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.