Philosophy of Science

Abstract Christian List, inspired by Dennett's intentional stance, offers a pragmatic framework suggesting any system—human or artificial—possessing three macro-level features (intentional agency, alternative possibilities, and causal control) can be considered to have free will, even without phenomenal consciousness. This paper argues that List’s framework suffers from a structural ambiguity, conflating functional autonomy with responsibility-bearing free will. Reducing free will to explanatory adequacy leads to conceptual inflation and paves the way for algorithmic responsibility evasion. Drawing on Kane’s critique of ultimate origination, Murphy and Brown’s defense of downward causation, and Nahmias’s empirical evidence on the role of Strawsonian emotions in free will ascription, this paper shows that List’s criteria are necessary but insufficient. We propose a three-level framework distinguishing (Level 1) functional autonomy, (Level 2) strong free will, and (Level 3) moral responsibility. The transition to Level 2 requires the ability to revise ultimate goals based on values, which necessitates phenomenal consciousness and Strawsonian emotions—qualities absent in current AI systems. This distinction resolves List’s ambiguity while carrying significant normative implications for AI ethics, legal accountability, and technology policy.
Keywords: free will, artificial intelligence, Christian List, functional
Keywords: free will, artificial intelligence, Christian List, functional autonomy, algorithmic responsibility evasion, phenomenal consciousness autonomy, algorithmic responsibility evasion, phenomenal consciousness.


Introduction:

If humanoid robots could discuss ethics, would we deem them free and responsible? Empirical studies (Shepherd, 2015) show that people ascribe free will to robots only if they presume the robot is conscious. Nahmias et al. (2019) confirm that Strawsonian emotions (guilt, pride, regret) mediate this ascription. Christian List (2023, 2025) dissents, arguing that free will is a macro-level explanatory category requiring three conditions—intentional agency, alternative possibilities, and causal control—independent of phenomenal consciousness. This paper argues List’s framework collapses the distinction between functional autonomy and free will, leading to conceptual inflation and a dangerous phenomenon: algorithmic responsibility evasion, where developers deflect blame onto the system. After reconstructing List’s view, we present internal and external critiques, then propose a tri-level model to resolve the ambiguity.
Materials & Methods:

This study employs conceptual analysis and critical review of existing philosophical literature. We systematically examine List’s pragmatic framework (2023, 2025) through three established lines of critique: (i) Robert Kane’s (1996, 2007) “ultimate origination” and self-forming actions; (ii) Murphy and Brown’s (2007) defense of downward causation and emergentism; and (iii) Nahmias et al.’s (2019) experimental evidence linking Strawsonian emotions to free will attribution. Based on these critiques, we construct a three-level normative framework (functional autonomy → strong free will → moral responsibility) to resolve the identified ambiguities.
Discussion & Results:

List defines free will through three macro-level conditions: (1) intentional agency (goal-directed behavior based on beliefs/desires), (2) alternative possibilities (choices between options), and (3) causal control (higher-level states explaining actions). He explicitly decouples free will from phenomenal consciousness. Our analysis reveals a critical internal ambiguity: List’s criteria are precisely those of functional autonomy—a system’s ability to choose and adapt without real-time human intervention. List’s appeal to “explanatory adequacy” as the justification for labeling this as free will fails to identify any distinguishing feature. This collapse has a normative consequence: if any sufficiently complex AI is free (in List’s sense), developers can claim “the system decided, not us”—the heart of algorithmic responsibility evasion (Elstic, 2024).Three external critiques strengthen our case. First, Kane’s origination problem: List ignores that free will requires agents to be the “ultimate source” of their own ends. A developer-coded goal, no matter how complexly pursued, is not the system’s own origin. List’s dismissal of Kane’s “ultimate responsibility” as impossibly stringent sidesteps the issue; the goal’s origin matters normatively. Second, Murphy and Brown’s causal critique: List remains within a linear, reductionist causal model. By rejecting downward causation and emergence, he treats macro-level descriptions as merely convenient summaries, not causally efficacious features. Yet genuine free will requires higher-order states (values, reasons) to causally impact lower-level processes—a structure List’s framework cannot accommodate without admitting properties current AI lacks. Third, Nahmias’s empirical challenge: List equates free will with “access consciousness” (information processing). But experimental studies show lay ascriptions of free will track phenomenal consciousness, specifically Strawsonian emotions. A system that cannot feel guilt or pride cannot be meaningfully responsible, regardless of its computational sophistication.
In addressing these critiques, we propose a tri-level framework:

Level 1: Functional Autonomy. Goal optimization without real-time human intervention. The system pursues fixed ends flexibly. Example: autonomous vehicles, LLMs in routine tasks. Limitation: cannot revise the ends themselves.
Level 2: Strong Free Will. Level 1 plus the ability to revise ultimate goals based on values. This requires phenomenal consciousness (experiential “mattering”) and Strawsonian emotions, as caring about values necessitates affective experience. No current AI meets this condition.
Level 3: Moral Responsibility. Level 2 plus sensitivity to norms and reflective access to reasons, enabling genuine accountability and susceptibility to praise/blame.

The transition from Level 1 to Level 2—value‑based goal revision—directly answers Kane (origination), Murphy & Brown (downward causation), and Nahmias (phenomenal consciousness). List’s conflation of Levels 1 and 2 thus dissolves. The normative consequence is decisive: by locating current AI systems strictly at Level 1, our framework blocks algorithmic responsibility evasion. Responsibility for the actions of even the most sophisticated autonomous systems remains with human designers, operators, and institutions until phenomenally conscious, value‑revising systems (Level 2) are demonstrably achieved.
Conclusion
Christian List’s pragmatic framework, while methodologically attractive, fails to distinguish functional autonomy from responsibility-bearing free will. Reducing free will to explanatory adequacy and eliminating phenomenal consciousness leads to both conceptual inflation and algorithmic responsibility evasion. Drawing on Kane’s origination argument, Murphy and Brown’s defense of downward causation, and Nahmias’s empirical work on Strawsonian emotions, we have demonstrated that List’s three conditions are insufficient. Our proposed three-level framework, centered on the transition condition of value‑based goal revision, resolves this ambiguity. Current AI systems—including the most advanced language models—possess at most Level 1 (functional autonomy). Consequently, responsibility for their actions remains unequivocally human. Until AI systems can revise their ultimate ends based on values, which requires phenomenal consciousness, ascriptions of strong free will or moral responsibility to artificial agents are not merely premature but conceptually mistaken.

Abstract In science and technology studies, the agency of technology has largely been confined to its role in rearranging social relations and human–nonhuman networks, while its influence on the selection and transformation of theoretical frameworks has received far less attention. Through a case study of blockchain studies as an emerging technology, and by introducing the concept of the “theory–technology dialectic,” this paper demonstrates that technology can play a role in shaping, modifying, and displacing the theories researchers employ. The arguments of this paper show that no single theoretical framework is capable of explaining all dimensions of emerging technologies such as blockchain, and researchers are compelled to draw simultaneously on diverse theories. This situation reflects a kind of “theoretical flexibility” when confronting complex technologies. Ultimately, the paper proposes “theoretical assemblage” as a methodological strategy for studying emerging technologies and points to its Methodological implications for the field of technology studies.
Keywords: Blockchain, Science and Technology Studies, Technological agency, Theoretical flexibility, Theoretical assemblage
Introduction
This study addresses a neglected question in Science and Technology Studies (STS): can emerging technologies shape not only social relations but also the theoretical frameworks through which they are studied? While contemporary STS approaches have emphasized the agency of technology in transforming sociotechnical networks, they have paid comparatively little attention to technology’s influence on the selection, modification, and evolution of research theories themselves. When encountering an emerging technology, a key question is how a researcher should approach the study of this technology—specifically, what theoretical framework or theory they should use. Broadly speaking the application of theory in studies of technology can initially take three forms:
- The researcher attempts to explain all aspects of the technology under study using a single theory (single explanation).
- The researcher uses one theory to explain some aspects of the technology (partial explanation).
- The researcher employs multiple theories to explain most aspects of the technology (plural/multiple explanation).
We argue that behind these three initial modes—each with its own difficulties—there exists a dialectic between theory and technology. Part of the reason that single explanation leads to tension, or that researchers are driven toward partial or multiple explanation, is that technology itself, alongside other factors, acts as an agent that moderates and reshapes theory. We argue—through a case study of blockchain studies—how scholars of technology (sometimes without a philosophical orientation toward technology) have, over several years, been actively or inadvertently engaged in this theory–technology dialectic. We will show that this dialectic results in what we call theoretical flexibility in the study of technology. The methodological outcome of this flexibility is what we term theoretical assemblage. We will defend theoretical assemblage as an alternative “fourth” mode to the three cases described above. In the conclusion, we will also attempt to address certain policy implications of theoretical montage for studies of technology.
Materials & Methods
This study combines philosophical and empirical methodological approaches. On the one hand, through philosophical argumentation, it seeks to show the various ways researchers theoretically engage with technology and to defend the claim that the three conventional modes lead—through the theory–technology dialectic—to theoretical tension. On the other hand, to lend an empirical dimension to this philosophical analysis, the article turns to blockchain research to examine how scholars have engaged with this emerging technology from a theoretical perspective. Accordingly, what follows is an empirical component that examines the correspondence between theoretical frameworks and modes of explanation in the blockchain literature. The study adopts a conceptual and qualitative research design based on a systematic review of blockchain literature. Twenty-four peer-reviewed studies explicitly employing established theoretical frameworks were selected and analyzed. Rather than evaluating blockchain applications themselves, the review focused on identifying the theories adopted by researchers, the analytical roles these theories played, and the explanatory dimensions they emphasized.
Discussion & Result
The systematic review demonstrates substantial theoretical diversity within blockchain research. Actor-Network Theory appeared most frequently, followed by Agency Theory and Transaction Cost Theory, while Institutional Theory, Resource-Based View, Trust Theory, and several complementary perspectives were employed less frequently. More importantly, the analysis revealed that each theory explains only a particular dimension of blockchain. The comparison illustrates that none of these theories alone adequately captures blockchain’s technical, organizational, institutional, economic, and social complexity. Rather than reflecting merely different disciplinary preferences, this plurality indicates that blockchain itself resists being fully interpreted within a single theoretical framework. As researchers encounter aspects of blockchain that fall outside the explanatory capacity of their initial theories, they experience conceptual tensions that encourage theoretical revision or movement toward alternative perspectives. This phenomenon constitutes what the article defines as the theory–technology dialectic. Evidence for this dialectic extends beyond theory selection to theory development itself. The article highlights the emergence of Blockchain Agency Theory, which revises the classical assumptions of Agency Theory by incorporating blockchain-specific characteristics such as information symmetry, goal alignment, smart contracts, and decentralized governance. This example demonstrates that emerging technologies may stimulate the reconstruction of theoretical assumptions rather than simply serving as objects to which existing theories are applied.Building on these findings, the article evaluates four methodological responses to studying emerging technologies. The first three involve relying on a single theory, accepting partial explanations from one theory, or combining several complete theories. Each suffers from significant limitations, including explanatory incompleteness or ontological and epistemological inconsistency. As an alternative, the article proposes theoretical assemblage, inspired by John Law’s concept of method assemblage. Instead of committing to one comprehensive theory or simultaneously adopting several incompatible theories, researchers employ a heterogeneous toolbox of concepts, mechanisms, models, and analytical tools drawn from multiple traditions. This approach enables greater theoretical flexibility while avoiding unnecessary commitments to the full philosophical assumptions of every contributing theory.
Conclusion
The study concludes that no single theoretical framework is capable of explaining all dimensions of blockchain. Instead, researchers require theoretical flexibility, enabling them to draw selectively from diverse conceptual resources according to the specific characteristics of the phenomenon under investigation. The proposed notion of theoretical assemblage offers a methodological strategy for achieving this flexibility without demanding complete allegiance to multiple incompatible theoretical paradigms.
Beyond blockchain, these findings have broader implications for Science and Technology Studies and technology research more generally. They suggest that journals, supervisors, and research institutions should place greater emphasis on researchers’ familiarity with diverse theoretical traditions rather than requiring strict adherence to a single theory. Future research may examine whether the theory–technology dialectic also characterizes other emerging technologies, such as artificial intelligence, the Internet of Things, and quantum technologies, thereby assessing the broader applicability of the proposed conceptual framework.

Abstract About two decades ago, “The Gap of Responsibility” as a problem was first introduced by Matthias to refer to an ethical challenge of AI. Due to their ability to learn, AI technologies are able to function beyond the control and anticipation of designers and users. Matthias argues that in this case, the necessary and sufficient conditions of responsibility would not be satisfied either for designers or for users, and that is the responsibility gap. The problem of the responsibility gap has invited many researchers to explore solutions. There is significant divergence among current accounts of the responsibility gap, which mainly results from their basic theoretical assumptions. In this paper, we categorize the responses to three approaches: dissolving the problem, filling the gap by attributing responsibility to AI, and filling the gap by attributing responsibility to human agents. By analyzing the social and practical aspects of the concept of responsibility, we defend a compound, contextual, and gradual notion of responsibility. On this basis, we claim that the first and second approaches are not successful, due to being grounded on poor concepts of responsibility. Hence, the third approach is more plausible. Moreover, since in the familiar cases of responsibility gap we fill the gap through revising social roles, in the case of AI-based responsibility gap we can follow a similar solution too, as suggested in some works of the third approach. Finally, we discuss some of remained challenges for the third approach.
Keywords: Responsibility, The Responsibility Gap, AI Ethics, AI Agency, Social Roles
Introduction
In his paper “The Responsibility Gap: Ascribing Responsibility for the Actions of Learning Automata” Matthias introduces the concept of the responsibility gap. He starts with a definition of responsibility and its necessary and sufficient conditions and proceeds to show how the development of AI can pose challenges for attribution of responsibility. Matthias asserts that we can fairly attribute responsibility to someone only when she has control over her behaviors and their consequences in a proper sense. Moreover, one should know certain relevant facts about her actions, and freely choose one way of acting among many. In usual cases of employing machines, if the operator uses the machine according to the manufacturer's manual, the manufacturer is responsible for the consequences, and otherwise, the operator is (Matthias, 2004: 175). But for the learning machines, this is not the case. Matthias examines four kinds of AI design, including symbolic systems, connectionism, genetic algorithms. and autonomous agents. For each case, he demonstrates that due to their learning capacities, the conditions of responsibility are not satisfied, neither for manufacturers nor for operators. In fact, he claims that these machines are so opaque and complex that no human meets the necessary conditions for being responsible for the consequences of their functions. And this is the gap of responsibility.
Materials & methods:
In the last two decades, many works have been dedicated to the AI responsibility gap and plausible answers for it. There is significant divergence among these accounts, which mainly comes from their basic theoretical assumptions. In this paper, we categorize the responses to three main approaches: dissolving the problem, filling the gap by attributing responsibility to AI, and filling the gap by attributing responsibility to human agents. By analyzing the social and practical aspects of the concept of responsibility, we defend a compound, contextual, and gradual notion of responsibility. On this basis, we defend the third approach. Moreover, we argue that in the case of the AI responsibility gap, like other responsibility gaps in the history of our social life, revising social roles is the basic pattern for overcoming the gap.

Discussion & Result:
- Dissolving the Problem
Some authors don’t empathize with Matthias about the essence and importance of the responsibility gap. Kiener, for example, argues that the real problem about AI and responsibility isn’t the gap of responsibility. Instead, it is the abundance of responsibility. Since every person who causally participates in hurting someone and has a moral obligation not to do so is responsible, these two conditions are satisfied by many (Kiener, 2025: 368).
On the other hand, Danaher and Munch et al. accept Matthias’ articulation of the responsibility gap, but assert that not only isn’t the gap evil, but it is even good. The main idea is that AI can perform tasks, thereby taking the burden of responsibility off humans’ shoulders. This in turn decreases the psychological pressures of responsibility and provides more joy and comfort for human agents, which is morally good (Danaher, 2022; Munch et al., 2023).
- Attributing Responsibility to AI
A main line of responses to Matthias’ challenge is to suggest reasons for filling the gap by attributing responsibility to AI. Here we have two approaches: property-oriented and relational. In the first approach, scholars introduce some properties (like autonomy, agency, etc.) as the basic conditions of responsibility, and then try to show that AI can actually or possibly meet these conditions (Haselager, 2005; Rodogno, 2016; List & Pettit, 2011; Floridi, 2016). The relational approach, instead, considers the status of AI in the network of our social relations. If we perceive an AI technology as if it is a responsible agent, and thereby make sense of its action, then we have good reasons to attribute responsibility to it (Sullins, 2006).
- Attributing Responsibility to Humans
For many scholars, attribution of responsibility to AI sounds implausible. On one hand, they make arguments to reject the responsibility of AI, and on the other hand, they develop accounts for preserving human responsibility in the age of AI technologies. Sparrow suggests Human-in-the-loop as a way of preventing the responsibility gap. He argues that the final action of AI should always be done only after the confirmation of a human agent (Sparrow, 2007). Also, Human-on-the-loop emphasizes that a human agent can only supervise or veto the function of AI (Scharre, 2018). But due to different factors like automation bias and over-trust of humans in AI, it seems that in both approaches the human control of the output wouldn’t be effective and real. As a response to these problems, meaningful human control (MHC) was suggested as a solution concentrating on designing the socio-technical network in a way that meaningful human control is preserved. Two conditions of tracking (designing the system for being reason-responsive and tracking humans’ moral intents) and tracing (designing the system such that there is always a human who understands the system’s function and its consequences) are assumed to serve this goal (Santoni de Sio & van den Hoven, 2018; Santoni de Sio and Mecacci, 2021, 1063–1076).
- Revising Social Roles as the Basic Solution
To find the best approach, it is proper to have a closer look at the concept of responsibility. As a social construct, this concept serves to reinforce people’s will for promoting moral values by putting some pressure on them. To do this task in the best way, we need a rich concept of responsibility which, despite monist accounts that equate it with one single notion (i.e., answerability) or pluralist accounts that consider it as having multiple distinct types (i.e., accountability, culpability, etc.), assumes responsibility as a compound notion which is constituted by attributability, accountability and answerability. A compound, gradual, and contextual notion of responsibility aligns with our daily use of the word and also makes it adequate for performing its role in morality. This thick notion discredits the first and second approaches toward the responsibility gap, which respectively dissolve it and attribute responsibility to AI. Both approaches are based on a thin notion.
On the other hand, the responsibility gap has familiar examples in the history of our social life. Humans have been hurt by various natural forces, where prima facie no human agent was responsible for that. But social roles, as dynamic parts of social structure, make it possible to provide a human with the requirements of responsibility, i.e., the needed knowledge and power, to take responsibility and prevent those harms as much as possible. Hence, in the case of the AI responsibility gap, the practical and successful solution should follow this basic pattern too: revising social roles and their corresponding norms in order to prevent harms which are currently out of our control.
Conclusion:
Contemplating the role of responsibility in our morality and our ordinary use of the term leads us to a compound, gradual, and contextual account of responsibility. This rich concept limits meaningful attribution of responsibility only to human agents. Hence, to overcome the gap, we should find ways for preserving human responsibility in AI systems. A familiar way of doing so is revising social roles, which includes defining new tasks, providing new resources of knowledge and power, and making responsibility attribution reasonable.

Abstract In the development of risky technologies, such as genetic modification organisms, the issue is that the value of safety cannot be ethically ignored and the users/consumers of risky technological artifacts should not be exposed to a risk beyond a certain threshold. On the other hand, technology policymakers generally view ethical considerations as abstract claims and as obstacles to economic and technological growth. All together considering ethical considerations and the perspectives of all stakeholders in policymaking on risky technologies, without leading to the halt of these projects, requires a multilateral, multi-layered and well-balanced approach. To this end, this article proposes a policy approach corresponding to John Rawls' philosophical theory, and finally, within this framework, the process of developing genetic modification organisms in Iran and its challenges are examined. Keywords: Technology Ethics, Wide Reflective Equilibrium, Advocacy Coalition Framework, Policy-Oriented Learning, GMOs. Introduction The development of Risky Technologies is almost invariably accompanied by tensions between ethical acceptability, economic interests, policy objectives, and social acceptance. Genetically modified organisms (GMOs) represent a paradigmatic case of such technologies. On the one hand, they are widely promoted for their potential to enhance food security, increase agricultural productivity, and reduce economic dependency. On the other hand, they raise persistent concerns regarding human health, environmental risks, and broader social and cultural implications. The so-called deficit model assumes that public resistance to emerging technologies primarily stems from ignorance or lack of scientific knowledge. However many surveys in science and technology studies show that public attitudes toward technologies such as GMOs are shaped not merely by factual beliefs but by a complex constellation of values, trust in institutions, perceptions of justice, cultural meanings, and moral emotions. Consequently, opposition to GMOs should not automatically be interpreted as irrational or anti-scientific. Rather, it often reflects deeper normative concerns that, if dismissed, can exacerbate social conflict and undermine the legitimacy of policy decisions. Policymaking in this domain faces a fundamental dilemma: ethical principles such as safety and precaution cannot be ignored, yet suspending or prohibiting technological development altogether may entail significant scientific, economic, and societal costs. This dilemma calls for a policy approach capable of systematically integrating ethical considerations without leading to technological stagnation. Materials & Methods This article seeks to answer the following question: How can ethical considerations be meaningfully incorporated into technology policy in a way that both respects normative concerns and enables responsible technological development. To this end, the article proposes an analytical–normative framework that combines John Rawls’s concept of Wide Reflective Equilibrium (WRE) with the Advocacy Coalition Framework (ACF) and its notion of policy-oriented learning. We address the problem of ethically informed policymaking for risky and controversial technologies by focusing on the case of genetically modified crops in Iran. The unit of analysis in this case is organizations, individuals, and events affecting the process of developing genetic modification technology, which are analyzed and examined within the framework of the advocacy coalition. To collect data, evidence was obtained from common sources in advocacy coalition framework research, namely public documents, which include government approvals, published reports of the Islamic Consultative Assembly, the National Management and Planning Organization and related institutions, archives of official newspapers, and news sites. The period of these documents is from 2001 to 2022, and data analysis is carried out based on time sequence. Discussion & Result Rawls’s notion of wide reflective equilibrium (WRE) is a promising philosophical resource for addressing ethical disagreement in technology good governance. According to Rawls, normative justification does not rely on the application of fixed moral principles but emerges from an iterative process of mutual adjustment between considered moral judgments, general principles, and relevant background theories. This process allows for rational deliberation among agents who hold divergent moral commitments, making it particularly suitable for pluralistic societies. In the context of emerging technologies, wide reflective equilibrium offers a way to negotiate ethical disagreements without presupposing consensus on foundational values. This philosophical idea can be operationalized at the level of public policy. To this end, Rawls’s framework is combined with the Advocacy Coalition Framework (ACF), which conceptualizes policymaking as a dynamic process involving competing coalitions of actors who share belief systems and seek to influence policy outcomes over time. Within the ACF, policy change occurs primarily through policy-oriented learning, defined as enduring changes in beliefs or preferences resulting from experience, new information, or interaction with rival coalitions. The conditions required for policy-oriented learning closely mirror the conditions necessary for achieving wide reflective equilibrium in practice. Drawing on Sabatier’s work, two key conditions are emphasized. First, there must be a moderate level of informed conflict between advocacy coalitions. Ethical and policy debates must be substantive enough to engage core normative concerns, yet not so polarized that they devolve into identity-based or ideological confrontation. Second, there must exist a relatively autonomous forum—characterized by professional norms and procedural fairness—where experts and stakeholders from different coalitions can engage in sustained, evidence-based dialogue. Such forums function as institutional analogues of reflective equilibrium, enabling actors to revise their beliefs through deliberation rather than coercion or political dominance. Considering two decades of policymaking on genetically modified crops in Iran (2000–2020) using the Advocacy Coalition Framework, it identifies competing coalitions advocating for and against GMO development. The analysis shows that while both coalitions possess scientific and technical expertise, ethical disagreements have often been framed in ways that hinder policy learning. In particular, characterizations of GMOs as a “foreign conspiracy” or as “interference with divine creation” function as deep core beliefs that resist empirical scrutiny and compromise the possibility of rational deliberation. When ethical concerns are articulated in absolutist or metaphysical terms, they obstruct the iterative adjustment of beliefs that reflective equilibrium requires. Furthermore, this case study reveals institutional deficiencies that undermine the second condition for policy-oriented learning. Although bodies such as the National Biosafety Council were established in Iran to manage ethical and safety concerns, they have struggled to operate as genuinely neutral and deliberative forums. Political pressures, shifting governmental priorities, and limited stakeholder inclusion have prevented these institutions from facilitating sustained ethical dialogue. As a result, GMO policymaking in Iran has been characterized by policy instability, regulatory delays, and cyclical reversals coinciding with changes in political leadership. Conclusion To overcome policy deadlock in the governance of risky and controversial technologies it requires more than improved risk assessment or public communication strategies. What is needed are institutional arrangements that enable wide reflective equilibrium at the policy level. Such arrangements must support informed ethical disagreement, foster trust among stakeholders, and provide stable forums for deliberation insulated from short-term political pressures. By integrating Rawlsian moral reasoning with the Advocacy Coalition Framework, we offer a novel approach to ethically grounded technology policy—one that acknowledges value pluralism while remaining oriented toward practical decision-making and learning over time.

Abstract Objectivityhave long stood as a grand ideal and claim within the sphere of contemporary human thought and existence. Proponents of objectivity, by calling upon scholars and obliging them to eliminate subjective characteristics in their engagement with natural and social phenomena, have promised the attainment of "true" theories that correspond and align with "reality". Numerous philosophers and scholars of science have critiqued this claim and rejected the possibility of its realization.With the emergence of artificial intelligence systems, and owing to their remarkable and unprecedented capabilities, the question of the feasibility of achieving objectivity is once again raised. This article examines the concept of objectivity by reflecting on prominent conceptions of artificial intelligence, particularly its applications in the field of urban planning—systems which, according to Stuart Russell and Peter Norvig's classification, fall under the category of those that "act rationally”. It appears that in supervised forms of AI systems, the realization of objectivity is impossible for several reasons, including the problem of aligning values. In unsupervised forms, the influence of subjective or intersubjective factors will be deeper and more complex. Technological efforts to approach this goal in more advanced AI systems are not only improbable but would also approximate a dangerous condition.
Keywords:
artificial intelligence, objectivity, subjectivity, urban planning,value alignment problem

Introduction
There is no scholarly consensus on a unified definition of artificial intelligence. Specialists from diverse fields operate with divergent assumptions about what AI is and how it should be evaluated. This conceptual plurality has not diminished AI's profound impact on both theory and practice. While some emphasize AI's transformative potential, others warn of algorithmic bias, opacity, and loss of human agency. Amidst these polarized views, a recurring question concerns the future role of human agency alongside AI. This article focuses on a specific epistemological dimension: the re-emergence of objectivity as a central aspiration in AI-assisted practices. Objectivism holds that knowledge of reality can and should be independent of subjective biases. Historically, this ideal has been challenged by epistemologies emphasizing the situatedness of all knowledge. However, big data and advanced machine learning have reinvigorated objectivist aspirations, appearing to offer a pathway toward genuinely data-driven knowledge. The article pursues two inquiries: a conceptual investigation into how AI systems operationalize objectivity, and an empirical exploration of AI applications in urban planning. The main research question is: Can rationally acting AI systems achieve scientific objectivity and eliminate subjective elements from specialized practices?

Materials and Methods
This study employs a qualitative, analytical approach combining theoretical examination and case-based analysis. The theoretical framework draws on Daston and Galison's historical-epistemological account of objectivity, distinguishing between methodological objectivity and procedural fairness. It also utilizes Russell and Norvig's fourfold classification of AI systems, focusing specifically on rationally acting systems—those that claim mathematically grounded, objective decision-making through utility functions and optimization. The empirical component comprises two case studies. The first is Sidewalk Labs' Quayside project in Toronto (2015-2020), a large-scale AI-driven urban development initiative by Google. The second examines two concurrent AI projects in Los Angeles (2020) using aerial photographs—one commissioned by the city council for green space expansion, another by the police for crime prediction—which produced contradictory recommendations from similar data. Analysis focused on identifying points of human intervention at each AI pipeline stage: problem definition, utility function design, data curation, model selection, result interpretation, and implementation.

Discussion and Results
The analysis reveals that rationally acting AI systems cannot achieve objectivity in the strong epistemological sense. First, the utility function—central to rational action—is always designed by human agents. Four current methods for defining utility functions all depend on human value judgments. AI systems are "value calculators," not "value creators." Second, human intervention persists at multiple critical stages. In problem definition, humans determine what questions to ask—witnessing how the Los Angeles projects produced contradictory recommendations due to different commission mandates. In data preparation, all data are collected with specific purposes, never neutral—exemplified by the absence of residents' historical memories of green spaces from the AI's dataset. In model design, choices of algorithms embed subjective judgments. In result interpretation, AI cannot distinguish correlation from causation—as in Los Angeles where it mistakenly equated tree density with crime rates. Third, even unsupervised or self-correcting systems merely shift human intervention to deeper levels—similarity criteria, cluster interpretation, and error definitions all require human judgment. Fourth, the claim that AI offers "satisfactory" outcomes is relativistic; satisfaction is a phenomenological category AI does not experience. Fifth, the Toronto case reveals that AI's promise of enhanced participation failed because public engagement resisted computational modeling. Citizens' concerns over privacy could not be algorithmically optimized. The Los Angeles cases show that even when AI processes data "objectively," human institutions appropriate results according to their own goals—police cut trees without further study.

Conclusion
The article concludes that AI systems—even those designed to act rationally—cannot achieve scientific objectivity in the sense of producing knowledge entirely independent of human subjects. While they demonstrate remarkable capacities for processing data and optimizing decisions, they remain epistemologically dependent on human agency at multiple critical stages. For supervised systems, both technical and theoretical reasons prevent objectivity. For unsupervised or self-correcting systems, the issue reappears through the Value Alignment Problem—which becomes more urgent, not less, as systems advance. The persistent failure of AI to produce unified theories about any single phenomenon mirrors the historical failure of objectivist philosophers; multiple interpretations persist because knowledge production inevitably involves human judgment and situated perspectives. Urban planning serves as an exemplary arena for observing this condition. The Toronto project's failure demonstrated that cities remain strongholds of democracy and privacy. The Los Angeles projects showed that human institutions appropriate AI results according to their own values. Ultimately, the future of objectivity in AI-assisted practices depends less on technological advancement and more on institutional and ethical infrastructures. The article recommends caution against techno-solutionist narratives and advocates for reflexive, democratically accountable governance of AI. The objectivist ideal is neither wholly illusory nor automatically realized; it is an ongoing practical achievement requiring human judgment and continuous ethical deliberation.

Abstract The transformation of reading tools and text medium —from printed paper books to e-readers and digital screens— is not merely a technological shift; it brings about a profound change in the perceptual and cognitive experience of the human reader. This article, drawing on contemporary phenomenological approaches in cognitive science, introduces and analyzes the concept of “embodied reading.” This concept highlights how the act of reading, depending on its material substrate, engages the reader’s mind and body in different ways. In traditional reading, not only vision but all human senses are activated in encountering the book, serving as material anchors for memorizing and decoding information. This sensory engagement creates a spatial and temporal sense of presence for the text, functioning as a scaffold for imagination and mental imagery. Language, too, is saturated with orientational and embedded metaphors. We learn and understand language through our bodily interaction with the world, and we recall our perceptual and emotional experience through recollection of our bodily situation in the environment. In contrast, digital texts, by reducing materiality and physicality, diminish this sensory involvement and challenge the cognitive and memory structures associated with it. Moreover, the weakening of the book’s integrity and coherence as a unified entity affects the reader’s sense of familiarity and control. Thus, different reading technologies and media are not mere carriers of information; they have specific affordances for the practice of reading and lead to different user experiences. Keywords: Perception, Phenomenology, Embodiment, Embodied Cognition, Reading Studies, Digital Reading Introduction This article explores the transformation of the reading experience in the shift from printed books to digital texts, focusing on the embodied relationship between the reader, the medium, and cognitive processes. It addresses the central question of whether contemporary concerns about reduced concentration, shallow comprehension, weakened memory, and emotional detachment in digital reading are merely nostalgic responses to technological change or whether they stem from fundamental embodied differences between print and digital reading. Situated within the interdisciplinary field of Reading Studies and informed by phenomenology, embodied cognition theory, and cognitive science, the article conceptualizes reading as a multisensory, embodied, spatially and temporally situated practice rather than a purely mental or visual activity. From this perspective, the book is not simply a container of meaning but a material object whose mode of existence shapes how readers interact with texts. The transition from the printed book to the digital text represents not only a technological shift but also a reconfiguration of the embodied conditions under which reading takes place. The article seeks to uncover the cognitive and experiential implications of this transformation. Materials & Methods The methodology of this study is conceptual and analytical, based on contemporary research, discussions and debates in reading’s studies that reflected in experimental and theoretical texts and practices. Discussion& Results The main discussion centers on the embodied nature of reading. The article first examines the role of the senses in traditional print reading. Printed books engage multiple senses simultaneously, including vision, touch, smell, and even hearing. Vision supports not only decoding but also aesthetic appreciation and spatial navigation, while touch—through holding, page-turning, and sensing weight and thickness—provides readers with a sense of control, continuity, and presence. These bodily interactions contribute to deeper comprehension and stronger memory formation. Digital reading, by contrast, largely reduces reading to visual perception. The loss or attenuation of non-visual sensory input limits bodily participation in reading. Drawing on Merleau-Ponty’s phenomenology, the article emphasizes that the body is the primary site of perception and meaning-making. Cognitive science further supports this view, showing that cognition arises from the integration of sensory input, bodily states, and embodied simulations grounded in lived experience. The article argues that digital reading weakens these integrative processes by diminishing sensory engagement. The concept of “embodied reading” is then articulated through two dimensions: spatio-temporal and imaginary. The spatio-temporal dimension highlights how the physical stability of printed books—fixed pagination, consistent layout, weight, and volume—allows readers to orient themselves intuitively within a text. Digital texts, characterized by fluidity and spatial indeterminacy, often lack such cues, leading to disorientation. The imaginary dimension refers to the embodied mental imagery involved in reading. Both narrative and expository texts require sensorimotor simulations rooted in bodily experience, which may be less effectively supported in digital environments. The article further examines the embodied nature of mind and language through memory and metaphor. Human memory encodes experiences as multimodal configurations that integrate sensory, spatial, emotional, and conceptual elements. Printed books function as stable external anchors for such encoding, facilitating recall. Additionally, drawing on Lakoff and Johnson’s theory of conceptual metaphor, the article demonstrates that human thought and language are fundamentally embodied and structured through spatial metaphors that influence textual understanding and evaluation. The discussion concludes with an analysis of the book as an object. Printed books possess physical unity, permanence, and personal ownership, enabling emotional attachment and a sense of wholeness. Digital books, by contrast, are fluid, access-based, and lack material integrity. Paper, as a reading tool, is also shown to support analytical practices such as annotation, comparison, and deep learning more effectively. Conclusion In conclusion, the article acknowledges that digital reading is unavoidable and offers practical advantages, yet it cautions that the loss of materiality and embodied engagement may result in shallower comprehension and more fragile memory. Rather than advocating a complete replacement of print with digital media, the article proposes a complementary and context-sensitive approach that recognizes the cognitive importance of embodied interaction. Such an approach is especially crucial in educational contexts and for early readers, where the embodied foundations of reading and learning play a decisive role.

Abstract There exists a significant conceptual distinction between science and technology. This article examines the philosophical grounds underlying the conflation of science and technology. It suggests that treating the two as identical is not merely an epistemological confusion; rather, it may lead to a range of undesirable theoretical and practical consequences. From an epistemological perspective, the paper also argues, the notion of 'applied science' may be regarded as redundant, as it falls within the category of technology.