cs.HCSep 18, 2026
SaveValue-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw
Organizations: College of Information Science University of Arizona Tucson, Arizona, United States
Abstract
Users increasingly delegate work to autonomous AI agents, yet evaluations typically measure task completion rather than the values users prioritize. Using Value Sensitive Design, we analyzed, with LLM assistance, 73,093 first-person Reddit posts about using OpenClaw, each for its human value, agent aspect, value fulfillment, and user outcome. The 21 values form six value groups, including Autonomous, Dependable, and Affordable Operation, Bounded Reach, Reviewability, and Equitable Access. Relative to each aspect's corpus share, values clustered not at the agent's outputs but at the operating conditions users set around a run. Values were usually met where users described what the agent delivered, in five of six groups, and mostly unmet where users described supervising it, in all six groups. We conceptualize this pattern as value-sensitive delegation. Supporting human values requires attention not only to what an agent accomplishes, but to the conditions users set around delegation, including cost, access, and oversight.
Figures & tables
| Value group | Definition | Human values (Friedman et al.’s list ( Friedman et al., 2006 ) ; † study-specific, developed for this corpus following ( Le Dantec et al., 2009 ) ) | |
| Dependable Operation | Whether the agent’s service holds and can be repaired | dependability†, trust, repairability† | |
| Autonomous Operation | The user’s own goals and state while the agent acts for them | autonomy, human welfare, calmness | |
| Affordable Operation | The money and resources a run consumes | affordability†, resource stewardship†, environmental sustainability | |
| Bounded Reach | What the agent may access and who controls that access | security†, privacy, identity, property ownership, data sovereignty†, contextual integrity† | |
| Reviewability | Whether users can see, approve, and answer for what the agent does | transparency†, meaningful human control†, accountability, informed consent | |
| Equitable Access | Who can use the agent at all | universal usability, freedom from bias | |
Table 1. The coding schema. The table lists six value groups constructed for this analysis, the 18 agent aspects in corpus-frequency order, and the nine user outcomes. Appendix B gives the operational definitions and boundary rules used in coding; Appendix C adds a definition and an example excerpt for each of the 21 values.
Figure 1. Distribution of values and their six value groups in the RQ1 sample ( ). Solid bars show the number of posts of each value. The shaded group bars show the number of posts in each value group; the six group counts therefore sum to the RQ1 sample. Bar chart showing 21 primary human values nested within six value groups. Autonomy and dependability are the two largest values, followed by affordability, resource stewardship, and universal usability. Dependable Operation, Autonomous Operation, and Affordable Operation are the three largest groups.
Figure 2. Distribution of agent aspects across the six value groups in the RQ1 sample ( ). Each cell shows the percentage of posts in a given value group coded for that agent aspect, and each group column sums to 100%. The first column gives each aspect’s share of the whole corpus; orange marks a larger share inside the group than in the corpus (O/E ratio above 1) and blue a smaller share, and Appendix F defines the ratio. Matrix of 18 agent aspects by six value groups. Affordable Operation is concentrated on resource accounting, Bounded Reach on environment access, Equitable Access on system access, and Reviewability on observability and human oversight. Dependable Operation and Autonomous Operation both most often concern the model core, but differ in several less frequent aspects.
Figure 3. Value fulfillment in the RQ1 sample ( ). Panel (a) reports met and not-met shares for the six value groups. Panel (b) reports the same shares for the eight most frequent agent aspects. Two stacked bar charts. Autonomous Operation has the highest group-level met rate at 77.3 percent, and Equitable Access has the lowest at 42.8 percent. Among the eight most frequent agent aspects, action effects have the highest met rate and resource accounting the lowest.
Figure 4. Observed value fulfillment and fulfillment expected from the distribution of agent aspects in each group in the RQ1 sample ( ). Differences are observed minus expected percentage points; intervals are conditional 95% percentile bootstrap intervals from 2,000 post-level resamples within each value group. Dot-and-interval plot showing observed-minus-expected fulfillment for six value groups. Autonomous Operation is 17.0 pp above expectation, and Affordable Operation is 3.5 pp above. Reviewability, Equitable Access, Dependable Operation, and Bounded Reach are below expectations.
Figure 5. Value group by user outcome among the 44,767 outcome-coded posts. Each cell describes the post count and the met rate, the share of its posts whose human value was coded as met. Blue is majority met, red is majority not met, and cells at 50% are unshaded. Asterisks mark cells with fewer than 20 posts, shown but not interpreted. Heatmap with six value groups as rows and nine user outcomes as columns. Task effectiveness is majority met in five of the six value groups, most strongly in Autonomous Operation. Supervision workload is majority not met in every group, and risk exposure is majority not met in every cell with at least 20 posts. Affordable Operation contains most resource-burden posts, and Bounded Reach contains the largest risk-exposure cell.
| Task eff. | Resource burden | Time eff. | Adoption behavior | Affective response | Supervision workload | Recovery behavior | Risk exposure | Trust calibration | ||
| Value met | 50.1 | 9.6 | 16.1 | 11.1 | 7.2 | 2.2 | 2.3 | 0.4 | 0.9 | 25,172 |
| Value not met | 30.8 | 23.2 | 6.5 | 11.1 | 12.2 | 6.9 | 2.3 | 4.7 | 2.4 | 19,595 |
| Difference (pp) |
Table 2. User outcomes among the 44,767 outcome-coded posts, split by whether the post’s value was coded met. Rows sum to 100% before rounding, and the final row gives the met-minus-not-met difference in percentage points.
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
| User outcome | Definition | Example post excerpt |
|---|---|---|
| Task effectiveness | Whether the agent completed the task and how correct or usable the result was. | “ I’ve been using my agent to build a temperature trading bot with mixed results. ” |
| Time efficiency | Whether the agent saved the user time or cost them time. | “ Im using an old surface pro 6 and it pretty slow with OpenClaw ” |
| Resource burden | What a run cost the user in money and metered resources. | “ OpenClaw made openAI and Anthropic $100K from my credit card. ” |
| Supervision workload | The effort of watching, checking, and managing what the agent did. | “ I prefer cowork but it’s too hard to keep a remote instance running smoothly for me. ” |
| Affective response | How the user felt about the experience. | “ It’s a total mess at my side as everyone is using everything. ” |
| Trust calibration | How far the user relied on the agent, and whether that reliance was warranted. | “ If an AI can read your emails, track your behavior, and make decisions for you… At what point do you stop being the one in control? ” |
Table 3. The nine user-outcome categories, defined as in Table 1 . Each names what a reported consequence concerned; value fulfillment carries its direction. Example excerpts come from the 400-post codebook-development sample.
| Value group | Value | Definition | Example post excerpt |
| Dependable Operation | Dependability† | Justified expectation that the agent delivers its intended service correctly and consistently under stated conditions. | “ OpenClaw feels dumber than Claude… even though it uses Claude? ” |
| Trust | Expectations that exist between people who can experience good will, extend good will toward others, feel vulnerable, and experience betrayal ( Friedman et al., 2006 ) (agent: the user’s reliance on the agent and its operators, and the vulnerability that reliance creates). | “ Trust grows once people see how consistently it performs. ” | |
| Repairability† | Ability to diagnose, reverse, correct, and recover from agent errors or unwanted changes. | “ I had this issue after an openclaw update got fixed with another update ” | |
| Autonomous Operation | Autonomy | People’s ability to decide, plan, and act in ways that they believe will help them to achieve their goals ( Friedman et al., 2006 ) (agent: the goals a user pursues by delegating a run rather than performing the work). | “ I want my Mac back! ” |
| Human welfare | People’s physical, material, and psychological well-being ( Friedman et al., 2006 ) (agent: the well-being of the user who delegates work to the agent). | “ switched from self-hosting openclaw to managed hosting and my weekends came back ” | |
| Calmness | A peaceful and composed psychological state ( Friedman et al., 2006 ) (agent: a state the agent’s interruptions during and between runs do not disturb). | “ OpenClaw always sends me a Telegram message after each run, even when the script finds nothing new. ” |
Table 4. The 21 human values and their six value groups; † marks the nine study-specific categories. Example excerpts come from the 400-post codebook-development sample.
| Value group | Sub-theme | Representative quotation |
|---|---|---|
| Autonomous Operation | Extending reach to new surfaces | “ The biggest unlock was letting the agent improve its own environment. ” |
| Delegating whole workflows rather than steps | “ So this morning I got OpenClaw to build an automated script that pays for parking at the times I used to get tickets. ” | |
| Hours returned rather than quality gained | “ My micro SaaS ops went from 8 hours a week to 45 minutes. Run Lobster (OpenClaw) runs the rest. ” | |
| Returned time questioned | “ Good point. When using n8n, the incremental value of openclaw and the time sink just wasn’t worth it. ” | |
| Dependable Operation | Silent or partial failure | “ OpenClaw sees empty content and silently falls through to the next model in your fallback chain — no error, just the wrong model answering. ” |
| Verification taken back by the user | “ ‘Verify before asserting’ exists because the agent told me a feature was working when it had never been tested. ” |
Table 5. Thematic analysis codebook. Each row pairs a sub-theme with one representative excerpt from the sampled posts. The two sub-themes on either side of a rule within a value group make up one theme, and the twelve themes are the bolded claims in Sections 5.1 – 5.6 .
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