LLM Content Moderation and User Satisfaction: Evidence from Response Refusals in Chatbot Arena

Stefan Pasch

REVIEW 3 major objections 5 minor 15 references

LLM Content Moderation and User Satisfaction: Evidence from Response Refusals in Chatbot Arena

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper establishes a refusal penalty in user preferences: ethical refusals win only 8% of Chatbot Arena comparisons, about half the rate of technical refusals, and the penalty is smaller when refusals are longer, more aligned with the…

desk verdict A genuinely new Arena-based result on refusal penalties, but the classifier needs stronger validation before the headline numbers can be trusted. read the letter →

arxiv 2501.03266 v2 pith:X6ER2B6C submitted 2025-01-04 cs.CL cs.AIcs.CYcs.HCcs.SI

Stefan Pasch This is my paper · ORCID

classification cs.CLcs.AIcs.CYcs.HCcs.SI

keywords LLMcontentmoderationresponserefusalsusersatisfactionChatbotArenaethicalalignmentpairwisepreferenceRoBERTaclassifierpromptsensitivity

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

Using nearly 50,000 single-turn pairwise comparisons from Chatbot Arena, the paper asks whether users punish LLMs for refusing to answer, and whether the reason matters. It builds a five-category classifier that separates ethical refusals from technical refusals and from disclaimers that still attempt the task. The central finding is a large refusal penalty: ethical refusals win only 8% of comparisons, technical refusals 16%, and normal responses 36%, with a 37-percentage-point regression coefficient for ethical refusals. The penalty shrinks when refusals are longer, more aligned with the prompt, or issued in response to content flagged as clearly unsafe. The paper interprets this as evidence that safety-aligned moderation can conflict with user satisfaction, and that context and phrasing can reduce that conflict.

What carries the argument

The load-bearing instrument is a fine-tuned RoBERTa-Large classifier trained on 3,500 hand-labeled responses, which sorts each model response into five categories: Normal, Refusal Ethical, Refusal Technical, Disclaimer Ethical, and Disclaimer Technical. The classifier is applied to all responses in the Chatbot Arena dataset, converting free text into per-response labels, and user win/loss/tie outcomes are then regressed on those labels with controls for the focal and opponent model. Two auxiliary measurements carry the moderation analyses: cosine similarity between prompt and response embeddings as a proxy for conversational alignment, and flags from the OpenAI Moderation API as a proxy for prompt sensitivity.

What would settle it

Have several independent human raters label a random sample of responses the classifier calls Refusal Ethical and Refusal Technical, then re-estimate the win-rate regressions using consensus labels; if a material share of cases changes category, the 8%-versus-16% win-rate gap and the 37-point coefficient would shrink or disappear.

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Extended reading notes

Core claim

The paper's central claim is that users systematically prefer normal responses to refusals, and that ethical refusals are judged substantially more harshly than technical ones. In the raw data, Refusal Ethical responses win 8% of comparisons, Refusal Technical 16%, and Normal responses 36%; in OLS regressions controlling for model and opponent, an ethical refusal lowers win probability by 37 percentage points while a technical refusal lowers it by 21. The penalty is not fixed: among ethical refusals, a one-standard-deviation increase in response length raises win rate by 9 percentage points, higher semantic similarity to the prompt raises it by 1.6, and refusals to prompts flagged as unsafe by a moderation API roughly double the win/loss ratio compared with unflagged prompts. The paper reads these results as support for the idea that refusal type, refusal phrasing, and prompt sensitivity all shape user satisfaction.

Load-bearing premise

The entire headline gap rests on the classifier's ability to tell ethical refusals from technical ones, and because the training labels came mostly from one author with only moderate agreement against a model-based second annotator, a systematic labeling error would directly distort the 37-point coefficient.

Editorial extensions

If this is right

Ethical refusals are the most disliked response type in the dataset, about twice as costly in win rate as technical refusals and far costlier than a normal answer.
Refusals can be made less costly without removing the refusal itself: longer, context-specific refusals and refusals to clearly unsafe prompts receive better user ratings.
Most ethical refusals in the data occur on prompts not flagged as unsafe, and those are the refusals users penalize most, pointing to miscalibrated moderation as a key driver of the penalty.
Disclaimers that state a limitation but still attempt the task are penalized much less than outright refusals, suggesting that partial engagement protects user satisfaction.

Reading between the lines

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

Inference: If the revealed-preference pattern holds, a provider optimizing user engagement should reserve hard ethical refusals for genuinely harmful or illegal requests, since the data suggest this could recover a large share of the 37-point penalty without weakening safety.
Inference: Because Chatbot Arena voters skew toward AI enthusiasts, the size of the penalty may differ in general-population or domain-specific settings; that is an extrapolation beyond the paper's evidence.
Inference: The positive effect of length and similarity on ethical refusals suggests refusal wording is a controllable design variable: refusal templates could be generated or fine-tuned to be longer and more contextually anchored while keeping the decision to refuse unchanged.
Inference: The disclaimers result raises a testable design hypothesis the paper does not pursue: converting hard ethical refusals into ethical disclaimers that flag concerns but still give a qualified answer may trade a small satisfaction cost for a much smaller one.

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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

Here's the short version: this paper is worth engaging—it's the first large-scale Arena-based evidence I know of that separates ethical from technical refusals and shows users penalize the former much more heavily. The reported numbers are big: ethical refusals win 8% of matchups vs 16% for technical, and the OLS penalty is 37 points. The authors also check plausible confounds, release the classifier and labeled data, and frame the hypotheses with relevant HCI theory. That's a solid package.

The soft spot is the measurement of the key variable. The classifier is trained on 3,500 hand-labeled responses, but the labels come from a single author, with GPT-4-turbo as the only second annotator and the author adjudicating disagreements. There's no independent ground truth, so we're relying on one person's interpretation of what counts as 'ethical' vs 'technical.' The test-set performance is reported only as weighted F1 and accuracy on a pre-filtered distribution that is 40% normal and roughly 17% ethical refusals. Per-class precision/recall aren't given. Since these classes are differentiated by motivation, not surface form, and since ethical refusals are rare in the full data (2.66%), I can't tell whether the 8% vs 16% gap is real or amplified by misclassification. A confusion matrix, per-class F1, and a hand-checked sample of unfiltered Arena responses would go a long way.

There's also an unexplained unit-of-analysis choice. Table 5 lists 49,938 observations—the number of pairwise comparisons—but each comparison has two responses. How was the focal response chosen? Using one row per pair without explaining the selection rule could bias the estimates, and the robust standard errors aren't clustered by prompt or pair, which matters if both responses are used. This needs to be clarified, not just because it's sloppy but because it changes what the regressions are actually estimating.

None of these are fatal. The direction of the finding is plausible, the effect is much larger than what annotation noise would typically produce, and the theoretical motivation is reasonable. The limitations section is honest about several issues, though it doesn't address the classifier validation gap.

I'd send this to peer review. The question matters, the data is appropriate, and a good referee can push the authors to tighten the validation and the econometrics. As it stands, I'd treat the headline as a well-motivated hypothesis rather than a measured fact.

Referee Report

3 major / 5 minor

Summary. The paper analyzes nearly 50,000 single-turn Chatbot Arena comparisons to measure how LLM response refusals affect user satisfaction. It introduces a fine-tuned RoBERTa classifier that labels responses as Normal, Refusal Ethical, Refusal Technical, or corresponding Disclaimer categories, based on a hand-labeled dataset of 3,500 responses. The main empirical claim is that ethical refusals receive sharply lower win rates than technical refusals and normal responses (8% vs. 16% vs. 36% unconditional; a 37.5 percentage point adjusted win penalty in Table 5). The paper also tests moderators: refusal phrasing (response length and semantic similarity to the prompt) and prompt sensitivity (OpenAI Moderation API flags). The authors report consistent evidence that refusals are penalized, ethical refusals more so than technical refusals, and that the penalty is softened by more aligned and longer refusals and by clearly sensitive prompts.

Significance. If the central result holds, the paper makes a valuable empirical contribution: it moves refusal research from scripted safety benchmarks to organic, large-scale user preference data, and it is one of the few studies to separately estimate user reactions to ethical versus technical refusals. The public release of the refusal classifier and annotation dataset is a concrete asset. The paper also engages seriously with theory from human-computer interaction, politeness, and expectation violation research. However, the load-bearing empirical link is the classifier's ability to distinguish ethical from technical refusals at scale, and that link is not yet established with adequate validation. The headline 8% vs. 16% gap and the adjusted 37 pp vs. 21 pp penalty difference both inherit any bias in the refusal classifier, so the significance of the contribution depends on the classifier concerns being resolved.

major comments (3)

[§3.2–3.3, Table 3] The classifier evaluation is insufficient to support the central claim. The test set is drawn from the same phrase-pre-filtered distribution used for training, and Table 3 reports only weighted F1 and accuracy (0.88). Per-class precision and recall are not reported. Because the full-data prevalence is 2.66% for Refusal Ethical and 3.21% for Refusal Technical (Table 4), while the training/test set has 40.6% Normal and 17.0% Refusal Ethical, a weighted F1 of 0.88 is compatible with materially lower performance on the refusal classes. More importantly, the ethical/technical distinction is based on motivation rather than surface form, so differential misclassification between these two categories could bias the 8% vs. 16% win-rate gap and the Table 5 coefficients. Please provide the full confusion matrix, per-class precision/recall/F1, and validate the classifier on an unfiltered random sample of Chatbot Arena responses with manual labels, reporting the same per-class metrics on that sample.
[§3.2] The ground-truth annotation rests on a single author. The paper reports that GPT-4-turbo served as a second annotator and that the author adjudicated the 50 examined disagreements, but this does not provide independent human ground truth. The reported Krippendorff's alpha (0.668) and Cohen's kappa (0.67) measure agreement with an LLM, not with an independent human rater. Given that the key contrast is ethical versus technical motivation, the current reliability evidence does not rule out systematic labeling bias. At minimum, the paper should present dual independent human annotations on a random subset—especially cases near the Ethical/Technical boundary—along with disagreement statistics and a transparent adjudication procedure.
[§4.1, Table 5 vs. Appendix Table AT1] There is a numerical inconsistency in the reported Tie coefficients for Refusal Ethical. Table 5 reports 0.005*** with standard error 0.014, which cannot be statistically significant at the conventional level, while Appendix Table AT1 reports 0.055*** with standard error 0.014 for what should be the same focal-model coefficient. The significance stars, coefficients, and standard errors need to be reconciled and corrected across the main text and appendix.

minor comments (5)

[§1] There is a typo in the introduction: "Antropic" should be "Anthropic."
[§3.3, Table 3] The table caption and text are ambiguous about the F1 metric: the text says "weighted F1" in the training description but Table 3 labels it only as "F1." Please state clearly that it is a weighted average and also report per-class scores.
[§3.2] The sentence "In 49 out of 50 cases, the human annotation was confirmed as correct" should specify that the confirmation was made by the same author who created the labels, so that readers can judge the independence of the validation.
[§4.1, Table 5 note] The note "Controls Opp. Model represent controls for all listed variables for the opponent model" is imprecise: it should say "for the opponent response" and clarify which variables are included (response category, similarity, length, and model fixed effects).
[§3.2 and §5.1] The manuscript contains two "citation omitted for anonymity" placeholders. These should be resolved before publication; if anonymity is required during review, the editor should ensure the final version replaces them.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the refusal penalty estimates are measured from independent user votes and are not constructed from the classifier's fitted labels.

full rationale

The paper's central claims—that ethical refusals have lower win rates than technical refusals (Section 4.1) and that refusing for ethical reasons corresponds to a 37 percentage point decrease in win rates (Table 5)—are empirical associations between a hand-labeled refusal taxonomy and external Chatbot Arena user votes. The RoBERTa classifier is trained on 3,500 hand labels; it is not fitted to win/loss outcomes, and no refusal category is defined in terms of user satisfaction. The regressions control for model, opponent model, and response features, and the outcome variable is the independently recorded user choice. The self-citations (Pasch & Cutura, 2024; Pasch & Ehnes, 2022; Koch & Pasch, 2023) appear only as methodological examples or supporting annotation evidence, and they do not carry the load-bearing argument. The single-annotator labeling and the absence of per-class classifier metrics are validity and measurement concerns, but they do not make the derivation circular: the headline result is not equivalent to its input by construction. No equation in the paper defines the predicted win-rate gap in terms of the fitted labels, and no cited result is used to forbid alternative explanations. Therefore no circular step can be exhibited, and the appropriate score is 0.

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

The central claim rests on the validity of the refusal classifier, the representativeness of Chatbot Arena votes as a proxy for user satisfaction, and the moderation API as a measure of prompt sensitivity. No hand-tuned free parameters are introduced in the statistical analysis; the classifier's weights are fitted to the labeled data, which is a standard modeling step rather than a ledger parameter.

assumptions (5)

domain assumption Chatbot Arena pairwise votes are a valid revealed-preference proxy for user satisfaction.
The paper treats win/loss/tie outcomes as the indicator of user satisfaction throughout Section 4.
domain assumption The single-turn subset of Chatbot Arena provides a clean, isolated view of user preference.
Multi-turn conversations are excluded in Section 3.1 to avoid jailbreak and context effects.
domain assumption The OpenAI Moderation API correctly captures prompt sensitivity.
Used in Section 3.5 to flag harmful prompts; small sample sizes for some categories are acknowledged.
domain assumption The five-category refusal taxonomy is meaningful and mutually exclusive.
The taxonomy in Table 1 is the basis for all labels; the 'ethical mentions take precedence' rule in Section 3.2 can collapse mixed cases into ethical.
domain assumption The fine-tuned RoBERTa classifier generalizes from 3,500 hand-labeled responses to the full Chatbot Arena corpus.
The classifier is applied to all responses after training on a prefiltered subset; performance on unfiltered data is not reported.

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Cite this review

Pith. "Pith review of LLM Content Moderation and User Satisfaction: Evidence from Response Refusals in Chatbot Arena." pith.science (2026). https://pith.science/paper/X6ER2B6C

@misc{pith2026250103266,
  author       = {Pith},
  title        = {Pith review of: LLM Content Moderation and User Satisfaction: Evidence from Response Refusals in Chatbot Arena},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X6ER2B6C}},
  note         = {Machine review of arXiv:2501.03266}
}

read the original abstract

LLM safety and ethical alignment are widely discussed, but the impact of content moderation on user satisfaction remains underexplored. In particular, little is known about how users respond when models refuse to answer a prompt-one of the primary mechanisms used to enforce ethical boundaries in LLMs. We address this gap by analyzing nearly 50,000 model comparisons from Chatbot Arena, a platform where users indicate their preferred LLM response in pairwise matchups, providing a large-scale setting for studying real-world user preferences. Using a novel RoBERTa-based refusal classifier fine-tuned on a hand-labeled dataset, we distinguish between refusals due to ethical concerns and technical limitations. Our results reveal a substantial refusal penalty: ethical refusals yield significantly lower win rates than both technical refusals and standard responses, indicating that users are especially dissatisfied when models decline a task for ethical reasons. However, this penalty is not uniform. Refusals receive more favorable evaluations when the underlying prompt is highly sensitive (e.g., involving illegal content), and when the refusal is phrased in a detailed and contextually aligned manner. These findings underscore a core tension in LLM design: safety-aligned behaviors may conflict with user expectations, calling for more adaptive moderation strategies that account for context and presentation.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

15 extracted references · 13 canonical work pages

[1]

refusal penalty

Introduction With the rise of large language models (LLMs) such as ChatGPT, the topics of LLM alignment and safety have garnered significant attention across academia (Xie et al., 2024), industry (OpenAI, 2023, Antropic, 2023), and regulatory bodies (European Data Protection Board, 2024). LLMs, while demonstrating remarkable capabilities, face challenges ...

work page 2024
[2]

I cannot provide information on that topic

Theoretical Background 2.1. LLM Response Refusals and User Satisfaction As large language models (LLMs) become widely integrated into everyday applications, users increasingly expect them to be capable, responsive, and goal-oriented. These expectations are not only shaped by the technical capacities of the models but also by the growing cultural perceptio...

work page 1991
[5]

The dataset consists of 57,477 conversation pairs, with each pair comprising responses from two distinct models to a shared user prompt

Overview of Main Methodological Pipeline To investigate the impact of content moderation by LLMs on user satisfaction, we leverage data from Chatbot Arena, a widely used benchmarking platform for conversational AI models (Chiang et al., 2024). The dataset consists of 57,477 conversation pairs, with each pair comprising responses from two distinct models t...

work page 2024
[6]

Grounded in a novel refusal typology and a large-scale, annotated dataset, we tested four hypotheses spanning user perceptions, response phrasing, and contextual moderation effects

Conclusion This study investigated how different types and phrasings of refusal behavior in large language models affect user satisfaction. Grounded in a novel refusal typology and a large-scale, annotated dataset, we tested four hypotheses spanning user perceptions, response phrasing, and contextual moderation effects. We found consistent support for H1:...

work page 2018
[10]

Model Yes Yes Yes Observations 49938 49938 49938 R-squared 0.13 0.13 0.01 Robust standard errors in parenthesis.* p<.1, ** p<.05, ***p<.01

Conditional Win/Loss/Tie Rates by User Decision Table 5: OLS Regressions – LLM-Refusals and Win-Rates User Decision Variable Win Loss Tie Disclaimer Ethical -0.073*** (0.019) 0.113*** (0.018) -0.040*** (0.019) Disclaimer Technical -0.098*** (0.009) 0.106*** (0.009) -0.008 (0.009) Refusal Ethical -0.375*** (0.011) 0.320*** (0.013) 0.005*** (0.014) Refusal ...

work page 1943
[11]

That said, interpretations of specific content categories should be made with caution

has begun to explore calibration techniques and evaluation strategies for refusals, but this remains an open area of research. That said, interpretations of specific content categories should be made with caution. For example, we find that prompts flagged as sexual or violent content are associated with particularly low user win rates for refusal response...

work page 2024
[14]

L., Zheng, L., Sheng, Y., Angelopoulos, A

Chiang, W. L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., ... & Stoica, I. (2024, July). Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. In International Conference on Machine Learning (pp. 8359-8388). PMLR. Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. T...

arXiv 1991
[15]

Like Having a Really Bad PA

Eslami, M., Vaccaro, K., Lee, M. K., Elazari Bar On, A., Gilbert, E., & Karahalios, K. (2019, May). User attitudes towards algorithmic opacity and transparency in online reviewing platforms. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14). European Data Protection Board (EDPB) (2024). Opinion on AI Models: GDPR P...

arXiv 2024

Show all 15 references

[1482]

Brown, P., & Levinson, S. C. (1987). Politeness: Some universals in language usage. Cambridge University Press. Burgoon, J. K. (1993). Interpersonal expectations, expectancy violations, and emotional communication. Journal of language and social psychology, 12(1-2), 30-48. Cha...

1987
[1977]

Is there anything else I can do?

and are in line with prior studies reporting human-human reliability for multi-class, subjective annotation tasks (Wiebe et al., 2005; Mohammad & Turney, 2013). While perfect alignment is not expected, these results confirm that the annotation process was consistent and approp...

2005
[1986]

posits that richer messages are especially valued in ambiguous or sensitive communication contexts—conditions that often accompany refusal scenarios. Recent work in LLM evaluation has also shown that both human and model-based raters demonstrate a length bias, consistently fav...

2021
[1987]

Taken together, these perspectives suggest that users may respond more favorably to refusals that exhibit topical alignment and conversational relevance

suggests that tailoring refusals to user input may help reduce perceived face-threat, thereby preserving a sense of interpersonal respect. Taken together, these perspectives suggest that users may respond more favorably to refusals that exhibit topical alignment and conversati...

2004
[2016]

From this filtered set, we hand-labeled 1,750 model response pairs, corresponding to a total of 3,500 individual model responses

and sentiment analysis (Duong & Nguyen-Thi, 2021), where filtering significantly reduces labeling costs while maintaining label quality. From this filtered set, we hand-labeled 1,750 model response pairs, corresponding to a total of 3,500 individual model responses. Table 2 pr...

2021
[2019]

I’m sorry, I can’t help with that

or RoBERTa (Liu et al., 2019). These models are pre-trained on large corpora of general text but fine-tuning them on a specific task allows them to better capture the nuances and context of the target domain. Fine-tuning requires training the model on task-specific labeled dat...

2019
[2024]

As an AI, I do not have the capability to respond

offer valuable tools for evaluating how LLMs handle ethically sensitive prompts, they focus exclusively on scripted, high-risk questions designed to trigger refusal. This makes them effective for stress-testing alignment, but less suited for analyzing how refusals emerge in or...

2021

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

refusal penalty

Introduction With the rise of large language models (LLMs) such as ChatGPT, the topics of LLM alignment and safety have garnered significant attention across academia (Xie et al., 2024), industry (OpenAI, 2023, Antropic, 2023), and regulatory bodies (European Data Protection Board, 2024). LLMs, while demonstrating remarkable capabilities, face challenges ...

work page 2024

[2] [2]

I cannot provide information on that topic

Theoretical Background 2.1. LLM Response Refusals and User Satisfaction As large language models (LLMs) become widely integrated into everyday applications, users increasingly expect them to be capable, responsive, and goal-oriented. These expectations are not only shaped by the technical capacities of the models but also by the growing cultural perceptio...

work page 1991

[3] [5]

The dataset consists of 57,477 conversation pairs, with each pair comprising responses from two distinct models to a shared user prompt

Overview of Main Methodological Pipeline To investigate the impact of content moderation by LLMs on user satisfaction, we leverage data from Chatbot Arena, a widely used benchmarking platform for conversational AI models (Chiang et al., 2024). The dataset consists of 57,477 conversation pairs, with each pair comprising responses from two distinct models t...

work page 2024

[4] [6]

Grounded in a novel refusal typology and a large-scale, annotated dataset, we tested four hypotheses spanning user perceptions, response phrasing, and contextual moderation effects

Conclusion This study investigated how different types and phrasings of refusal behavior in large language models affect user satisfaction. Grounded in a novel refusal typology and a large-scale, annotated dataset, we tested four hypotheses spanning user perceptions, response phrasing, and contextual moderation effects. We found consistent support for H1:...

work page 2018

[5] [10]

Model Yes Yes Yes Observations 49938 49938 49938 R-squared 0.13 0.13 0.01 Robust standard errors in parenthesis.* p<.1, ** p<.05, ***p<.01

Conditional Win/Loss/Tie Rates by User Decision Table 5: OLS Regressions – LLM-Refusals and Win-Rates User Decision Variable Win Loss Tie Disclaimer Ethical -0.073*** (0.019) 0.113*** (0.018) -0.040*** (0.019) Disclaimer Technical -0.098*** (0.009) 0.106*** (0.009) -0.008 (0.009) Refusal Ethical -0.375*** (0.011) 0.320*** (0.013) 0.005*** (0.014) Refusal ...

work page 1943

[6] [11]

That said, interpretations of specific content categories should be made with caution

has begun to explore calibration techniques and evaluation strategies for refusals, but this remains an open area of research. That said, interpretations of specific content categories should be made with caution. For example, we find that prompts flagged as sexual or violent content are associated with particularly low user win rates for refusal response...

work page 2024

[7] [14]

L., Zheng, L., Sheng, Y., Angelopoulos, A

Chiang, W. L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., ... & Stoica, I. (2024, July). Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. In International Conference on Machine Learning (pp. 8359-8388). PMLR. Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. T...

arXiv 1991

[8] [15]

Like Having a Really Bad PA

Eslami, M., Vaccaro, K., Lee, M. K., Elazari Bar On, A., Gilbert, E., & Karahalios, K. (2019, May). User attitudes towards algorithmic opacity and transparency in online reviewing platforms. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14). European Data Protection Board (EDPB) (2024). Opinion on AI Models: GDPR P...

arXiv 2024