Mark Graves

Advances in scientific study of the brain now enable the examination of nature and grace in human rationality’s embodiment in the brain’s biological processing. I model the brain’s biology using the dispositional tendencies of nature—characterized by Jonathan Edwards, C. S. Peirce, and the Jesuit philosophical theologian Donald Gelpi—to examine gracing of the mind’s habit formation in terms of memory, learning, and decision making. This turn to tendency suggests a shift from understanding soul as Aristotelian act to instead emphasizing the potentiality informing the body and clarifies a scientifically plausible Rahnerian interpretation of obediential potency

As artificial intelligence (AI) becomes progressively more engaged with society, its shift from technical tool to participating in society raises questions about AI personhood. Drawing upon developmental psychology and systems theory, a mediating structure for AI proto-personhood is defined analogous to an early stage of human development. The proposed AI bridges technical, psychological, and theological perspectives on near-future AI and is structured by its hardware, software, computational, and sociotechnical systems through which it experiences its world as embodied (even for putatively disembodied AI). Further social and moral construction can occur building upon a simple “self” for AI synthesized from symbolic and statistical approaches to AI.

Practical wisdom enables moral decision-making and action by aligning one’s apprehension of proximate goods with a distal, socially embedded interpretation of a more ultimate Good. A focus on purpose within the overall process mutually informs human moral psychology and moral AI development in their examinations of practical wisdom. AI practical wisdom could ground an AI system’s apprehension of reality in a sociotechnical moral process committed to orienting AI development and action in light of a pluralistic, diverse interpretation of that Good. Defining AI purpose in terms of the human struggle to determine the moral “ought” (versus the way reality is) can overcome practical and normative limitations of principle-based approaches to AI ethics without requiring idiosyncratic moral reasoning or a premature commitment to a single ethical theory, which could hinder navigating the moral implications of future unknowns. Dividing practical wisdom into proximate, mediating, and distal processes enables each division to incorporate the moral dimensions of the situation, individual AI system, and sociotechnical community, respectively. Drawing upon externalist (4E) and ecological cognitive psychology theories to extend Xavier Zubiri’s sentient intelligence, proximate apprehension of what an object affords the perceiver includes affordances for practical and moral purposes. Those apprehensions are structured by moral and other social-cognitive schemas that mediate moral constructs, the apprehended situation, and individual commitments and other motivating identifications in a process Ignacio Ellacuria calls historical reality. Distally, Josiah Royce’s community of interpretation characterizes one way of organizing a sociotechnical process to synthesize diverse interpretations of the Good, orient AI moral practice, and align AI development with human and AI moral purpose.

The expanding social role and continued development of artificial intelligence (AI) needs theological investigation of its anthropological and moral potential. A pragmatic theological anthropology adapted for AI can characterize moral AI as experiencing its natural, social, and moral world through interpretations of its external reality as well as its self-reckoning. Systems theory can further structure insights into an AI social self that conceptualizes itself within Ignacio Ellacuria’s historical reality and its moral norms through Thomistic ideogenesis. This enables a conceptualization process capable of carrying moral weight and grounded in reality; structures the experience of an AI emergent self into multiple levels of interpretation; and drives a multi-level systems architecture for moral AI. Modeling AI’s interpretive experience and self-reckoning as a causal, sociotechnical, and moral actor can help moral AI identify conflicts between its normative values and develop the practical wisdom (phronesis) needed to apply its general moral models.

It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare as an example, attention to sociocultural conversion factors relevant to health can help in navigating technical choices that require broader considerations of the sociotechnical system, such as metric tradeoffs in model validation, resulting in better ethical and technical choices. This approach promotes awareness of the ethical dimension of technical choices by data scientists and others, and that can foster the cultivation of 'ethical skills' as integral to data science.

Sallie McFague’s theological models construct a tensive relationship between conceptual structures and symbolic, metaphorical language to interpret the defining and elusive aspects of theological phenomena and loci. Computational models of language can extend and formalize the conceptual structures of theological models to develop computer-augmented interpretations of theological texts. Previously unclear is whether computational models can retain the tensive symbolism essential for theological investigation. I demonstrate affirmatively by constructing a computational topic model of the moral theology of Thomas Aquinas from Summa Theologica (Second Part, in English translation) useful for interpreting not only the Thomistic text but also recent papal encyclicals.

Does science argue against the existence of the human soul? Many scientists and scholars believe the whole is more than the sum of the parts. This book uses information and systems theory to describe the "more" that does not reduce to the parts. One sees this in the synapses--or apparently empty gaps between the neurons in one's brain--where informative relationships give rise to human mind, culture, and spirituality. Drawing upon the disciplines of cognitive science, computer science, neuroscience, general systems theory, pragmatic philosophy, and Christian theology, Mark Graves reinterprets the traditional doctrine of the soul as form of the body to frame contemporary scientific study of the human soul.

Examining AI spirituality can illuminate problematic assumptions about human spirituality and AI cognition, suggest possible directions for AI development, reduce uncertainty about future AI, and yield a methodological lens sufficient to investigate human-AI sociotechnical interaction and morality. Incompatible philosophical assumptions about human spirituality and AI limit investigations of both and suggest a vast gulf between them. An emergentist approach can replace dualist assumptions about human spirituality and identify emergent behavior in AI computation to overcome overly reductionist assumptions about computation. Using general systems theory to organize models of human experience yields insight into human morality and spirituality, upon which AI modeling can also draw. In this context, the pragmatist Josiah Royce’s semiotic philosophy of spirituality identifies unanticipated overlap between symbolic AI and spirituality and suggests criteria for a human-AI community focused on modeling morality that would result in an emergent Interpreter-Spirit sufficient to influence the ongoing development of human and AI morality and spirituality.

Terrence Deacon has described three orders of emergence; Arthur Peacocke and others have suggested four levels of human systems and sciences; and Philip Clayton has postulated an additional, transcendent, level. Orders and levels describe distinct aspects of emergence, with orders characterizing topological complexity and levels characterizing theoretical knowledge and causal power. By using Deacon's orders to analyze and relate each of the four "lower" levels one can project that analysis on the transcendent level to gain insight into the teleodynamic emergence of transcendent-level systems. I argue that cross-cultural interactions among human cultural-level systems results in the emergence of the "universal" transcendental norms historically characterized as the Greek Good, Beauty, and Truth. These norms require a dynamic existence that I characterize as the emergence of Spirit, using Josiah Royce's community of interpretation, and that I suggest provides a pragmatic clarification of Clayton's transcendent level. An understanding of those emergent norms clarifies ethical systems, highlights the importance of aesthetics in understanding scientific systems, and suggests the necessity of community in fruitful science-and-religion dialogue on human systems.

Abstract.Cognitive science and religion provides perspectives on human cognition and spirituality. Emergent systems theory captures the subatomic, physical, biological, psychological, cultural, and transcendent relationships that constitute the human person. C. S. Peirce's metaphysical categories and existential graphs enrich traditional cognitive science modeling tools to capture emergent phenomena. From this richer perspective, one can reinterpret the traditional doctrine of soul as form of the body in terms of information as the constellation of constitutive relationships that enables real possibility.

Almost all admit that there is beauty in the natural world. Many suspect that such beauty is more than an adornment of nature. Few in our contemporary world suggest that this beauty is an empirical principle of the natural world itself and instead relegate beauty to the eye and mind of the beholder. Guided by theological and scientific insight, the authors propose that such exclusion is no longer tenable, at least in the data of modern biology and in our view of the natural world in general. More important, we believe an empirical aesthetics exists that can help guide experimental design and development of computational models in biology. Moreover, because theology and science can both contribute toward and equally profit from such an aesthetics, we propose that this empirical aesthetics provides the foundation for a living synergy between theology and science.

In the past few years, the ethical ramifications of AI technologies have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue theory tradition, in this paper, we formulate a different approach to ethics in data science which is based on a different conception of “being ethical” and, ultimately, of what it means to promote a morally responsible data science. First, we develop the idea that, rather than only compliance, ethical decision-making consists in using certain moral abilities, which are cultivated by practicing and exercising them in the data science process. An aspect of virtue development that we discuss here is moral attention, which is the ability of data scientists to identify the ethical relevance of their own technical decisions in data science activities. Next, by elaborating on the capability approach, we define a technical act as ethically relevant when it impacts one or more of the basic human capabilities of data subjects. Therefore, rather than “applying ethics”, data scientists should cultivate ethics as a form of reflection on how technical choices and ethical impacts shape one another. Finally, we show how this microethical framework concretely works, by dissecting the ethical dimension of the technical procedures involved in data understanding and preparation of electronic health records.

Our research project will investigate the virtue of humility among real world humanitarian exemplars, such as holocaust rescuers and hospice workers. We will use computer technology to analyze interviews with these types of populations to understand the different factors involved in the virtue of humility. Following the work of Aristotle, we believe this virtue is formed as a kind of habit that becomes a natural extension of one’s character. We aim to operationalize and empirically evaluate aspects of the virtue of humility through the computational analysis of implicit semantic processing embedded in the narratives of real world humanitarians. Latent semantic analysis, multi-dimensional scaling, and hierarchical cluster analysis will be used to map self-understanding schemas in these populations and its role in humility while serving others.

In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools to be interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London’s position. In particular, we make two claims. First, we claim that London’s solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London’s views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like.