Andrea Berber

The intention of this paper is to point to the dilemma humanity may face in light of AI advancements. The dilemma is whether to create a world with less evil or maintain the human status of moral agents. This dilemma may arise as a consequence of using automated decision-making systems for high-stakes decisions. The use of automated decision-making bears the risk of eliminating human moral agency and autonomy and reducing humans to mere moral patients. On the other hand, it also has the potential to bring tremendous benefits to humanity by decreasing human-induced harm in the world. After presenting how this dilemma may arise, I explore general avenues for addressing it. I will argue that we do not have to solve this dilemma in an all-or-nothing fashion and that a more nuanced approach may be suitable. However, the main point I want to highlight is that we need to have a principled way of addressing this dilemma, which is currently missing.

Because of its practical advantages, machine learning (ML) is increasingly used for decision-making in numerous sectors. This paper demonstrates that the integral characteristics of ML, such as semi-autonomy, complexity, and non-deterministic modeling have important ethical implications. In particular, these characteristics lead to a lack of insight and lack of comprehensibility, and ultimately to the loss of human control over decision-making. Errors, which are bound to occur in any decision-making process, may lead to great harm and human rights violations. It is important to have a principled way of assigning responsibility for such errors. The integral characteristics of ML, however, pose serious difficulties in defining responsibility and regulating ML decision-making. First, we elaborate on these characteristics and their epistemic and ethical implications. We then analyze possible general strategies for resolving the assignment of moral responsibility and show that, due to the specific way in which ML functions, each potential solution is problematic, whether we assign responsibility to humans, machines, or using hybrid models. Then, we shift focus on an alternative approach that bypasses moral responsibility and attempts to define legal liability independently through solutions such as informed consent and the no-fault compensation system. Both of these solutions prove unsatisfactory because they leave too much room for potential abuses of ML decision-making. We conclude that both ethical and legal solutions are fraught with serious difficulties. These difficulties prompt us to re-weigh the costs and benefits of using ML for high-stake decisions.

The aim of this paper is to show that the nascent field of ethics of collegiality may considerably benefit from a symbiosis with virtue and vice epistemology. We start by bringing the epistemic virtue and vice perspective to the table by showing that competence, deemed as an essential characteristic of a good colleague (Betzler & Löschke 2021), should be construed broadly to encompass epistemic competence. By endorsing the anti-individualistic stance in epistemology as well as context-specificity of epistemic traits, we show how the individual vice of a colleague can have a positive epistemic outcome for the team while individual virtue can be damaging if all team members share it, thereby contributing to the negative epistemic outcome. We argue that the evaluation and analysis of collegial relationships should be done through one’s contribution to the team dynamics: without this collectivist perspective, the ethics of collegiality cannot aspire to become encompassing normative theory.

In this paper, we consider the relative significance of concrete and abstract features for the identity and persistence of a group. The theoretical background for our analysis is the position according to which groups are realizations of structures. Our main argument is that the relative significance of the abstract features with respect to the significance of concrete features can vary across different types of groups. The argumentation will be backed by introducing the examples in which we show that this difference in significance can affect the identity and persistence of the group.

Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what it is specifically that makes ML methodology substantially unsuitable for reaching explanations. The analysis is given broader philosophical context by connecting it with the views on the role of prediction and explanation in science, their relationship, and the value of explanation. We proceed to show, first, that ML disrupts the relationship between prediction and explanation commonly understood as a functional relationship. Then we show that the value of explanation is not exhausted in purely predictive functions, but rather has a ubiquitously recognized value for both science and everyday life. We then invoke two hypothetical scenarios with different degrees of automatization of science, which help test our intuitions on the role of explanation in science. The main question we address is whether ML will reorient or otherwise impact our standard explanatory practice. We conclude with a prognosis that ML would diversify science into purely predictively oriented research based on ML-like techniques and, on the other hand, remaining faithful to anthropocentric research focused on the search for explanation.

This paper intends to critically consider the idea put forward by Johan Gamper that the principle of causal closure can be reconciled with the possibility of pluralism. This idea is based on redefining causal closure and on the introduction of so-called interfaces between the universes. By reconstructing and analyzing the author's argumentative steps, we will try to show that this approach is methodologically and explanatory unfounded. Firstly, this way of redefining the principle of causal closure is inconsistent with the very reasons why this principle was introduced. Secondly, the proposed view does not bring the explanatory benefits it promises, that is, it cannot solve difficulties such as the problem of the first cause. Moreover, the position itself produces many additional concerns and perplexities that remain unresolved. The conclusion of our analysis is that certain aspects of the proposed position may be sustainable and plausible, but only if novel arguments should be offered.

The purpose of the present paper is to demonstrate the inconsistencies between ethical theory and legal practice of animal treatment. Specifically, we discuss contemporary legal solutions, based on three case studies – Serbian, German and UK positive law, and point out the inconsistencies in them. Moreover, we show that the main cause of these inconsistencies is anthropocentric view of moral relevance. Finally, when it comes to the different treatment of animals living in the wild and domestic animals, we show that the current theoretical explanations are unsatisfactory.

We employ data envelopment analysis on a series of experiments performed in Fermilab, one of the major high-energy physics laboratories in the world, in order to test their efficiency (as measured by publication and citation rates) in terms of variations of team size, number of teams per experiment, and completion time. We present the results and analyze them, focusing in particular on inherent connections between quantitative team composition and diversity, and discuss them in relation to other factors contributing to scientific production in a wider sense. Our results concur with the results of other studies across the sciences showing that smaller research teams are more productive, and with the conjecture on curvilinear dependence of team size and efficiency.