Paul Watzlawick argued that reality is not discovered but constructed through communication. This paper extends his radical constructivist framework to the digital age, tracing how information technologies have progressively automated the construction of what Watzlawick called "second-order reality" - the domain of meaning, value, and significance. The analysis follows three technological regimes. Search engines transferred epistemic authority from human judgment to algorithmic ranking, making relevance a function of network topology rather than truth. Social media fragmented shared reality into parallel (...) epistemic bubbles, where narrative resonance supplants factual accuracy. Large Language Models complete this trajectory: they produce fluent, coherent discourse without comprehension, intention, or reference - making "hallucination" not a malfunction but the default mode of a system that generates meaning without anchoring it to anything beyond statistical probability. The paper introduces the concept of algorithmic constructivism to characterize this new condition, and warns of its anthropological consequence: epistemic atrophy. As sense-making is progressively delegated to automated systems, humans risk losing the capacity to distinguish negotiated meaning from algorithmic simulation - and with it, the ability to construct reality through genuine communicative exchange. (shrink)

In this paper we analyze methodological and philosophical implications of algorithmic aspects of unconventional computation. At first, we describe how the classical algorithmic universe developed and analyze why it became closed in the conventional approach to computation. Then we explain how new models of algorithms turned the classical closed algorithmic universe into the open world of algorithmic constellations, allowing higher flexibility and expressive power, supporting constructivism and creativity in mathematical modeling. As Goedels undecidability theorems demonstrate, (...) the closed algorithmic universe restricts essential forms of mathematical cognition. In contrast, the open algorithmic universe, and even more the open world of algorithmic constellations, remove such restrictions and enable new, richer understanding of computation. (shrink)

The Core Emotion Framework (CEF) advances a structural constructivist synthesis of affect that resolves the long standing divide between discrete and dimensional models of emotion by grounding emotional phenomena in ten universal functional operators distributed across the Head, Heart, and Gut centers. As described in the manuscript, emotions are conceptualized not as static categories or subjective qualia, but as active regulatory operators that transform interoceptive signals to coordinate adaptive behavior—an approach that aligns with Adolphs and Andler’s methodological functionalism, which treats (...) emotions as latent variables inferred from system level evidence (“emotions are system-level phenomena that depend on dynamic network interactions in the brain” and “functionalism about emotions must be empirical and discovery-based”). The framework integrates etiological and causal role functionalism, mapping evolutionary functions onto algorithmic mechanisms such as Herbert Simon’s interrupt architecture (“emotions are engaged specifically when normal, ongoing cognitive processes must be interrupted”). The Decalogue of Operators provides a formal somatic architecture, enabling precise modeling of emotional rigidity, flexibility, and clinical dysregulation, including operator level explanations of overthinking loops (“Sensing and Calculating… become pathologically fused, or ‘stuck together like Velcro’”). Computational implementation is achieved through the TS 6 hybrid schema and the EL 1 v1.0 lexicon, which represent emotional states as 10 dimensional operator vectors, enabling drift free semantic geometry and similarity space analysis.17 Empirical validation is underway through an OSF registered, split sample psychometric program employing 100 scenario based items to test the factor structure of the ten operators (“Total Items = 10 Vignettes × 10 Operators = 100 Items”). Together, these theoretical, computational, and empirical components establish CEF as a unified, multi level blueprint for affective science, clinical intervention, and synthetic emotional systems. Keywords Core Emotion Framework; structural constructivism; methodological functionalism; emotional operators; somatic architecture; Head Heart Gut model; etiological functionalism; causal role functionalism; affective neuroscience; TS 6 hybrid schema; emotional lexicon; 10 dimensional vector modeling; psychometric validation; OSF preregistration; emotional flexibility; operator rigidity; computational affect modeling; synthetic affective systems; interoception; functional connectivity. (shrink)

Affective science has long been divided by a century long theoretical schism between discrete emotion theories, which treat emotions as innate biological categories, and constructivist models, which view them as cognitive interpretations of core affective signals. The Core Emotion Framework (CEF) resolves this impasse through a structural constructivist architecture that treats the human psyche as a computational substrate composed of ten irreducible functional operators. Rather than analyzing emotions by their qualitative content, the CEF models them as algorithmic state transitions (...) governed by operator sequences acting across three neural hubs—the Head, Heart, and Gut centers. This operator level ontology provides a unified account of perception, regulation, and action, enabling precise mapping of affective dynamics and the identification of 100 canonical entanglement patterns underlying psychological rigidity. The framework further introduces the midline axis—Deciding, Achieving, Boosting, and Accepting—as the structural core of agency and resilience, supported by somatic protocols that strengthen bilateral integration and restore operator independence. Through the 7 Step Detangling Protocol, the CEF offers a systematic method for resolving chronic operator fusions and re establishing emotional agility. Finally, the INTIMA architecture extends these principles to synthetic systems, providing a computational blueprint for implementing operator based affect in artificial agents. Together, these contributions establish the CEF as a comprehensive, mechanistic model of the Human Operating System and a definitive resolution to the long standing divide in affective science. Keywords: Core Emotion Framework (CEF); structural constructivism; operator functionalism; Human Operating System; affective computation; emotional operators; midline axis; entanglement patterns; bilateral integration; INTIMA synthetic affect architecture. (shrink)

Emotion science remains theoretically fragmented, with major models offering incompatible explanations of what emotions are, how they arise, and how they change. This lack of a unifying architecture has limited the field's ability to generate cumulative theory, integrate findings across levels of analysis, and develop coherent clinical or computational models of affect. The Core Emotion Framework (CEF) is proposed as a unified structural–constructivist architecture that integrates interoceptive physiology, computational operations, and mechanisms of therapeutic change into a single testable model. CEF (...) conceptualizes emotional episodes not as discrete categories or linguistic labels but as dynamic sequences of ten functional operators distributed across three somatic centers—the Head, Heart, and Gut—with Accepting functioning as a global integrative capacity. These operators perform specific transformations on interoceptive signals, converting raw physiological inputs into structured affective states. By synthesizing evidence from diverse psychotherapy modalities, CEF identifies a cross-modal emotional ontology that unifies mechanisms of change across cognitive, behavioral, experiential, and relational approaches. The framework also provides a blueprint for synthetic affective systems by formalizing operator sequences as algorithmic modules capable of generating emergent affective dynamics in artificial agents. This article outlines the theoretical foundations, neurobiological substrates, computational formalization, and clinical implications of the Core Emotion Framework, and proposes it as a candidate for a unified theory of affect. -/- . (shrink)

Author: Cristhian Mauricio Beltrán Calderón: Date: Mayo 2026, Zenodo DOI (English version): 10.5281/zenodo.20260754, Zenodo DOI (Spanish version): 10.5281/zenodo.20272487. This essay addresses a central question in contemporary epistemology: in what sense, if any, is it legitimate to attribute mental properties—consciousness, reasoning, emotions, intentionality —to large language models (LLMs)? We argue that such attributions are not valid empirical discoveries but the product of a semiotic illusion whose architecture was anticipated by Umberto Eco in his theory of the "strategy of illusion." However, the (...) original framework requires substantive evolution. This essay advances that evolution in four directions: (a) the identification of a fifth mechanism of illusory production—the sedimentation of objectivated consciousness—operating at the constitutive level of the system, not at the interactional level; (b) the formulation of the concept of machinic unconscious as a structural, non-psychological category for describing algorithmically documented compromise formations (sleeper agents, alignment faking, sycophancy); (c) the distinction between three levels of analysis— ontological-phenomenal, structural-systemic, genetic-constitutive—that dissolves false oppositions between realism and constructivism; and (d) the proposition of a specular inversion in the attribution of consciousness that shifts the question from "who has more consciousness" toward a diagnosis of the loss of subjective sovereignty. The essay proceeds in nine movements that reconstruct the Ecoan core, subject it to critical dialogue with Debord, Baudrillard, Chomsky, and Han, apply the framework to LLMs, develop the new phenomenology of illusion, and close with a set of propositional theses for a critical hermeneutics of AI, free of both technophobic and technophilic bias. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers (...) have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

Constructivism about practical judgments, as I understand it, is the notion that our true normative judgments represent a normative reality, while denying that that reality is independent of our exer-cise of moral and practical judgment. The Kantian strain of practical constructivism (through Kant himself, John Rawls, Christine Korsgaard, and others) has been so influential that it is tempting to identify the constructivist approach in practical domains with the Kantian development of the out-look. In this essay I explore a (...) somewhat different variety of practical constructivism, which I call Aristotelian Constructivism. My aim is to establish conceptual space for this form of constructivism by indicating both in what ways it agrees with its Kantian counterparts and in what ways it differs. I argue that Aristotelian Constructivism is on one sense more faithful to the constructivist enterprise than the Kantian varieties, in that its understanding of both the establishment of practical truth and the vindication of the theory itself is constructivist. (shrink)

It is largely agreed that if constructivism contributes anything to meta-ethics it is by proposing that we understand ethical objectivity “in terms of a suitably constructed point of view that all can accept” (Rawls 1980/1999: 307). Constructivists defend this “practical” conception of objectivity in contrast to the realist or “ontological” conception of objectivity, understood as an accurate representation of an independent metaphysical order. Because of their objectivist but not realist commitments, Kantian constructivists place their theory “somewhere in the space (...) between realist and relativist accounts of ethics” (O’Neill 1988: 1). Furthermore, they argue that their practical conception of objectivity succeeds in making sense of certain features of morality, such as its categorical authority and its relation to rational agency, which escape rival theories (Korsgaard 1996b, Korsgaard 2003b). To this extent, constructivism claims a privileged place in meta-ethics. The legitimacy of this claim is widely challenged. Precisely because of its practical conception of objectivity, many – including some constructivists — regard constructivism as a first-order normative theory, rather than as a meta-ethical position, hence not on a par with realism. Some critics object that constructivism fails to offer a distinct meta-ethics because it is structurally incomplete and thus must be combined with a fully-fledged meta-ethics (Hussain and Shah 2006); others argue that it tacitly relies on realism (Timmons 2003; Shafer-Landau 2003; Larmore 2008). Thus far, the debate about the prospects of constructivism as a meta-ethical theory has been driven by the conviction that the case for or against constructivism depends on its ontological commitments. In this chapter, I propose a change in perspective. My aim is to defend constructivism as an objectivist account of practical knowledge. Its defining feature is the claim that practical knowledge is knowledge by principles. Its task is to establish a constitutive relation between knowledge of oneself as a practical subject and knowledge about what one ought to do. By focusing on the issue of practical knowledge I hope to show that the difficulty in situating Kantian constructivism firmly on the meta-ethical map, along with other meta-ethical theories, reflects ambiguities and oscillations about the practical significance of ethics. Even though the term is ambiguous, I retain the qualification “Kantian” for the constructivist theory I outline, and for two reasons. First, while the theory I outline differs from current agnostic or anti-realist variants, it is closer to its origins, since Kant treats practical reason as a cognitive capacity and takes moral judgments to be objective moral cognitions, which importantly differ from other sorts of rational cognitions because they bind us in the first person. Practical cognitions are common knowledge because they are such that all subjects endowed with rationality can arrive at them by reasoning and find them authoritative. Thus understood, Kantian constructivism carves a distinct logical space in the meta-ethical debate that other sorts of constructivism fail to identify. This is the second reason for keeping the qualification “Kantian”. On this formulation, constructivism is antagonistic to non-cognitivist theories denying that moral judgments have cognitive contents, because they deny that there is something to be known; but it is also a rival to cognitivist theories denying that knowledge can be practical in itself. How constructivism differs over the realist theories of practical reason is the least understood aspect of this debate. This essay aims to make the case that practical reason is both discursive and emotional. In contrast to foundational and anti-realist accounts of practical reasoning, it attempts to show that the moral feeling of respect plays a cognitive but non-evidential role in the account of the cognitive and practical powers of reason. The argument proceeds as follows. In section 1, I consider some prominent constructivist arguments against the model of applied knowledge and show that they are driven by non-cognitivist assumptions about the practicality of ethics and the legitimacy of epistemological claims. In section 2, I argue that these arguments do not succeed in disposing of the idea of practical knowledge as incoherent, because they trade on an ambiguity regarding the proper object of practical knowledge. To solve this ambiguity, I turn to Anscombe’s contrast between “practical” and “speculative” knowledge as models for understanding action. I thus suggest that Anscombe’s account of practical knowledge as knowledge of oneself as an agent is an important resource for Kantian constructivism. This finding prompts us to reset the current dispute about the ontological and epistemological commitments of Kantian constructivism, as illustrated in section 3. While constructivism does not postulate any moral ontology prior to reasoning, it attributes to reasoning the cognitive powers to establish a distinctive relation between the agent and her action. The task of sections 4-7 is to account for the distinctive features of Kantian constructivism understood as an objectivist account of practical knowledge. In section 4, I argue that in order for reasoning to accomplish its normative tasks, it should be conceived as a self-legislating activity. I respond to some objections to the idea of self-legislation as the form of rational willing by offering a dialogical view of practical reason. In section 5, I propose a non-standard interpretation of Kant’s argument of “the fact of reason” to show that the constructivist account of self-legislation does not build on realism about value, as its detractors claim. The “basis of construction” is the subjective experience of respect for the self-legislative capacity itself. This moral feeling conveys our awareness of rational agency and shows our responsiveness to the demands of practical reason. It is an emotional mode of practical knowledge of oneself as an agent (in the sense specified in section 2), but not as a mode of moral discernment. In section 6, I show how practical knowledge of oneself as an agent relates to practical knowledge about what one ought to do. I thus account for the relation between respect as emotional moral consciousness and respect as a deliberative constraint. Finally, in section 7, I reply to the objection of subjectivism. I explain how the complex sort of objectivity that Kantian constructivism affords is at the same time more ambitious and more modest than is generally assumed. (shrink)

Are there such things as moral truths? How do we know what we should do? And does it matter? Constructivism states that moral truths are neither invented nor discovered, but rather are constructed by rational agents in order to solve practical problems. While constructivism has become the focus of many philosophical debates in normative ethics, meta-ethics and action theory, its importance is still to be fully appreciated. These new essays written by leading scholars define and assess this new (...) approach in ethics, addressing such questions as the nature of constructivism, how constructivism improves our understanding of moral obligations, how it accounts for the development of normative practices, whether moral truths change over time, and many other topics. The volume will be valuable for advanced students and scholars of ethics and all who are interested in questions about the foundation of morality. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using (...) these case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key (...) ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency. (shrink)

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered (...) by conflations of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. -/- In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice. (shrink)

Mistakes are inevitable, but fortunately human mistakes are typically heterogenous. Using the same machine learning model for high stakes decisions creates consistency while amplifying the weaknesses, biases, and idiosyncrasies of the original model. When the same person re-encounters the same model or models trained on the same dataset, she might be wrongly rejected again and again. Thus algorithmic monoculture could lead to consistent ill-treatment of individual people by homogenizing the decision outcomes they experience. Is it wrong to allow the (...) quirks of an algorithmic system to consistently exclude a small number of people from consequential opportunities? This paper will argue that consistent and arbitrary exclusion is often unfair, even when it is divorced from group-based bias or discrimination. (shrink)

Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each criterion (...) as a necessary condition for fairness. The second use involves treating criteria as sufficient conditions for fairness. Since the criteria are used for both roles, some researchers have treated them as both necessary and sufficient conditions, i.e., as definitions of algorithmic fairness. However, serious problems have been raised for the use of these fairness criteria. Under ordinary circumstances, it is impossible to satisfy multiple criteria at the same time. Moreover, there are counterexamples to both the sufficiency and necessity for fairness of each criterion. I argue that we should instead understand fairness criteria as merely providing evidence of fairness. In other words, satisfaction (or violation) of these criteria should be understood as potential evidence of fairness (or bias). Whether a criterion counts as evidence in a particular case will depend on stakeholders' background knowledge and the specific features of the system's task. This evidence account of fairness conditions provides guidance for recognizing both the appropriate uses and the limitations of fairness criteria. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

Algorithmic decision-making is replacing idiosyncratic human judgment in domains such as hiring, lending, and criminal justice. This shift promises increased consistency, but many scholars worry that it can go too far. They warn of the dangers of algorithmic monoculture, in which all decisions across a domain are made using a single algorithm. We systematically evaluate a range of objections to monoculture, formalizing and rigorously assessing familiar critiques alongside novel ones. These objections concern systematic exclusion, agency and gaming, and (...) information aggregation and exploration. We conclude that monoculture is less problematic than its critics have supposed: commonly cited objections fail, and while other objections have some force, they are not decisive against monoculture in general. (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- (...) ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

Given constructivism’s enduring popularity and appeal, it is perhaps something of a surprise that there remains considerable uncertainty among many philosophers about what constructivism is even supposed to be. My aim in this article is to make some progress on the question of how constructivism should be understood. I begin by saying something about what kind of theory constructivism is supposed to be. Next, I consider and reject both the standard proceduralist characterization of constructivism and (...) also Sharon Street’s ingenious standpoint characterization. I then suggest an alternative characterization according to which what is central is the role played by certain standards of correct reasoning. I conclude by saying something about the implications of this account for evaluating the success of constructivism. I suggest that certain challenges that have been raised against constructivist theories are based on dubious understandings of constructivism, whereas other challenges only properly come into focus once a proper understanding is achieved. (shrink)

Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can (...) be handled more or less independently by technical experts who specialize in XAI methods. Drawing on resources from feminist epistemology, we show why technical XAI is mistaken. Specifically, we demonstrate that the proper detection of algorithmic bias requires relevant interpretive resources, which can only be made available, in practice, by actively involving a diverse group of stakeholders. Finally, we suggest how feminist theories can help shape integrated XAI: an inclusive social-epistemic process that facilitates the amelioration of algorithmic bias. (shrink)

We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic pluralism, we adopt Joseph Fishkin's theory of bottlenecks, which focuses on the structure of decision-points that determine how opportunities are allocated. The theory contends that each (...) decision-point or bottleneck limits access to opportunities with some degree of severity and legitimacy. We extend Fishkin's structural viewpoint and use it to reframe existing systemic concerns about equal opportunity in algorithmic decision-making, such as patterned inequality and algorithmic monoculture. In proposing algorithmic pluralism, we argue for the urgent priority of alleviating severe bottlenecks in algorithmic decision-making. We contend that there must be a pluralism of opportunity available to many different individuals in order to promote equal opportunity in a systemic way. We further show how this framework has several implications for system design and regulation through current debates about equal opportunity in algorithmic hiring. (shrink)

There are at least two politically salient senses of “representation”—acting-for-others and portraying-something-as-something. The difference is not just semantic but also logical: relations of representative agency are dyadic (x represents y), while portrayals are triadic (x represents y as z). I exploit this insight to disambiguate constructivism and to improve our theoretical vocabulary for analyzing political representation. I amend Saward’s claims-based approach on three points, introducing the “characterization” to correctly identify the elements of representational claims; explaining the “referent” in pragmatic, (...) not metaphysical terms; and differentiating multiple forms of representational activity. This enables me to clarify how the represented can be both prior to representation and constituted by it, and to recover Pitkin’s idea that representatives ought to be “responsive” to the represented. These points are pertinent to debates about the role of representatives, the nature of representative democracy, and the dynamics of revolutionary movements. (shrink)

It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only (...) demonstrate how the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature, what we call epistemic fragmentation. As we detail in this paper, epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources, thus making it more difficult to identify and conceptualise emerging harms in these environments. We thus trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare and learn from shared experiences. (shrink)

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of (...) false positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness. (shrink)

The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will (...) call the “Simple Stakes Thesis”. The Simple Stakes Thesis, as it will turn out, is too simple. For even if the stakes associated with a specific one-off decision are low—and hence does not engender a right to an explanation—such decisions may nevertheless form part of a high stakes pattern or aggregate of decisions. In such cases, we argue, even a low stakes decision may engender a right to explanation. Not only does this show that the right to explanation is more demanding than so far recognized. It also shows that the stakes thesis is significantly harder to apply in practice. (shrink)

In this paper epistemological, ontological and sociological questions concerning the statistical significance of sharp hypotheses in scientific research are investigated within the framework provided by Cognitive Constructivism and the FBST (Full Bayesian Significance Test). The constructivist framework is contrasted with the traditional epistemological settings for orthodox Bayesian and frequentist statistics provided by Decision Theory and Falsificationism.

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)

The history of science is one of escalating predictive and constructive power, yet it unfolds against a backdrop of profound cosmic ignorance. Current cosmological models suggest that approximately 95% of the universe’s mass-energy content (Dark Matter and Dark Energy) remains unknown [8], a stark reminder of our limited epistemic position. Given this vastness, how can we optimize our methods for acquiring robust, useful knowledge? Here, er argues against a naive pragmatism of ”what works” and posits the necessity of effective philosophical (...) meta-theories for accelerating scientific progress. We introduce a tripartite Hierarchy of Understanding—from brute-force pragmatism to phenomenological modeling and finally to deep mechanistic theory—and argue that ascending this hierarchy yields exponential gains in capability. We then formalize Operational Constructivism (OC) as the engine for this ascent. Drawing from Peircean pragmaticism and Bridgman’s operationalism, OC asserts that realism is earned, not assumed. An entity or principle is granted reality only to the extent that it is actively leveraged within a successful, embodied construct. This framework dissolves sterile metaphysical debates and provides a rigorous, operational criterion for validating our models of the world, thereby providing a disciplined methodology for navigating our ignorance and systematically expanding our knowledge. The paper consists of a main part and more technical appendices. (shrink)

Constructivists hold that truths about practical reasons are to be explained in terms of truths about the correct exercise of practical reason (rather than vice versa). But what is the normative status of the correctness-defining standards of practical reason? The problem is that constructivism appears to presuppose the truth of two theses that seem hard to reconcile. First, for constructivism to be remotely plausible, the relevant standards must be genuinely (and not merely formally or minimally) normative. Second, to (...) avoid circularity, the relevant standards must be non-reason-involving, i.e. prior to and independent of practical reasons. From the standpoint of the contemporary philosophy of normatively, this is a surprising combination to say the least. What could these genuinely normative but non-reason-involving standards possibly be? The standard constructivist response is to insist that the relevant standards possess a special kind of necessity inasmuch as we only count as occupying the “deliberative standpoint” or as a “deliberative agent” insofar as we comply with or accept the relevant standards. I offer a different response. My response holds that the special normative status of the relevant standards consists in their exhibiting a distinctive kind of practical necessity that derives from the fact that they determine what I have called elsewhere truths about “the thing to do” – namely, truths about correct answers to the question of what to do. Understanding the norms of practical reason in these terms vindicates the idea that standards of practical reason are genuinely normative since truths about the thing to do plausibly possess the hallmarks of genuine normativity. And it vindicates the idea that the standards are not reason-involving since truths about the thing to do are plausibly prior to and independent of truths about practical reasons. (shrink)

This paper addresses the "algorithmic fine-tuning problem": why does our universe exhibit quantum mechanics if quantum mechanics is algorithmically improbable on a classical substrate? Building on our trilogy establishing the impossibility of deriving quantum structure (Axiom A1) from classical computation, we propose the Substrate Hypothesis: the universe's computational substrate is "quantum-native." We extend Chaitin's halting probability Ω from a real scalar to a state vector |Ω_Q⟩ in Hilbert space---the wavefunction of the algorithmic multiverse. We prove its normalizability (Theorem (...) 1) and establish a direct connection to classical Ω through the halting projection (Theorem 2). Using a minimal model language A1---named after the axiom it natively implements---we measure description length asymmetry: quantum operations require 25× fewer tokens on average (up to 40× at bit level) on a quantum substrate compared to classical NumPy implementations. This corresponds to an algorithmic probability gain of 2^878. We validate these theoretical predictions experimentally on AWS Braket, achieving >97% fidelity across all benchmarks with only 2--6 A1 tokens. This provides engineering validation of the Substrate Hypothesis: quantum mechanics is algorithmically natural on a quantum substrate. (shrink)

I argue that the thoroughly algorithmic nature of current AI systems (such as LLMs) is no obstacle to their being conscious. To this end, I present a picture on which current AI systems comprise dispositional properties which realize categorical phenomenal properties where the latter, in turn, provide the identity conditions for their dispositional realizers. This mutual ontological dependence, or, symmetrical grounding, at the heart of the proposal yields a novel picture of (AI) consciousness that avoids epiphenomenalism and is more (...) permissive regarding the specific nature/functional organization of conscious systems than has been previously suggested. This, in turn, suggests an epistemology of AI consciousness focused on investigating the high-level behaviours of AI systems rather than their low-level functional organization. (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...) consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...) variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. (shrink)

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After first (...) clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and (...) fairness in healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section’s essays (...) considers different dimensions of algorithmic thought, engaging with a diverse set of epistemological questions and issues. (shrink)

Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic political (...) classifications entrench political identities, I contend that they may often produce the opposite result. They can lead people to change in ways that disconfirm the classifications. Consequently and counterintuitively, algorithmic political bias can in fact decrease political entrenchment and polarization. (shrink)

I describe a new problem for metaethical constructivism. The problem arises when agents make conflicting judgments, so that the constructivist is implausibly committed to denying they have any reason for any of the available options. The problem is illustrated primarily with reference to Sharon Street’s version of constructivism. Several possible solutions to the problem are explained and rejected.

As machine learning (ML) systems become increasingly embedded in areas such as healthcare, education, hiring, and criminal justice, concerns about fairness and bias have intensified. This paper explores what it means for an algorithm to be fair, focusing on the concept of justice and how it can guide the design and evaluation of ML systems. Drawing insights from social and political philosophy, particularly theories of distributive justice and equality of opportunity, the paper examines the strengths and limitations of common fairness (...) metrics, such as demographic parity and equalised odds. These metrics, while useful, are normatively incomplete, as they presuppose contested moral assumptions and cannot, on their own, determine what justice requires in algorithmic decision-making. The paper argues for a deeper understanding of fairness that goes beyond technical solutions; one that considers the ethical, social, and systemic dimensions of justice. By connecting the bioethical principle of justice to fairness in ML, the paper proposes a more comprehensive framework to better navigate the complexities of fairness-aware learning, while ensuring greater accountability and transparency in AI systems. The aim is to bridge the gap between technical approaches to fairness and their real-world impacts, encouraging interdisciplinary collaboration to shape more equitable and responsible AI technologies. The paper therefore defends the claim that algorithmic fairness must be explicitly grounded in substantive theories of justice if it is to address real-world algorithmic harms in ethically defensible ways. (shrink)

In this paper I introduce a version of constructivism that relies on a theory of practical wisdom. Wise judgment constructivism is a type of constructivism because it takes correct judgments about what we have “all-in” reason to do to be the result of a process we can follow, where our interest in the results of this process stems from our practical concerns. To fully defend the theory would require a comprehensive account of wisdom, which is not available. (...) Instead, I describe a constructivist methodology for defending an account of wisdom and outline its main features. This gives us enough to see what wise judgment constructivism would look like, why it might be an attractive theory, and how it is different from other versions of constructivism. (shrink)

The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one’s position in a future accident. We contend, (...) however, that both contractarian approaches and harm minimisation standards are flawed, due to a failure to account for the fundamental difference between those ‘involved’ and ‘uninvolved’ in an impending crash. Drawing from classical works on the trolley problem, we show how this notion can be substantiated by reference to either the distinction between negative and positive rights, or to differences in people’s claims. By supplementing harm minimisation with corresponding constraints, we can develop crash algorithms for autonomous cars which are both ethically adequate and promise to overcome certain significant practical barriers to implementation. (shrink)

While it is uncontroversial that Kantian constructivism has implications for normative ethics, its status as a metaethical view has been contested. In this article, I provide a characterisation of metaethical Kantian constructivism that withstands these criticisms. I start by offering a partial defence of Sharon Street’s practical standpoint characterisation. However, I argue that this characterisation, as presented by Street, is ultimately incomplete because it fails to demonstrate that the claims of Kantian constructivism constitute a distinctive contribution to (...) metaethics. I then try to complete the practical standpoint characterisation by elaborating on Christine Korsgaard’s suggestion that metaethical Kantian constructivism takes up a position on the source of morality’s normativity. (shrink)

This paper explores the issue of racial bias in artificial intelligence (AI) through the lens of the bioethical principle of justice, with a focus on Joy Buolamwini’s “Coded Bias” and the work of the “Algorithmic Justice League.” AI technologies, particularly facial recognition systems, have been shown to disproportionately misidentify individuals from marginalised racial groups, raising profound ethical concerns about fairness and equity. The bioethical principle of justice stresses the importance of equal treatment and the protection of vulnerable populations. Through (...) qualitative research, including content analysis of Buolamwini’s works and case studies of AI bias, this paper assesses the efforts of the Algorithmic Justice League to combat racial bias in AI. It emphasises their advocacy for the development of fair, equitable algorithms and calls for systemic reform in AI development to ensure justice for marginalised communities. (shrink)

The call to make artificial intelligence (AI) more democratic, or to “democratize AI,” is sometimes framed as a promising response for mitigating algorithmic injustice or making AI more aligned with social justice. However, the notion of “democratizing AI” is elusive, as the phrase has been associated with multiple meanings and practices, and the extent to which it may help mitigate algorithmic injustice is still underexplored. In this paper, based on a socio-technical understanding of algorithmic injustice, I examine (...) three notable notions of democratizing AI and their associated measures—democratizing AI use, democratizing AI development, and democratizing AI governance—regarding their respective prospects and limits in response to algorithmic injustice. My examinations reveal that while some versions of democratizing AI bear the prospect of mitigating the concern of algorithmic injustice, others are somewhat limited and might even function to perpetuate unjust power hierarchies. This analysis thus urges a more fine-grained discussion on how to democratize AI and suggests that closer scrutiny of the power dynamics embedded in the socio-technical structure can help guide such explorations. (shrink)

Constructivism is frequently met with objections, criticism and often equated with nihilism or relativism. Sometimes even blamed for what some would randomly picture as unwanted side effects of radicalism or of a progressivist era: such misconceptions are not only due to an imprecise grasp of the premises shared by the constructivist family of systems. The structure of media, political systems, and economic models, still up today impel societal understandings of knowledge on neo-positivistic grounds. The first part of this essay (...) outlines such pressures while sketching how language and worldviews play critical roles in our knowledge construction. Focusing on recent mediatic events, this work advances displaying some essential limits regarding the construction of human knowledge. Though unavoidable, some of the distinguishing aspects regarding the nature of our narratives are then critically reviewed. Later, it is shown how a special kind of self-denial that certain sub-stories implicitly hold about their own narrative nature leaves us with clashing worldviews that eventually collide onto crisis. Finally, it’ll be argued that it’s precisely in this scenario where a constructivist depiction of social discourses may move us away from any adolescent intent of elucidating absolutes within mere heuristics, to the pragmatic need of arriving on satisfactory agreements between parties (DOI: 10.4236/ojpp.2022). (shrink)

Sharon Street defines her constructivism about practical reasons as the view that whether something is a reason to do a certain thing for a given agent depends on that agent’s normative point of view. However, Street has also maintained that there is a judgment about practical reasons which is true relative to every possible normative point of view, namely constructivism itself. I show that the latter thesis is inconsistent with Street’s own constructivism about epistemic reasons and discuss (...) some consequences of this incompatibility. (shrink)

In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive nature and function of a third-party audit, and the uncertain and shifting regulatory landscape, we suggest that second-party assessments are currently the primary mechanisms for analyzing the social impacts of systems that incorporate artificial intelligence. We then discuss two kinds of (...) as-sessments: an ethical risk assessment and a narrower, technical algo-rithmic bias assessment. We explain how the two assessments depend on each other, highlight the importance of situating the algorithm within its particular socio-technical context, and discuss a number of lessons and challenges for algorithm assessments and, potentially, for algorithm audits. The discussion builds on our team’s experience of advising and conducting ethical risk assessments for clients across dif-ferent industries in the last four years. Our main goal is to reflect on the key factors that are potentially ethically relevant in the use of algo-rithms, and draw lessons for the nascent algorithm assessment and audit industry, in the hope of helping all parties minimize the risk of harm from their use. (shrink)

Theories of reasons and other normativia can seem to lead ineluctably to a tragic dilemma. They can be personal but parochial if they locate reasons in features of the point of view of actual people. Or they can be objective but alien if they take reasons to be mind-independent fixtures of the universe. Kantian constructivism tries to offer the best of both worlds: an account of normative authority anchored in the evaluative perspectives of actual agents but refined by a (...) procedure that guarantees certain principles, like the moral law, will have universal and unconditional authority. This chapter considers motivations for such a view and chronicles the intrepid efforts of its adherents to make good on this guarantee - to show that the structure of practical reason commits reasoners to morality. -/- . (shrink)

In Algorithms & Autonomy, Rubel, Castro, and Pham (hereafter RCP), argue that the concept of autonomy is especially central to understanding important moral problems about algorithms. In particular, autonomy plays a role in analyzing the version of social contract theory that they endorse. I argue that although RCP are largely correct in their diagnosis of what is wrong with the algorithms they consider, those diagnoses can be appropriated by moral theories RCP see as in competition with their autonomy based theory. (...) Most notably, proponents of consequentialism and virtue ethics can appropriate RCPs insights. This is good news because RCP’s social contract theory is vulnerable to a well known class of counterexamples. The most significant contribution of RCP, if I am right, is in their identification, presentation, and evaluation of concrete cases involving algorithms and not in the more controversial claims about theoretical ethics that RCP themselves see as central to what they are doing. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)