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AI’s growing role in finance challenges traditional expectations of transparency and theoretical understanding. While machine learning (ML) models enhance financial decision-making, they remain largely agnostic to established financial theories, producing knowledge and ignorance in ways that differ from traditional models like VaR, DCF, and Black-Scholes. This essay explores the decoupling of AI models from theoretical financial knowledge and the resulting forms of ignorance. Using 22 semi-structured interviews, we investigate how ML models generate epistemic uncertainties. We focus on causal ignorance: AI systems, including those supported by XAI, fail to provide genuine causal explanations. Because understanding causation is inherently theoretical , AI-driven finance remains theory-agnostic and marked by theoretical ignorance. We explore how this ignorance differs from that of traditional models and what it implies for the role of theory in finance. Finally, we present three possible scenarios for the future of theory in finance and outline directions for further research.

Is there a unique way to de-idealize models? If not, how might the possible ways of reducing the distortion between models and reality differ from each other? Based on an empirical case study conducted in financial markets, this paper discusses how a popular valuation model (the Discounted Cash Flow model) idealizes reality and how the market participants de-idealize it in concrete market situations. In contrast to Cartwright's view that economic models are generally over-constrained, this paper suggests that valuation models are under-constrained. This serves as the reason why the relaxation of simplifying assumptions and concretization do not work as methods of de-idealization. The paper finds that financial market participants de-idealize models using commentary that takes the form of judgment. As a conclusion, a hypothesis is formulated that proposes that the more underdetermined the model is the bigger role narrative and other pragmatic elements play in the process of model application.

In recent years, research on modeling in both the philosophy of science and the social studies of science and technology has undergone an acute transformation. Philosophers and social scientists have begun to realize that science, in the words of Carrier and Nordmann, has increasingly shifted its focus from “epistemic or truth-oriented” research to “application-dominated” research. “Science is viewed today as an essentially practical endeavor” (Carrier and Nordmann 2011, 1) and should be considered in the context of its application. In accordance with this re-orienting of science, research on modeling has also changed. Still considering models as genuinely scientific tools, philosophers and social scientists promoted the “practice turn” that suggests a sharper focus on pragmatic issues and the performative and productive role of modeling. Application of models for the resolution of practice-related problems is viewed as an extension of science.

Purpose The purpose of this paper is to report the outcome of an interdisciplinary discussion on the concepts of profit and profitability and various ways in which we could potentially problematize these concepts. It is our hope that a much greater attention or reconsideration of the problematization of profit and related accounting numbers will be fostered in part by the exchanges we include here. Design/methodology/approach This paper adopts an interdisciplinary discussion approach and brings into conversation ideas and views of several scholars on problematizing profit and profitability in various contexts and explores potential implications of such problematization. Findings Profit and profitability measures make invisible the collective endeavour of people who work hard (backstage) to achieve a desired profit level for a division and/or an organization. Profit tends to preclude the social process of debate around contradictions among the ends and means of collective activity. An inherent message that we can discern from our contributors is the typical failure of managers to appreciate the value of critical theory and interpretive research for them. Practitioners and positivist researchers seem to be so influenced by neo-liberal economic ideas that organizations are distrusted and at times reviled in their attachment to profit. Research limitations/implications Problematizing opens-up the potential for interesting and significant theoretical insights. A much greater pragmatic and theoretical reconsideration of profit and profitability will be fostered by the exchanges we include here. Originality/value In setting out a future research agenda, this paper fosters theoretical and methodological pluralism in the research community focussing on problematizing profit and profitability in various settings. The discussion perspectives offered in this paper provides not only a basis for further research in this critical area of discourse and regulation on the role and status of profit and profitability but also emancipatory potential for practitioners (to be reflective of their practices and their undesired consequences of such practices) whose overarching focus is on these accounting numbers.

The “unplugged” section seeks to experience new forms of book reviews. We regularly grant a wild card to a world-class scholar to review his/her own Classic. In “My own book review”, authors will tell us the story of \"what I was trying to do\" with sometimes some auto-ethnographic considerations. By recounting the building process of one seminal research with a contemporary lens, they may give some insights for the current craft of research and also share with us renunciations, doubts and joys in their intimate writing experience.

Based on the study of counterfactual thinking in financial markets, the paper suggests a deviation from the standard definition of counterfactuals. Social psychology traditionally defines counterfactual thinking as the development of alternative versions of the past events. Two enhancements of this understanding are suggested in the paper: the contradicted facts are related to the future and are socially constructed. Thus, counterfactual thinking should be investigated not only as a common feature of the human mentality but also as an element of social life. It is an instrument to cope with the complexity of future events in social settings.

The chapter presents two modes of critical discussions about financial models. One mode refers to criticizing finance as a science involved in developing and spreading unrealistic models that are detached from the complex reality of economic life. The proponents of this strong critical position take the \"ideological\" or even \"political\" standpoint and show how finance is always enmeshed with social and political power and contributes to inequality, unjust risk distribution, market crashes, etc. (critical as political). The other approach-as pursued, for example, by social studies of finance-shifts the attention from how financial models fail to adequately represent the economic reality to how they are used and shape, or perform, reality. The performativity approach questions the traditional representational view of finance and aims for a deeper understanding of financial practices and their interplay with models and theories (critical as analytical). The connection between the two modes in form of politics of performativity is explored.

As Artificial Intelligence (AI) becomes increasingly embedded in financial decision-making, the opacity of complex models presents significant challenges for professionals and regulators. While the field of Explainable AI (XAI) attempts to bridge this gap, current research often reduces the implementation challenge to a binary trade-off between model accuracy and explainability. This paper argues that such a view is insufficient for the financial domain, where algorithmic choices must navigate a complex sociotechnical web of strict regulatory bounds, budget constraints, and latency requirements. Through semi-structured interviews with twenty finance professionals, ranging from C-suite executives and developers to regulators across multiple regions, this study empirically investigates how practitioners prioritize explainability relative to four competing factors: accuracy, compliance, cost, and speed. Our findings reveal that these priorities are structured not as a simple trade-off, but as a system of distinct prerequisites and constraints. Accuracy and compliance emerge as non-negotiable \"hygiene factors\": without them, an AI system is viewed as a liability regardless of its transparency. Operational levers (speed and cost) serve as secondary constraints that determine practical feasibility, while ease of understanding functions as a gateway to adoption, shaping whether AI tools are trusted, used, and defensible in practice.

In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of science, namely within the philosophical debate on modeling. Specifically, I review my empirical investigations into the issues of model de-idealization, model justification and performativity.