How have online communities affected the ways their users construct, view, and define their identity? In this paper, we will approach this issue by considering two philosophical sets of problems related to personal identity: the “Characterization Question” and the “Self-Other Relations Question.” Since these queries have traditionally brought out different problems around the concept of identity, here we aim at rethinking them in the framework of online communities. To do so, we will adopt an externalist and cognitive point of view (...) on online communities, describing them as virtual cognitive niches. We will evaluate and agree with the Attachment Theory of Identity, arguing that there is continuity between offline and online identity and that usually the latter contributes to the alteration of the former. Finally, we will discuss ways users can enact self-reflection on online frameworks, considering the impact of the Filter Bubble and the condition of Bad Faith. (shrink)

An amendment to this paper has been published and can be accessed via the original article.

In philosophical logic and metaphysics there is a long-standing debate around the most appropriate structures to represent indeterministic scenarios concerning the future. We reconstruct here such a debate in a computational setting, focusing on the fundamental difference between moment-based and history-based structures. Our presentation is centered around two versions of an indeterministic scenario in which a programmer wants a machine to perform a given task at some point after a specified time. One of the two versions includes an assumption about (...) the future behaviour of the machine that cannot be encoded in any programming instruction; such version has models over history-based structures but no model over a moment-based structure. Therefore, our work adds a new stance to the debate: moment-based structures can be said to rule out certain indeterministic scenarios that are computationally unfeasible. (shrink)

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example use (...) case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised. (shrink)

In current philosophy of information, different authors have been supporting the veridicality thesis. According to this thesis, an epistemically-oriented concept of information must have truth as one of its necessary conditions. Two challenges can be raised against VT. First, some philosophers object that veridicalists erroneously ignore the informativeness of false messages. Secondly, it is not clear whether VT can adequately explain the information considered in hypothetical reasoning. In this sense, logical diagrams offer an interesting case of analysis: by manipulating a (...) logical diagram we can verify that a certain conclusion follows from a set of premises, but it cannot help us to determine the actual truth-value of a given set of propositions. Focusing on the latter challenge, in this paper I claim that logical diagrams set out potential counterexamples to VT and, consequently, pose a real challenge to this thesis. First, a veridicalist analysis of logical diagrams requires the assumption of metatheoretical properties which are not satisfied by some logical systems. So, VT does not fit well as a general framework for a theory of logical diagrammatic information. Secondly, based on semantic inferentialism, one can propose a normative interpretation of the inferential content of logical diagrams not exposed to the problems faced by VT. Moreover, there are several reasons to believe that veridicalism cannot accommodate such a normative interpretation. In other words, normativism represents a real alternative to veridicality. Due to these reasons, I conclude that, until further research, we should adopt a more parsimonious standpoint and say that logical diagrams provide inferential information simply. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly work as computational (...) models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...) to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an “assembly language” for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems. (shrink)

Artificial intelligence is becoming increasingly entwined with our daily lives: AIs work as assistants through our phones, control our vehicles, and navigate our vacuums. As these objects become more complex and work within our societies in ways that affect our well-being, there is a growing demand for machine ethics—we want a guarantee that the various automata in our lives will behave in a way that minimizes the amount of harm they create. Though many technologies exist as moral artifacts, the development (...) of a truly ethical AI system is highly contentious; theorists have proposed and critiqued countless possibilities for programming these agents to become ethical. Many of these arguments, however, presuppose the possibility that an artificially intelligent system can actually be ethical. In this essay, I will explore a potential path to AI ethics by considering the role of imagination in the deliberative process via the work of John Dewey and his interpreters, showcasing one form of reinforcement learning that mimics imaginative deliberation. With these components in place, I contend that such an artificial agent is capable of something very near ethical behavior—close enough that we may consider it so. (shrink)

Accountability and responsibility are key concepts in the academic and societal debate on Autonomous Weapon Systems, but these notions are often used as high-level overarching constructs and are not operationalised to be useful in practice. “Meaningful Human Control” is often mentioned as a requirement for the deployment of Autonomous Weapon Systems, but a common definition of what this notion means in practice, and a clear understanding of its relation with responsibility and accountability is also lacking. In this paper, we present (...) a definition of these concepts and describe the relations between accountability, responsibility, control and oversight in order to show how these notions are distinct but also connected. We focus on accountability as a particular form of responsibility—the obligation to explain one’s action to a forum—and we present three ways in which the introduction of Autonomous Weapon Systems may create “accountability gaps”. We propose a Framework for Comprehensive Human Oversight based on an engineering, socio-technical and governance perspective on control. Our main claim is that combining the control mechanisms at technical, socio-technical and governance levels will lead to comprehensive human oversight over Autonomous Weapon Systems which may ensure solid controllability and accountability for the behaviour of Autonomous Weapon Systems. Finally, we give an overview of the military control instruments that are currently used in the Netherlands and show the applicability of the comprehensive human oversight Framework to Autonomous Weapon Systems. Our analysis reveals two main gaps in the current control mechanisms as applied to Autonomous Weapon Systems. We have identified three first options as future work for the design of a control mechanism, one in the technological layer, one in the socio-technical layer and one the governance layer, in order to achieve comprehensive human oversight and ensure accountability over Autonomous Weapon Systems. (shrink)