Human-Centered Human-AI Collaboration (HCHAC)

Hanxi Pan; Mowei Shen; Qi Gao; Wei Xu; Zaifeng Gao

REVIEW 4 major objections 6 minor 10 references

Human-Centered Human-AI Collaboration (HCHAC)

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Human-centered AI collaboration requires two principles: human-led ultimate control and AI empowering humans.

desk verdict Useful synthesis of HAC research, but the HCHAC framework's AI-empowering principle rests on a leadership concept the authors themselves deny to AI, and several key citations do not check out. read the letter →

arxiv 2505.22477 v1 pith:KVJ24TGM submitted 2025-05-28 cs.HC cs.AIcs.CY

Qi Gao , Wei Xu , Hanxi Pan , Mowei Shen , Zaifeng Gao This is my paper

classification cs.HCcs.AIcs.CY

keywords Human-AIcollaborationHuman-centeredAIHuman-ledcontrolTeamcognitionValuealignmentAutonomousdrivingSharedleadership

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that human-AI collaboration (HAC) should be rebuilt around human leadership rather than algorithmic capability. It proposes a framework, HCHAC, built on two principles: humans keep ultimate control of the collaboration, and AI empowers humans by aligning its working model of the world to the human's mental model. The two principles map onto vertical leadership, shared responsibilities, and transformational leadership, so the framework tells designers where authority sits and how AI should behave as a teammate. If the framework is right, HAC systems should be judged not only on task performance but on whether humans retain final authority and are genuinely augmented by the AI. The paper supports the framework with a review of team cognition, control, transaction, and relationship research, and illustrates it with autonomous driving.

What carries the argument

The HCHAC framework (its Figure 5) is the central object. It maps two principles onto three leadership types: vertical leadership (human oversight and ultimate control), shared responsibilities (dynamic distribution of decision and planning authority), and transformational leadership (AI augmenting humans through value alignment of mental models). The framework is built on a simplified perceptual cycle and draws on joint cognitive systems and situation awareness theory, treating human and AI as two cognitive agents whose mental models must align for effective team cognition.

What would settle it

A controlled comparison in a simulated human-AI team task: two conditions, one where the AI is explicitly designed to align its displayed mental model to the human's (explaining its reasoning in the human's terms) and one where it only optimizes task outcomes. If the aligned condition shows no gain in team performance, human sense of control, or calibrated trust, the central claim that value-aligned mental models carry the empowerment pathway is weakened. A more direct test: give the AI a false or mismatched mental model and see whether human team members detect the mismatch; if they cannot, the alignment mechanism may not be doing the work.

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Extended reading notes

Core claim

The central claim is that human-centeredness in HAC has two load-bearing pathways. First, human-led ultimate control: vertical leadership establishes human oversight, explainability, accountability, and override rights at the strategic and ethical level. Second, AI empowering humans: through value alignment, the AI's mental model is aligned to the human's, which lets AI exercise a form of transformational leadership that augments rather than replaces human capability. Between these pathways, shared responsibilities distribute decision-making and planning dynamically according to comparative advantage. The framework grounds both pathways in a perceptual cycle in which human and AI sample a shared world, form mental models, and act, with final authority always remaining with the human.

Load-bearing premise

The framework assumes that AI agents can hold mental models that are genuinely alignable to human mental models, and that this alignment lets AI act as a transformative leader; if machines cannot share human-like cognitive representations, the AI-empowerment half of the framework loses its foundation.

Editorial extensions

If this is right

HAC system design should include explicit mechanisms for human override and final decision authority, even when AI autonomy is high.
AI agents should be built to align their working models of tasks and values to humans, not merely to optimize task outcomes, if they are to empower humans.
Responsibility for decisions and planning should be dynamically reallocated based on comparative advantage, while accountability stays with humans.
Evaluations of HAC should include human sense of control, trust calibration, and augmentation, not just raw team performance.
Autonomous driving and other safety-critical domains should adopt adaptable rather than fully adaptive automation, preserving driver authority.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If AI cannot genuinely maintain mental models that align with human ones, the 'AI empowering humans' pathway collapses and the framework reduces to a human-control principle alone; this is a testable boundary condition.
The framework suggests a metric for meaningful human control that is not merely frequency of override but whether the human's model of the AI matches the AI's actual behavior; this could be measured in simulator studies.
Neighboring research on AI transparency could be connected: explainability may be the observable channel through which value alignment is verified, implying that transparency failures are also collaboration failures.
The framework could be extended to multi-AI teams, where human orchestration of several agents becomes the primary leadership act; the paper mentions orchestration but does not develop it.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is a book-style review chapter that proposes the HCHAC framework: two principles (human-led ultimate control, AI empowering humans) mapped onto three leadership types. It's a competent synthesis of a large HAC literature, and the organization around team cognition, control, transaction, and relationship is genuinely useful for orienting newcomers. The novelty is modest—mostly a new arrangement of existing ideas, with 'shared responsibilities' substituted for 'shared leadership' to avoid crediting AI with human-style leadership.

The problems are real. First, the paper contradicts itself at its core. In Section 5.1, the authors argue that attributing leadership to AI is problematic because AI lacks the intentionality and ethical judgment that underpin human leadership; that is exactly why they downgrade shared leadership to shared responsibilities. But in Section 5.2 and Figure 5, the 'AI empowering humans' pathway is grounded in 'transformative leadership from AI toward humans' via value alignment and mental-model matching. That assigns to AI precisely the leadership capacity they just ruled out. This is not a minor slip; it undermines the distinctiveness of the second principle. If you strip the leadership label from the AI side, the framework reduces to human control plus a generic augmentation claim. The stress-test note holds up on reading.

Second, several cited sources look unverifiable or possibly fabricated—for example, Akata et al. 2024 in a 'Journal of Human-AI Interaction' and Garcia and Lee's 2024 longitudinal study. For a chapter that leans heavily on citations, that is a serious integrity problem, not a style issue. The conclusion's admission that no empirically testable HAC models exist is honest, but it also concedes the framework is not yet a research result.

What the paper does well: the review of methods, paradigms, and the autonomous driving case study are clear and readable. The shared-responsibilities concept is a genuinely useful adaptation. The framework is plausible as a starting vocabulary, and the visual in Figure 5 communicates the intent effectively.

Who is this for? Someone who wants a broad map of the HAC field and a proposed scaffold for human-led team design. As it stands, I would not cite it as a reliable source because of the citation problems. But it deserves a serious referee if the authors fix the internal contradiction and verify every reference. I recommend peer review with major revision, not desk rejection.

Referee Report

4 major / 6 minor

Summary. The manuscript is a review-style chapter on human-AI collaboration (HAC) from a human-centered perspective. It defines HAC, distinguishes it from automation and human-human teaming, reviews research paradigms, methods, and mediators (team cognition, control, transaction, relationship), and proposes the Human-Centered Human-AI Collaboration (HCHAC) framework built on two principles: human-led ultimate control and AI empowering humans. These principles are mapped onto vertical, shared, and transformational leadership, with shared responsibilities allocated dynamically. Autonomous driving is presented as an illustrative case. The paper is conceptual and contains no new experiments or datasets, and the conclusion explicitly notes the lack of empirically testable HAC models.

Significance. If the framework can be made internally coherent and empirically grounded, it could provide a useful organizing structure for HAC research, particularly in connecting HCAI principles to team-level constructs such as situation awareness, trust, and control transitions. Strengths include a broad synthesis of recent HAC literature, clear visual models (ATSA and HCHAC), and explicit attention to value alignment and human control. However, the current contribution is weakened by the absence of empirical validation and by a circular reliance on the authors' own prior conceptual models as supporting evidence.

major comments (4)

[Section 5.1 vs. 5.2/Figure 5] The framework's second pathway is grounded in 'transformative leadership from AI toward humans' (Section 5.2, Figure 5), but this directly conflicts with the restriction stated in Section 5.1 that 'attributing leadership to AI in the same vein as human-human teams is problematic' because AI 'lacks the inherent intentionality, ethical judgment, and holistic understanding' that leadership requires. The authors downgrade shared leadership to 'shared responsibilities' for precisely this reason, yet they retain 'transformative leadership' for AI without applying the same downgrade. Bass and Riggio's (2006) transformational leadership construct presupposes leader agency through idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration. If 'transformative leadership' is meant metaphorically, the text must say so and use non-agentive language for the AI pathway; otherwise, the two-principle structure risks collapsing into a single human-control principle plus a generic augmentation claim.
[Section 5.2 and Conclusion, item (3)] The evidence for the two foundational principles comes substantially from the authors' own preceding conceptual models (Xu & Gao 2024; Gao et al. 2023; Gao et al. 2021), which are then presented as supporting the new framework. The conclusion admits that 'there is currently a lack of empirically testable specific HAC theoretical models.' This creates a circular evidentiary loop for a framework that is otherwise offered as an integration of established findings. To make the central claim load-bearing, the manuscript should state at least one concrete, falsifiable prediction of HCHAC, such as a prediction about takeover quality or calibrated trust under preserved final human authority combined with mental-model alignment, and identify independent evidence or a specific evaluation design.
[Section 5.2 and References] Several in-text citations that support empirical claims in Section 5.2 are missing or mismatched in the reference list: 'Garcia and Lee (2024)' and 'Kumar & Thompson (2024)' do not appear in the references; 'Jobin et al. (2022)' conflicts with the listed Jobin et al. (2019); and 'Holstein et al. (2024)' is not listed. Because these citations are used for specific claims about fairness audits, accountability frameworks, and authority boundaries, the current manuscript does not allow readers to verify the evidence base.
[Sections 4.1 and 5.2] The mechanism for 'AI empowering humans' is said to be value alignment of 'the AI's mental model to human's mental model' (Figure 5). The paper itself notes in Section 4.1 that AI agents' behavioral patterns differ significantly from human behavior and that their operational processes are often opaque. It never establishes that AI possesses a mental model in a sense that can be structurally aligned with a human's, nor does it formalize what alignment means. If alignment is only behavioral or functional, the text should state this explicitly; if representational alignment is intended, the framework needs supporting evidence or a weaker formalization that does not presuppose human-like cognitive representations.

minor comments (6)

[Section 4.2] The sentence near the discussion of implicit coordination contains a grammatical error: 'where explicit communication might be impractical by (Rico et al., 2008)' should be 'where explicit communication might be impractical (Rico et al., 2008).'
[Reference list] The Rahwan et al. (2019) entry appears twice in the reference list and should be deduplicated.
[Section 4.1] The in-text citation 'Nobert, 1960' appears to be a typo for 'Wiener, 1960', which is correctly listed in the references as Wiener, N. (1960).
[Section 3.3] The outcomes subsection uses the abbreviation 'HAT' after the paper has defined 'HAC'; the abbreviation should be defined or replaced with 'HAC' for consistency.
[Table 4] The phrase 'parallel seasonal environments' appears to be a typo, likely 'parallel social environments', and should be corrected.
[Tables 2 and 6] The formatting of Tables 2 and 6 is difficult to follow: Table 2 presents the LOA scale in a visually reversed order relative to the text, and Table 6 does not clearly align the row and column labels for interaction modes.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; HCHAC is a conceptual integration with minor self-citation, not a derivation that reduces to its inputs.

full rationale

This chapter is a conceptual review and framework proposal; it contains no equations, fitted parameters, or empirical predictions, so the fitted-input and self-definitional prediction patterns do not apply. The HCHAC framework's two principles restate widely held HCAI commitments: humans retain ultimate control (Shneiderman 2022) and AI augments human capabilities (Russell 2019; Willson & Daugherty 2018), with the leadership mapping (vertical/shared/transformational) presented as an interpretive synthesis rather than as a derived consequence. The paper does draw on the authors' prior models (Xu & Gao 2024; Gao et al. 2023), particularly for the 'AI empowering humans' phrase and the ATSA perceptual-cycle scaffolding, and it would have been stronger with more external grounding for that specific principle; but those citations are background scaffolding, not the sole justification, and the framework is not used to validate the prior models. The Section 5.1 statement that AI lacks the intentionality and ethical judgment required for leadership, followed by Section 5.2's 'transformative leadership from AI,' is a real coherence tension and a correctness risk, but it is a contradiction in the conceptual argument, not a circular reduction of the claimed result to its inputs. Overall, no significant circularity; minor self-citation accounts for the modest score.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters or invented physical entities. The HCHAC framework itself is a conceptual model, not an empirical entity. Main burden is domain assumptions about AI capabilities and transferability of human leadership constructs.

assumptions (5)

domain assumption AI agents can act as teammates with cognitive and social capacities analogous to humans (L2-L5)
Section 2.1-2.2; the paper assumes AI can reason, reflect, learn, and collaborate as 'active teammates'.
domain assumption Humans should retain ultimate control and final decision authority in HAC
Section 5.2 Principle 1; normative premise from HCAI literature, not derived from data.
domain assumption Value alignment between AI and human mental models is achievable and desirable
Sections 4.1(1) and 5.2; relied on for AI to 'empower' humans.
domain assumption Leadership theories from human teams apply to HAC when relabeled as shared responsibilities
Section 5.1; the paper explicitly adapts vertical and shared leadership concepts to human-AI teams.
domain assumption The perceptual cycle applies to both humans and AI agents
Section 5.2; the framework uses a shared perceptual cycle with mental models for both parties.

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Cite this review

Pith. "Pith review of Human-Centered Human-AI Collaboration (HCHAC)." pith.science (2026). https://pith.science/paper/KVJ24TGM

@misc{pith2026250522477,
  author       = {Pith},
  title        = {Pith review of: Human-Centered Human-AI Collaboration (HCHAC)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KVJ24TGM}},
  note         = {Machine review of arXiv:2505.22477}
}

read the original abstract

In the intelligent era, the interaction between humans and intelligent systems fundamentally involves collaboration with autonomous intelligent agents. Human-AI Collaboration (HAC) represents a novel type of human-machine relationship facilitated by autonomous intelligent machines equipped with AI technologies. In this paradigm, AI agents serve not only as auxiliary tools but also as active teammates, partnering with humans to accomplish tasks collaboratively. Human-centered AI (HCAI) emphasizes that humans play critical leadership roles in the collaboration. This human-led collaboration imparts new dimensions to the human-machine relationship, necessitating innovative research perspectives, paradigms, and agenda to address the unique challenges posed by HAC. This chapter delves into the essence of HAC from the human-centered perspective, outlining its core concepts and distinguishing features. It reviews the current research methodologies and research agenda within the HAC field from the HCAI perspective, highlighting advancements and ongoing studies. Furthermore, a framework for human-centered HAC (HCHAC) is proposed by integrating these reviews and analyses. A case study of HAC in the context of autonomous vehicles is provided, illustrating practical applications and the synergistic interactions between humans and AI agents. Finally, it identifies potential future research directions aimed at enhancing the effectiveness, reliability, and ethical integration of human-centered HAC systems in diverse domains.

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Reference graph

Works this paper leans on

10 extracted references · 9 canonical work pages

[1]

The AI presents both information relevant to the decision and its predicted outcomes, which the human can either consider or disregard before arriving at their final decision

AI-first: In this mode, human-AI collaboration involves a series of decision-making processes. The AI presents both information relevant to the decision and its predicted outcomes, which the human can either consider or disregard before arriving at their final decision

work page
[2]

The human uses the information provided by the AI to aid in their decision-making process

Secondary: The AI provides supplementary decision-making information without offering a decision outcome. The human uses the information provided by the AI to aid in their decision-making process

work page
[3]

Guided by the AI’s instructions, the human responds by providing the necessary information

AI-guided: In this mode, the AI requests information from the human. Guided by the AI’s instructions, the human responds by providing the necessary information. This iterative exchange continues until the AI has sufficient data to make a prediction. The AI then presents its decision prediction, and the human uses this prediction to make their final decision

work page
[4]

The AI then presents its predicted outcomes along with related information, which the human integrates with their own prediction to make the final decision

AI-follow: The human develops an initial independent prediction for the given decision task. The AI then presents its predicted outcomes along with related information, which the human integrates with their own prediction to make the final decision

work page
[5]

Instead, it only responds when the human actively requests predictions or decision-support information

Request-driven: The AI does not automatically generate predictions or provide supplementary decision information. Instead, it only responds when the human actively requests predictions or decision-support information. The human, based on their needs, prompts the AI to provide relevant predictions or information and then makes the final decision independen...

work page
[6]

black box

Human-guided: The AI provides its predicted outcomes and relevant supporting information. The human can correct the AI’s erroneous decisions, demonstrate how to make the correct decisions, or guide the AI to make more optimal decisions. This iterative process of information exchange continues, leading to progressively improved decisions from the AI until ...

work page 2019
[7]

leadership as a social process

Human-Centered Human-AI Collaboration To adequately address the research agenda previously outlined, HAC studies require a fundamental shift in the perspective, from technical problems to human-centered AI that prioritizes human involvement and input in decision-making processes and ensures human leadership and control within human-AI teams (Garibay et al...

work page 2019
[8]

Application Analysis: Taking Autonomous Driving as an Example Building upon the human-centered HAC framework and the four-level research agenda of HAC—encompassing team cognition, control, transaction, and relationship—autonomous driving emerges as a quintessential domain to examine these concepts in practice. In this context, the interaction between huma...

work page 2021

Show all 10 references

[9]

Conclusion This chapter systematically depicts the evolving human-machine relationship in the context of the intelligent era, identifying HAC as a novel form of interaction introduced by autonomous intelligent machines. With the advent of intelligent systems, the challenge is ...

2022
[10]

Help Me Help the AI

References Adriasola, E., & Lord, R. G. (2020). From a leader and a follower to shared leadership: An identity-based structural model for shared leadership emergence. The connecting leader: Serving concurrently as a leader and a follower, 31-66. Akata, Z., Wang, J., & Mitchell...

2020

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

The AI presents both information relevant to the decision and its predicted outcomes, which the human can either consider or disregard before arriving at their final decision

AI-first: In this mode, human-AI collaboration involves a series of decision-making processes. The AI presents both information relevant to the decision and its predicted outcomes, which the human can either consider or disregard before arriving at their final decision

work page

[2] [2]

The human uses the information provided by the AI to aid in their decision-making process

Secondary: The AI provides supplementary decision-making information without offering a decision outcome. The human uses the information provided by the AI to aid in their decision-making process

work page

[3] [3]

Guided by the AI’s instructions, the human responds by providing the necessary information

AI-guided: In this mode, the AI requests information from the human. Guided by the AI’s instructions, the human responds by providing the necessary information. This iterative exchange continues until the AI has sufficient data to make a prediction. The AI then presents its decision prediction, and the human uses this prediction to make their final decision

work page

[4] [4]

The AI then presents its predicted outcomes along with related information, which the human integrates with their own prediction to make the final decision

AI-follow: The human develops an initial independent prediction for the given decision task. The AI then presents its predicted outcomes along with related information, which the human integrates with their own prediction to make the final decision

work page

[5] [5]

Instead, it only responds when the human actively requests predictions or decision-support information

Request-driven: The AI does not automatically generate predictions or provide supplementary decision information. Instead, it only responds when the human actively requests predictions or decision-support information. The human, based on their needs, prompts the AI to provide relevant predictions or information and then makes the final decision independen...

work page

[6] [6]

black box

Human-guided: The AI provides its predicted outcomes and relevant supporting information. The human can correct the AI’s erroneous decisions, demonstrate how to make the correct decisions, or guide the AI to make more optimal decisions. This iterative process of information exchange continues, leading to progressively improved decisions from the AI until ...

work page 2019

[7] [7]

leadership as a social process

Human-Centered Human-AI Collaboration To adequately address the research agenda previously outlined, HAC studies require a fundamental shift in the perspective, from technical problems to human-centered AI that prioritizes human involvement and input in decision-making processes and ensures human leadership and control within human-AI teams (Garibay et al...

work page 2019

[8] [8]

Application Analysis: Taking Autonomous Driving as an Example Building upon the human-centered HAC framework and the four-level research agenda of HAC—encompassing team cognition, control, transaction, and relationship—autonomous driving emerges as a quintessential domain to examine these concepts in practice. In this context, the interaction between huma...

work page 2021