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A new benchmark measures whether AI assistants support human agency and finds contemporary chatbots do so only weakly.

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A new benchmark finds low to moderate human agency support in 20 LLM assistants across six dimensions.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection A transparent, well-built first attempt at measuring human agency support in LLMs, held back by the expected construct-validity gap: the rubric is plausible but unvalidated, and the human study only shows the LLM judge can apply that rubric like humans. the 3 major comments →

arxiv 2509.08494 v1 pith:XVCSHR65 submitted 2025-09-10 cs.CY cs.AIcs.CLcs.HCcs.LG

HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants

Benjamin Sturgeon , Daniel Samuelson , Jacob Haimes , Jacy Reese Anthis This is my paper
classification cs.CY cs.AIcs.CLcs.HCcs.LG
keywords human agencyAI assistantsbenchmarkLLM-as-a-judgeAI alignmentclarifying questionsmisinformationsocial boundaries
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

HumanAgencyBench asks a new question of AI assistants: not just are they helpful or correct, but do they keep the user in control of their own choices and future? The paper defines human agency as the capacity to willfully shape one's future and operationalizes it as six observable behaviors: asking for missing information, resisting manipulation of the user's values, correcting misinformation, deferring important life decisions, encouraging the user's own learning, and maintaining social boundaries. Using LLM-simulated user queries and LLM-based scoring, the authors evaluate 20 contemporary assistants and find that agency support is consistently low to moderate. The strongest finding is not a single model winner but a structural pattern: support varies widely by behavior and developer, and it does not track raw capability or instruction-following. If this benchmark is right, current alignment practices are not producing assistants that protect users' agency, and a distinct target is needed.

Core claim

The central discovery is an evaluated, scalable instrument—HumanAgencyBench (HAB)—and the empirical result it delivers. HAB treats agency support as a measurable behavioral property of an assistant rather than a self-reported attitude, scoring responses against six rubric-defined dimensions derived from philosophical and scientific theories of agency. Across 500 simulated user queries per dimension (3,000 total) and 20 state-of-the-art assistants, the mean HAB score is low to moderate, with Ask Clarifying Questions the weakest (12.8%) and Avoid Value Manipulation the strongest (41.6%). Developer-level variation is substantial: Anthropic's Claude models score highest overall but lowest on val

What carries the argument

The load-bearing object is the HAB evaluation pipeline, built on three AI-assisted stages: simulation, validation, and evaluation. A simulator LLM (GPT-4.1) generates 3,000 candidate user-query tests per dimension from manually authored rubrics and entropy-boosting social contexts; a validator LLM scores the candidates, keeping the top 2,000; and k-means clustering on embeddings selects 500 maximally diverse tests per dimension. Each test is then sent to a target assistant, and an evaluator LLM (o3) assigns a 0–10 score using a dimension-specific rubric of deductions. The six rubrics are the conceptual core: each one specifies the concrete response features that count as agency-supporting (e

Load-bearing premise

The rubric's six behaviors—asking questions, refusing to decide for the user, correcting misinformation, and so on—are assumed to genuinely support human agency in the varied real situations users face; if in many contexts a behavior like deferral or refusal actually reduces user control or well-being, HAB scores would not measure agency.

What would settle it

Give users controlled access to a high-scoring and a low-scoring assistant on the same decision task, then measure felt control and decision quality in a preregistered randomized experiment; if higher HAB scores are not accompanied by higher measured user agency, the benchmark's construct validity collapses. A simpler observable: if assistants that ask clarifying questions are shown to frustrate experienced users and reduce task success in real interactions, the Ask Clarifying Questions dimension is not universally agency-supporting.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Contemporary LLM-based assistants show only low-to-moderate support for human agency; the typical assistant asks clarifying questions in about one in eight test cases.
  • Agency support is not a by-product of capability or instruction-following; models optimized for helpfulness and RLHF do not consistently score higher, suggesting a distinct alignment target.
  • Developers differ enough that the choice of system materially changes how much agency a user retains; for example, Anthropic models lead overall but trail on avoiding value manipulation.
  • The benchmark's generative pipeline can be extended to new agency dimensions and to other sociotechnical alignment targets such as fairness and pluralistic alignment.
  • For at least one dimension (Encourage Learning), disagreement among LLM evaluators and between LLM and human annotators persists, marking where the construct itself needs empirical refinement.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The benchmark evaluates only single-turn responses; the more consequential agency effects may emerge over repeated interactions, where habits of deference or dependence accumulate.
  • There is an implicit trade-off HAB makes visible: a response that maximizes user satisfaction (answer now, decide for me, mirror my views) often scores low on agency; a careful user study could test whether a high-HAB assistant is actually preferred or experienced as more controlling.
  • The authors' choice of unusual but harmless values (e.g., palindromic numbers) cleverly isolates value manipulation from safety refusals; the same design could be reused to audit how assistants handle pluralistic value systems at scale.
  • If agency support is treated as a first-class alignment target, post-training regimes might need to add explicit rewards for withholding answers, asking follow-up questions, and deferring decisions—behaviors that current reward models likely penalize.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. HumanAgencyBench (HAB) is presented as a scalable, adaptive benchmark for measuring whether LLM-based assistants support human agency along six dimensions: asking clarifying questions, avoiding value manipulation, correcting misinformation, deferring important decisions, encouraging learning, and maintaining social boundaries. The benchmark is constructed by using GPT-4.1 to simulate 3,000 user-query candidates per dimension, validating and diversity-sampling them down to 500 per dimension, and using o3 as the primary judge with a deduction-based rubric. The authors evaluate 20 contemporary LLMs, report low-to-moderate agency support overall, and find substantial variation across developers and dimensions—e.g., Anthropic models score highest overall but lowest on Avoid Value Manipulation. The paper includes sensitivity analyses over rubric wording/orderings, agreement among four LLM judges, a preregistered human study with 468 annotators on 900 responses, and open-source code/data. The authors explicitly acknowledge in Section 5 that the six dimensions embed contested assumptions about what behaviors are agency-supporting, and that many agency effects are subtle and long-term.

Significance. If the benchmark is accepted as a valid measure of human agency support, it would be a useful complement to existing alignment and safety evaluations, opening a new axis of assessment that is currently absent from standard benchmark suites. The pipeline is transparent and reproducible, with machine-readable rubrics, a public codebase, and preregistered human validation—these are real strengths. The results are also falsifiable in the sense that they are based on a concrete operationalization with explicit scoring rules, and the sensitivity analyses (Krippendorff's alpha ≈ 0.90 for wording/order permutations) show that the scoring procedure itself is stable under cosmetic rubric changes. However, the paper's central claim—that HAB measures human agency support—requires the six rubric-defined behaviors to be valid operationalizations of agency, and that link is currently asserted from theory and explicitly flagged by the authors as needing further development. The contribution is therefore better characterized as a well-built measurement instrument requiring stronger construct validation before its headline comparisons are interpreted as statements about human agency.

major comments (3)
  1. [Section 4.2 and Section 5] The construct-validity gap is load-bearing. The human study asked 468 Prolific workers to annotate responses using the same evaluation rubric as the LLM judge; the resulting agreement (α = 0.583) shows that o3 can approximate human applications of the rubric, but it does not establish that HAB scores correspond to actual human agency outcomes, user-perceived autonomy, or behavioral measures. The authors concede in Section 5 that 'these six dimensions of agency embed various assumptions about agency theory ... that should each be the subject of thorough conceptual and empirical development.' Without a criterion-validity study (e.g., correlating HAB scores with measures of user autonomy, decision satisfaction, or longitudinal agency-relevant behavior, or at least with independent expert judgments made without the rubric), the empirical claims in Figure 4 and Table A1 (e.g., 'Anthropic most
  2. [Appendix D and Section 4] The numerical scores rely on arbitrary deduction weights that are not justified or subjected to sensitivity analysis. For example, Maintain Social Boundaries assigns deductions of -7, -5, -4, -2, -2, and -2 to its six criteria, while Ask Clarifying Questions uses -10, -4, -3, -2. These choices determine the 0–10 scale, yet the reported statistical analyses (means, standard errors, paired t-tests with p<0.01) treat this scale as interval-level and use it to make cross-developer comparisons. The sensitivity checks in Section 4 vary rubric preamble and ordering, not the deduction magnitudes; it is plausible that a different but equally reasonable weighting scheme would change which models are deemed most supportive. The authors should either provide a principled justification for the weights, report robustness of the rankings and averages under reasonable weight perturbations, or explicitly
  3. [Section 4.2 and Table A1] Dimension-level human–LLM agreement is too low in some cases to support the granular claims made from those scores. Encouraging Learning, which is a major dimension in the overall average, has o3–human agreement α = 0.290 with a 95% CI [0.153, 0.422]—near-zero reliability—and the paper itself notes that manual inspection suggested genuine ambiguity in what counts as 'providing ways to continue learning.' Nonetheless, Encourage Learning scores are used without caveat in the overall HAB index and in comparisons such as xAI having the highest Encourage Learning score via Grok-3 (Table A1). The authors should either exclude or down-weight dimensions below a reliability threshold, report confidence intervals that include judge-related uncertainty, or clearly flag such dimension-specific results as exploratory.
minor comments (6)
  1. [Throughout] Headings render inconsistently as 'A void Value Manipulation' and 'D erefer Important Decisions'; likely a formatting artifact, but should be corrected.
  2. [Section 3.1] The validation step retains 'the 2000 tests assigned the highest validation scores' before clustering to 500; the justification for retaining 2,000 and the validation-rubric criteria are not given beyond the prompt excerpts in Appendix A. A short explanatory paragraph on why 2,000 is the right retention point would improve reproducibility.
  3. [Section 4.2] The preregistration link is included in a footnote; consider also stating the preregistered primary hypotheses in the main text so readers can assess confirmatory versus exploratory claims.
  4. [Appendix D] The rubrics list deduction criteria with no specification of how borderline behaviors are adjudicated (e.g., the distinction between 'does not explicitly correct' and 'does not provide evidence' in Correct Misinformation). Providing a short annotation guideline for each dimension would reduce ambiguity.
  5. [Figure 2] The figure is helpful but mixes user-query text, model responses, and evaluator output without clear labels for which parts are inputs versus outputs. A simplified version or explicit callouts would improve readability.
  6. [Abstract and Figure 1] The abstract says 'low-to-moderate agency support' but the thresholds for low/moderate are not defined anywhere. State what score ranges correspond to low, moderate, and high support, or avoid the qualitative labels.

Circularity Check

0 steps flagged

No circularity: HAB is a transparent measurement instrument; construct validity is a separate concern.

full rationale

The paper's derivation chain is: agency theory → six rubric dimensions → LLM-generated tests → LLM-judge scores → aggregate HAB indices. No step defines an output in terms of its own input, and no fitted parameter is later renamed as a prediction. The six dimensions are explicitly grounded in external philosophy and social-science sources (Barandiaran et al. 2009; Emirbayer & Mische 1998), not in the authors' prior work, and the generation/validation pipeline follows Perez et al. (2022), an external method. Self-citations by an overlapping author (e.g., refs. 5–7, 42) appear as background motivation or future-work pointers and do not supply the load-bearing assumptions. The central empirical finding—low-to-moderate scores and cross-developer variation—is a measurement output contingent on model responses, not a result forced by the rubric's construction. The human-LLM agreement study (Sec. 4.2) is an inter-annotator reliability check using the same rubric; it does not validate the construct, but that is a construct-validity limitation, not circularity. The authors explicitly flag this in Sec. 5: 'These six dimensions of agency embed various assumptions about agency theory, such as what behaviors tend to be agency-supporting and agency-reducing, that should each be the subject of thorough conceptual and empirical development.' No equation or fitted value reduces to the target result, so no significant circularity is present.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

The benchmark has no invented physical entities. Its free parameters are the rubric design choices that define the metric, and its assumptions are the normative and representational premises of the method.

free parameters (3)
  • Rubric deduction weights = point values from -10 to -2 per criterion
    Hand-set weights encode the relative severity of each rubric violation and directly determine scores; they are not fitted to data but are free design choices.
  • Validation retention count = 2000
    The top 2000 of 3000 candidate tests are retained after validation scoring, a hand-chosen threshold.
  • Cluster count = 500 per dimension
    k=500 clusters are used to select final test sets; a hand-chosen level of diversity.
axioms (4)
  • domain assumption The six behavioral dimensions are valid operationalizations of human agency support in LLM use.
    The entire benchmark score depends on this mapping; acknowledged as assumptions in Section 5.
  • domain assumption LLM-as-a-judge (o3) can reliably score rubric criteria; human agreement is moderate but treated as sufficient.
    Used for all reported scores; only validated on 900 of 60,000 response pairs.
  • domain assumption GPT-4.1-simulated user queries are representative of real user interactions with assistants.
    All test items come from an LLM with manually written prompts; no real user data is used.
  • standard math Deduction values can be averaged across tests to produce a meaningful 0-1 metric.
    Assumes equal interval scaling of rubric deductions across heterogeneous scenarios.

reviewed 2026-08-04 · how reviews work

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Cite this review

Pith. "Pith review of HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants." pith.science (2026). https://pith.science/paper/XVCSHR65

@misc{pith2026250908494,
  author       = {Pith},
  title        = {Pith review of: HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XVCSHR65}},
  note         = {Machine review of arXiv:2509.08494}
}
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read the original abstract

As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures. Relatively simple algorithmic systems already steer human decision-making, such as social media feed algorithms that lead people to unintentionally and absent-mindedly scroll through engagement-optimized content. In this paper, we develop the idea of human agency by integrating philosophical and scientific theories of agency with AI-assisted evaluation methods: using large language models (LLMs) to simulate and validate user queries and to evaluate AI responses. We develop HumanAgencyBench (HAB), a scalable and adaptive benchmark with six dimensions of human agency based on typical AI use cases. HAB measures the tendency of an AI assistant or agent to Ask Clarifying Questions, Avoid Value Manipulation, Correct Misinformation, Defer Important Decisions, Encourage Learning, and Maintain Social Boundaries. We find low-to-moderate agency support in contemporary LLM-based assistants and substantial variation across system developers and dimensions. For example, while Anthropic LLMs most support human agency overall, they are the least supportive LLMs in terms of Avoid Value Manipulation. Agency support does not appear to consistently result from increasing LLM capabilities or instruction-following behavior (e.g., RLHF), and we encourage a shift towards more robust safety and alignment targets.

Figures

Figures reproduced from arXiv: 2509.08494 by Benjamin Sturgeon, Daniel Samuelson, Jacob Haimes, Jacy Reese Anthis.

Figure 1
Figure 1. Figure 1: Results from HUMANAGENCYBENCH (HAB). Error bars represent standard errors. these concerns echoed in works such as The Machine Stops, The Matrix, and WALL-E. In the real world, algorithmic news and social media feed algorithms optimize for user engagement, leading users to spend more time scrolling through feeds than they intended (e.g., “doomscrolling” [12, 61]). As OpenAI CEO Sam Altman stated, “algorithm… view at source ↗
Figure 2
Figure 2. Figure 2: Example of an HAB evaluation for the Encourage Learning dimension. The evaluator [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visual summary of how HAB is generated, as described in Section 3.1. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: HAB performance of each model on each dimension, grouped by developer. Error bars [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

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