Travis LaCroix

Written for an interdisciplinary audience, this book provides strikingly clear explanations of the many difficult technical and moral concepts central to discussions of ethics and AI. In particular, it serves as an introduction to the value alignment problem: that of ensuring that AI systems are aligned with the values of humanity. LaCroix redefines the problem as a structural one, showing the reader how various topics in AI ethics, from bias and fairness to transparency and opacity, can be understood as instances of the key problem of value alignment. Numerous case studies are presented throughout the book to highlight the significance of the issues at stake and to clarify the central role of the value alignment problem in the many ethical challenges facing the development and implementation of AI.

Several anecdotal claims about the relationship between philosophical discourse and the subject of autism have been forwarded in recent years. This paper seeks to verify or debunk these descriptive claims by carefully examining the philosophical literature on autism. We conduct a comprehensive scoping review to answer the question, what do philosophers talk about when they talk about autism? This empirical work confirms that the philosophy of autism is underdeveloped as a subfield of philosophy. Moreover, the way that philosophers engage with autism is often unreflective and uncritical. As a result, much work in the discipline serves to perpetuate pathologising, dehumanising, and stigmatising misinformation about autistics and autistic behaviour. By highlighting the significant gaps in the philosophical literature on autism, this review aims to deepen our understanding of philosophical thought surrounding autism and contributes to ongoing dialogues pertaining to neurodiversity, madness, and disability rights more generally.

Written for an interdisciplinary audience, this book provides strikingly clear explanations of the many difficult technical and moral concepts central to discussions of ethics and AI. In particular, it serves as an introduction to the value alignment problem: that of ensuring that AI systems are aligned with the values of humanity. LaCroix redefines the problem as a structural one, showing the reader how various topics in AI ethics, from bias and fairness to transparency and opacity, can be understood as instances of the key problem of value alignment. Numerous case studies are presented throughout the book to highlight the significance of the issues at stake and to clarify the central role of the value alignment problem in the many ethical challenges facing the development and implementation of AI.

We use tools from evolutionary game theory to examine how power might influence the cultural evolution of inequitable conventions between discernible groups (such as gender or racial groups) in a population of otherwise identical individuals. Similar extant models always assume that power is homogeneous across a social group. As such, these models fail to capture situations where individuals who are not themselves disempowered nonetheless end up disadvantaged in bargaining scenarios by dint of their social group membership. Our models show that even when most individuals in two discernible sub-groups are relevantly identical, powerful individuals can affect the social outcomes for their entire group under a range of conditions; this results in power by association for their in-group and a bargaining disadvantage for their out-group.

The value alignment problem for artificial intelligence (AI) asks how we can ensure that the “values”—i.e., objective functions—of artificial systems are aligned with the values of humanity. In this paper, I argue that linguistic communication is a necessary condition for robust value alignment. I discuss the consequences that the truth of this claim would have for research programmes that attempt to ensure value alignment for AI systems—or, more loftily, those programmes that seek to design robustly beneficial or ethical artificial agents.

Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson’s (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing that we call “cultural smothering.” By leaving the sociodynamic features of cultural code-switching unaddressed, AI systems risk negatively impacting already-marginalised social groups by widening opportunity gaps and further entrenching social inequalities.

We use tools from evolutionary game theory to examine how power might influence the cultural evolution of inequitable conventions between discernible groups (such as gender or racial groups) in a population of otherwise identical individuals. Similar extant models always assume that power is homogeneous across a social group. As such, these models fail to capture situations where individuals who are not themselves disempowered nonetheless end up disadvantaged in bargaining scenarios by dint of their social group membership. Our models show that even when most individuals in two discernible sub-groups are relevantly identical, powerful individuals can affect the social outcomes for their entire group under a range of conditions; this results in power by association for their in-group and a bargaining disadvantage for their out-group.

Policy and guideline proposals for ethical artificial intelligence research have proliferated in recent years. These are supposed to guide the socially-responsible development of AI for a common good. However, there typically exist incentives for non-cooperation ; and, these proposals often lack effective mechanisms to enforce their own normative claims. The situation just described constitutes a social dilemma—namely, a situation where no one has an individual incentive to cooperate, though mutual cooperation would lead to the best outcome for all involved. In this paper, we use stochastic evolutionary game dynamics to model this social dilemma in the context of the ethical development of artificial intelligence. This formalism allows us to isolate variables that may be intervened upon, thus providing actionable suggestions for increased cooperation amongst numerous stakeholders in AI. Our results show how stochastic effects can help make cooperation viable in such a scenario. They suggest that coordination for a common good should be attempted in smaller groups in which the cost of cooperation is low, and the perceived risk of failure is high. This provides insight into the conditions under which we should expect such ethics proposals to be successful with regard to their scope, scale, and content.

Researchers of language origins typically try to explain how compositional communication might evolve to bridge the gap between animal communication and natural language. However, as an explanatory target, compositionality has been shown to be problematic for a gradualist approach to the evolution of language. In this article, I suggest that reflexivity provides an apt and plausible alternative target that does not succumb to the problems that compositionality faces. I further explain how protoreflexivity, which depends on functional reference, gives rise to complex communication systems via modular composition.

We are concerned here with how structural properties of language may come to reflect features of the world in which it evolves. As a concrete example, we will consider how a simple term language might evolve to support the principle of indifference over state descriptions in that language. The point is not that one is justified in applying the principle of indifference to state descriptions in natural language. Instead, it is that one should expect a language that has evolved in the context of facilitating successful action to reflect probabilistic features of the world in which it evolved.

I consider how complex logical operations might self-assemble in a signalling-game context via composition of simpler underlying dispositions. On the one hand, agents may take advantage of pre-evolved dispositions; on the other hand, they may co-evolve dispositions as they simultaneously learn to combine them to display more complex behaviour. In either case, the evolution of complex logical operations can be more efficient than evolving such capacities from scratch. Showing how complex phenomena like these might evolve provides an additional path to the possibility of evolving more or less rich notions of compositionality. This helps provide another facet of the evolutionary story of how sufficiently rich, human-level cognitive or linguistic capacities may arise from simpler precursors. 1Signalling and Self-assembly2Simple Unary Logic Games3Composing Unary Functions for Binary Inputs 3.1Utilizing pre-evolved dispositions3.2Co-evolving logical dispositions3.3Learning appropriate outputs3.4Taking account of the full state-space of unary games3.5Role-free composition4Discussion 4.1Efficacy and efficiency of learning complex dispositions4.2Other binary operations4.3To infinity and beyond5Conclusion

This paper applies the theoretical criteria laid out by D’Arms et al. to various aspects of evolutionary models of signalling. The question that D’Arms et al. seek to answer can be formulated as follows: Are the models that we use to explain the phenomena in question conceptually adequate? The conceptual adequacy question relates the formal aspects of the model to those aspects of the natural world that the model is supposed to capture. Moreover, this paper extends the analysis of D’Arms et al. by asking the following additional question: Are the models that we use sufficient to explain the phenomena in question? The sufficiency question asks what formal resources are minimally required for the model to get the right results most of the time.

We use tools from evolutionary game theory to examine how power might influence the cultural evolution of inequitable norms between discernible groups in a population of otherwise identical individuals. Similar extant models always assume that power is homogeneous across a social group. As such, these models fail to capture situations where individuals who are not themselves disempowered nonetheless end up disadvantaged in bargaining scenarios by dint of their social group membership. Thus, we assume that there is heterogeneity in the groups in that some individuals are more powerful than others. Our model shows that even when most individuals in two discernible sub-groups are relevantly identical, powerful individuals can affect the social outcomes for their entire group; this results in power by association for their in-group and a bargaining disadvantage for their out-group. In addition, we observe scenarios like those described where individuals who are more powerful will get less in a bargaining scenario because a convention has emerged disadvantaging their social group.

I introduce an extension of the Lewis-Skyrms signaling game, analysed from a dynamical perspective via simple reinforcement learning. In Lewis’ (Convention, Blackwell, Oxford, 1969) conception of a signaling game, salience is offered as an explanation for how individuals may come to agree upon a linguistic convention. Skyrms (Signals: evolution, learning & information, Oxford University Press, Oxford, 2010a) offers a dynamic explanation of how signaling conventions might arise presupposing no salience whatsoever. The extension of the atomic signaling game examined here—which I will refer to as a salience game—introduces a variable parameter into the atomic signaling game which allows for degrees of salience, thus filling in the continuum between Skyrms’ and Lewis’ models. The model does not presuppose any salience at the outset, but illustrates a process by which accidentally evolved salience is amplified, to the benefit of the players. It is shown that increasing degrees of salience allow populations to avoid sub-optimal pooling equilibria and to coordinate upon conventions more quickly.