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arxiv: 2606.27274 · v1 · pith:ZLOJKVBUnew · submitted 2026-06-25 · 💻 cs.LG

BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media

MSVPJ Sathvik , Parmitha Vangapadu , Nishit Rane , Sathwik Narkedimilli , Mark Lee , Akrati Saxena This is my paper

Pith reviewed 2026-06-26 05:16 UTC · model grok-4.3

classification 💻 cs.LG
keywords betting advertisementsmanipulative advertisingsocial mediaannotated datasetexplainable detectionInstagramRedditpersuasive techniques
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The pith

A new dataset of betting advertisements from Instagram and Reddit includes human explanations for each manipulative label.

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

The paper creates BetXplain, a collection of betting ads scraped from Instagram and Reddit that carries both binary labels for manipulative content and written human reasons for each label. The authors argue this resource fills a gap that has blocked automated detection of deceptive promotions known to encourage risky gambling and affect mental health. With the explanations attached, the dataset is positioned to support research on explainable models rather than black-box classifiers alone. The work also catalogs common persuasive tactics in the ads and links them to potential user harms, while outlining downstream uses such as warning plugins and regulatory crawlers.

Core claim

The central claim is that releasing an annotated dataset of real betting advertisements together with human-written explanations for the manipulative labels will allow researchers to build and evaluate explainable detection systems for deceptive social-media content that current work cannot address because no such public resource exists.

What carries the argument

The explanation-annotated dataset, in which every advertisement receives both a classification label and a human-provided textual justification for that label.

If this is right

  • Models trained on the dataset can output both a manipulativeness score and a human-readable reason.
  • Browser extensions or platform filters can surface warnings tied to the specific tactics detected.
  • Regulatory tools can scan social media at scale for the same patterns the annotations capture.
  • Analysis of the annotated tactics can quantify how often certain persuasive devices appear and how they correlate with described mental-health effects.

Where Pith is reading between the lines

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

  • The explanations may serve as seed data for training language models to generate their own justifications for new ads.
  • The dataset could be extended to other harmful ad categories such as health supplements or financial schemes by reusing the same annotation protocol.
  • If the explanations prove consistent across platforms, they could inform platform policy on what counts as deceptive gambling promotion.

Load-bearing premise

The authors' manual annotations accurately and consistently identify manipulative practices without reported measures of agreement or external validation.

What would settle it

An independent re-annotation of a random subset of the ads by new raters that produces low agreement with the original labels and explanations.

Figures

Figures reproduced from arXiv: 2606.27274 by Akrati Saxena, Mark Lee, MSVPJ Sathvik, Nishit Rane, Parmitha Vangapadu, Sathwik Narkedimilli.

Figure 1
Figure 1. Figure 1: Overview of the BetXplain framework, illustrating the pipeline from betting advertisement data collection [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Psychological manipulation, architecture, and mental health impact(a) Keyword frequency heatmap across [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Influence cues and deceptive claims across categories: (a) Influence cues intensity by category. (b) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Clinical risk profile: (a) Risk severity distributions by disorder. (b) Psychological impact heatmap across [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sentiment, emotional tone, and the deceptive positivity effect: (a) Sentiment polarity by segment. (b) [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
read the original abstract

The promotion of betting applications on social media platforms has increased significantly in recent years. Many of these advertisements use persuasive techniques that may mislead users, encourage risky behavior, and potentially influence users' mental well-being. However, research on the automated detection of manipulative and deceptive betting advertisements remains limited due to the lack of publicly available annotated datasets. In this work, we introduce a new dataset of betting-related advertisements collected from two widely used social media platforms, Instagram and Reddit. The advertisements were manually annotated for manipulative and deceptive advertising practices. In addition to classification labels, the dataset includes human-provided explanations that describe the reasoning behind each annotation, enabling research into explainable approaches to detecting manipulative advertising. Furthermore, we analyze the strategies commonly used in betting advertisements and examine how these persuasive tactics may impact users' mental health. The proposed framework can also enable practical applications such as browser plugins that warn users about manipulative betting advertisements and automated web crawlers that help regulatory authorities monitor and detect such promotions online.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces BetXplain, a new dataset of betting-related advertisements collected from Instagram and Reddit. The advertisements are manually annotated for manipulative and deceptive advertising practices, with the addition of human-provided explanations for each annotation to support explainable detection research. The work further analyzes common persuasive strategies in these ads and their potential impact on users' mental health, while suggesting practical applications such as browser plugins and regulatory monitoring tools.

Significance. If the annotations prove reliable and the dataset is released with full documentation, this resource would address a documented gap in publicly available data for studying manipulative betting advertisements on social media. It could enable new work on explainable AI methods for content detection and support downstream applications in user protection and regulatory oversight.

major comments (2)
  1. [Abstract] Abstract: No information is provided on dataset scale (number of ads collected), annotator count, annotation guidelines, inter-annotator agreement metrics, or any external validation of the labels. These details are load-bearing for the central claim that the dataset (with explanations) can support explainable detection research; without them the reliability of the manual annotations cannot be assessed.
  2. [Abstract] Abstract / Introduction: The claim that the annotations capture 'manipulative and deceptive advertising practices' rests on an unverified assumption of annotator correctness. The absence of reported agreement statistics or validation against external criteria (e.g., expert review or established advertising standards) directly affects whether the dataset meets the requirements for downstream explainable-AI experiments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback on the abstract and the need to substantiate the reliability of the annotations. We will revise the manuscript to incorporate the requested details and strengthen the description of the annotation process.

read point-by-point responses

  1. Referee: [Abstract] Abstract: No information is provided on dataset scale (number of ads collected), annotator count, annotation guidelines, inter-annotator agreement metrics, or any external validation of the labels. These details are load-bearing for the central claim that the dataset (with explanations) can support explainable detection research; without them the reliability of the manual annotations cannot be assessed.

    Authors: We agree that these details are essential and should be summarized in the abstract. In the revised version we will add the total number of advertisements collected, the number of annotators involved, a concise overview of the annotation guidelines, the computed inter-annotator agreement, and any steps taken toward external validation. This will allow readers to directly evaluate the dataset's suitability for explainable detection research. revision: yes

  2. Referee: [Abstract] Abstract / Introduction: The claim that the annotations capture 'manipulative and deceptive advertising practices' rests on an unverified assumption of annotator correctness. The absence of reported agreement statistics or validation against external criteria (e.g., expert review or established advertising standards) directly affects whether the dataset meets the requirements for downstream explainable-AI experiments.

    Authors: We recognize that explicit evidence of annotation quality is required to support downstream use. We will expand both the abstract and introduction to report inter-annotator agreement statistics and to describe how the guidelines were derived with reference to established advertising standards. While an independent external expert review was not performed, the human-provided explanations included in the dataset enable transparent scrutiny and further validation by users. These additions will directly address the concern about annotator correctness. revision: partial

Circularity Check

0 steps flagged

No circularity: pure dataset paper with no derivations or fitted quantities

full rationale

The paper introduces a manually annotated dataset of betting ads from Instagram and Reddit, including classification labels and human explanations. No equations, models, predictions, parameters, or first-principles derivations are present. The contribution is data collection and annotation; there are no self-definitional steps, fitted inputs renamed as predictions, or self-citation chains that reduce any claim to its own inputs by construction. Annotation reliability concerns (e.g., lack of IAA) fall under correctness risk, not circularity per the rules.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The contribution rests entirely on the validity and utility of the newly created annotations; no free parameters, mathematical axioms, or invented entities are involved.

axioms (1)
  • domain assumption Human annotators can reliably identify manipulative and deceptive practices in betting advertisements and provide accurate explanations for those judgments.
    The dataset's value for explainable detection depends on this unvalidated premise; the abstract provides no agreement statistics or external validation.

pith-pipeline@v0.9.1-grok · 5728 in / 1279 out tokens · 25019 ms · 2026-06-26T05:16:38.285393+00:00 · methodology

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Reference graph

Works this paper leans on

294 extracted references · 16 canonical work pages

  1. [1]

    Bert: Pre-training of deep bidirectional transformers for language understanding , author=. Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) , pages=

    2019

  2. [2]

    arXiv preprint arXiv:1907.11692 , year=

    Roberta: A robustly optimized bert pretraining approach , author=. arXiv preprint arXiv:1907.11692 , year=

    Pith/arXiv arXiv 1907

  3. [3]

    arXiv preprint arXiv:1910.01108 , year=

    DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter , author=. arXiv preprint arXiv:1910.01108 , year=

    Pith/arXiv arXiv 1910

  4. [4]

    , author=

    DeBERTa-v3 with R-Drop regularization for Multi-Author Writing Style Analysis. , author=. CLEF (Working Notes) , pages=

  5. [6]

    Proceedings of the 58th annual meeting of the association for computational linguistics , pages=

    BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension , author=. Proceedings of the 58th annual meeting of the association for computational linguistics , pages=

  6. [7]

    Journal of machine learning research , volume=

    Exploring the limits of transfer learning with a unified text-to-text transformer , author=. Journal of machine learning research , volume=

  7. [8]

    arXiv preprint arXiv:2412.08905 , year=

    Phi-4 technical report , author=. arXiv preprint arXiv:2412.08905 , year=

    Pith/arXiv arXiv

  8. [9]

    , author=

    Lora: Low-rank adaptation of large language models. , author=. Iclr , volume=

  9. [10]

    arXiv preprint arXiv:1711.05101 , year=

    Decoupled weight decay regularization , author=. arXiv preprint arXiv:1711.05101 , year=

    Pith/arXiv arXiv

  10. [12]

    arXiv preprint arXiv:1909.11942 , year=

    Albert: A lite bert for self-supervised learning of language representations , author=. arXiv preprint arXiv:1909.11942 , year=

    Pith/arXiv arXiv 1909

  11. [13]

    Current addiction reports , volume=

    Online gambling addiction: the relationship between internet gambling and disordered gambling , author=. Current addiction reports , volume=. 2015 , publisher=

    2015

  12. [14]

    GAMBLING AND PROBLEM GAMBLING IN NEW ZEALAND , author=

  13. [15]

    2014 , publisher=

    Sports betting and advertising , author=. 2014 , publisher=

    2014

  14. [16]

    International Review for the Sociology of Sport , volume=

    Understanding the convergence of markets in online sports betting , author=. International Review for the Sociology of Sport , volume=. 2018 , publisher=

    2018

  15. [17]

    Public health , volume=

    The changing epidemiology of gambling disorder and gambling-related harm: public health implications , author=. Public health , volume=. 2020 , publisher=

    2020

  16. [18]

    Addiction Research & Theory , volume=

    Dark nudges in gambling , author=. Addiction Research & Theory , volume=. 2019 , publisher=

    2019

  17. [19]

    Media Influence: Breakthroughs in Research and Practice , pages=

    Online gambling advertising and the third-person effect: A pilot study , author=. Media Influence: Breakthroughs in Research and Practice , pages=. 2018 , publisher=

    2018

  18. [20]

    2014 , publisher=

    Gambling advertising: A critical research review , author=. 2014 , publisher=

    2014

  19. [21]

    2005 , publisher=

    Pathological gambling: Etiology, comorbidity, and treatment , author=. 2005 , publisher=

    2005

  20. [22]

    Seventh National Conference of the National Association for Gambling Studies, Glenelg, South Australia , year=

    The prevalence of problem gambling in Australia: Another lottery , author=. Seventh National Conference of the National Association for Gambling Studies, Glenelg, South Australia , year=

  21. [23]

    Proceedings of the international AAAI conference on web and social media , volume=

    Automated hate speech detection and the problem of offensive language , author=. Proceedings of the international AAAI conference on web and social media , volume=

  22. [24]

    Acm Computing Surveys (Csur) , volume=

    A survey on automatic detection of hate speech in text , author=. Acm Computing Surveys (Csur) , volume=. 2018 , publisher=

    2018

  23. [25]

    Proceedings of the fifth international workshop on natural language processing for social media , pages=

    A survey on hate speech detection using natural language processing , author=. Proceedings of the fifth international workshop on natural language processing for social media , pages=

  24. [26]

    Learning from the worst: Dynamically generated datasets to improve online hate detection , author=. Proceedings of the 59th annual meeting of the Association for Computational Linguistics and the 11th international joint conference on natural language processing (volume 1: long papers) , pages=

  25. [27]

    Advances in neural information processing systems , volume=

    Language models are few-shot learners , author=. Advances in neural information processing systems , volume=

  26. [28]

    1867 , note =

    The Public Gambling Act, 1867 , author =. 1867 , note =

  27. [29]

    2000 , note =

    Information Technology Act, 2000 , author =. 2000 , note =

    2000

  28. [30]

    3 of 1987) , author =

    UAE Federal Penal Code (Federal Law No. 3 of 1987) , author =. 1987 , note =

    1987

  29. [31]

    2019 , note =

    Prohibition of Gambling under Saudi Arabian Legal Framework , author =. 2019 , note =

    2019

  30. [32]

    11 of 2004) , author =

    Qatar Penal Code (Law No. 11 of 2004) , author =. 2004 , note =

    2004

  31. [33]

    16 of 1960) , author =

    Kuwait Penal Code (Law No. 16 of 1960) , author =. 1960 , note =

    1960

  32. [34]

    2025 , url=

    5 Betting App Scams to Be Aware of in 2025 , author=. 2025 , url=

    2025

  33. [35]

    2026 , url=

    Online Casino Spam: How Fake Gambling Sites Steal Money , author=. 2026 , url=

    2026

  34. [36]

    2025 , month =

    SC says growth of online betting apps issue of public importance, seeks states' responses , journal =. 2025 , month =

    2025

  35. [37]

    2025 , month =

    Youth at Risk: Supreme Court issues notice to Centre on PIL seeking ban on betting apps , journal =. 2025 , month =

    2025

  36. [38]

    Australian & New Zealand Journal of Psychiatry , volume=

    Prevalence of psychiatric co-morbidity in treatment-seeking problem gamblers: A systematic review and meta-analysis , author=. Australian & New Zealand Journal of Psychiatry , volume=. 2015 , publisher=

    2015

  37. [39]

    Gaming Law Review , volume=

    Online gambling and geolocation technology: implications for regulation and potential threats to player protection , author=. Gaming Law Review , volume=. 2019 , publisher=

    2019

  38. [40]

    Addiction , volume=

    A pathways model of problem and pathological gambling , author=. Addiction , volume=. 2002 , publisher=

    2002

  39. [41]

    Proceedings of the ACM on human-computer interaction , volume=

    Dark patterns at scale: Findings from a crawl of 11K shopping websites , author=. Proceedings of the ACM on human-computer interaction , volume=. 2019 , publisher=

    2019

  40. [42]

    Proceedings of the 2018 CHI conference on human factors in computing systems , pages=

    The dark (patterns) side of UX design , author=. Proceedings of the 2018 CHI conference on human factors in computing systems , pages=

    2018

  41. [43]

    Proceedings of the ACM on Human-Computer Interaction , volume=

    Endorsements on social media: An empirical study of affiliate marketing disclosures on YouTube and Pinterest , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2018 , publisher=

    2018

  42. [44]

    International conference on learning representations , volume=

    Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning , author=. International conference on learning representations , volume=

  43. [45]

    Aho and Jeffrey D

    Alfred V. Aho and Jeffrey D. Ullman , title =. 1972

    1972

  44. [46]

    Publications Manual , year = "1983", publisher =

    1983

  45. [47]

    ACM Transactions on Computer-Human Interaction (TOCHI) , year=

    Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems , author=. ACM Transactions on Computer-Human Interaction (TOCHI) , year=

  46. [48]

    Psychological Medicine , year=

    Machine learning in mental health: a scoping review of methods and applications , author=. Psychological Medicine , year=

  47. [49]

    Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages =

    The Risk of Racial Bias in Hate Speech Detection , author =. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages =

  48. [50]

    Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , pages =

    Demographic Dialectal Variation in Social Media: A Case Study of African-American English , author =. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , pages =

    2016

  49. [51]

    Proceedings of the 11th International AAAI Conference on Web and Social Media (ICWSM) , year =

    Automated Hate Speech Detection and the Problem of Offensive Language , author =. Proceedings of the 11th International AAAI Conference on Web and Social Media (ICWSM) , year =

  50. [52]

    Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media (SocialNLP) , year =

    Demoting Racial Bias in Hate Speech Detection , author =. Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media (SocialNLP) , year =

  51. [53]

    Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media (SocialNLP) , pages =

    Detecting Offensive Tweets in Hindi-English Code-Switched Language , author =. Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media (SocialNLP) , pages =

  52. [54]

    Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media , pages =

    A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection , author =. Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media , pages =

  53. [55]

    arXiv preprint arXiv:2004.06465 , year =

    Deep Learning Models for Multilingual Hate Speech Detection , author =. arXiv preprint arXiv:2004.06465 , year =

    arXiv 2004

  54. [56]

    Advances in Neural Information Processing Systems , year =

    Counterfactual Fairness , author =. Advances in Neural Information Processing Systems , year =

  55. [57]

    Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =

    Counterfactual Fairness in Text Classification through Robustness , author =. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages =

    2019

  56. [58]

    Perturbation Sensitivity Analysis to Detect Unintended Model Biases , author =. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , pages =

    2019

  57. [59]

    Causality , author =

  58. [60]

    Advances in Neural Information Processing Systems , year =

    Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings , author =. Advances in Neural Information Processing Systems , year =

  59. [61]

    Science , volume =

    Semantics Derived Automatically from Language Corpora Contain Human-like Biases , author =. Science , volume =

  60. [62]

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =

    Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection , author =. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =

  61. [63]

    Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages =

    Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them , author =. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages =

    2019

  62. [64]

    Computational Linguistics , volume =

    Bias and Fairness in Large Language Models: A Survey , author =. Computational Linguistics , volume =

  63. [65]

    Language (Technology) is Power: A Critical Survey of ``Bias'' in

    Blodgett, Su Lin and Barocas, Solon and Daum. Language (Technology) is Power: A Critical Survey of ``Bias'' in. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =

  64. [66]

    ACM Computing Surveys , volume =

    A Survey on Bias and Fairness in Machine Learning , author =. ACM Computing Surveys , volume =

  65. [67]

    Communications of the ACM , volume =

    A Snapshot of the Frontiers of Fairness in Machine Learning , author =. Communications of the ACM , volume =

  66. [68]

    Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society , pages =

    Measuring and Mitigating Unintended Bias in Text Classification , author =. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society , pages =

    2018

  67. [69]

    Proceedings of the 2019 World Wide Web Conference , pages =

    Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification , author =. Proceedings of the 2019 World Wide Web Conference , pages =

    2019

  68. [70]

    Bhan, Aditya and Vittaut, Jean-Noel and Chesneau, Nicolas and Lesot, Marie-Jeanne , booktitle =

  69. [71]

    Kennedy, Brendan and Atari, Mohammad and Davani, Aida Mostafazadeh and Yeh, Leigh and Omrani, Ali and Kim, Yehsong and Coombs, Kris and Hagan, Chris and Razi, Krystal and Gonzalez, Alyssa and Hoover, Joe and Hyun, Joe and Portillo-Wightman, Gwen and Lee, Youngseo and Leong, Crystal and Mendelsberg, Joel and Meng, Xinchen and Mirrezaei, Nasrin and Noh, Jun...

  70. [72]

    Proceedings of the 14th Workshop on Semantic Evaluation , year =

    Zampieri, Marcos and Nakov, Preslav and Rosenthal, Sara and Atanasova, Pepa and Karadzhov, Georgi and Mubarak, Hamdy and Derczynski, Leon and Pitenis, Zeses and. Proceedings of the 14th Workshop on Semantic Evaluation , year =

  71. [73]

    Proceedings of the National Academy of Sciences , volume =

    Racial Disparities in Automated Speech Recognition , author =. Proceedings of the National Academy of Sciences , volume =

  72. [74]

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =

    Towards Debiasing Sentence Representations , author =. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =

  73. [75]

    Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in

    Schick, Timo and Udupa, Sahana and Sch\"utze, Hinrich , journal =. Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in

  74. [76]

    Quantifying Social Biases in

    Czarnowska, Paula and Vyas, Yogarshi and Shah, Kashif , journal =. Quantifying Social Biases in

  75. [77]

    Proceedings of the 3rd Innovations in Theoretical Computer Science Conference , pages =

    Fairness Through Awareness , author =. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference , pages =

  76. [78]

    Advances in Neural Information Processing Systems , year =

    Equality of Opportunity in Supervised Learning , author =. Advances in Neural Information Processing Systems , year =

  77. [79]

    ACM Computing Surveys , volume =

    A Survey on Automatic Detection of Hate Speech in Text , author =. ACM Computing Surveys , volume =

  78. [80]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume =

    On Measuring and Mitigating Biased Inferences of Word Embeddings , author =. Proceedings of the AAAI Conference on Artificial Intelligence , volume =

  79. [81]

    Linear Guardedness and its Implications for Local and Global Biases in

    Ravfogel, Shauli and Goldberg, Yoav and Tyers, Francis , journal =. Linear Guardedness and its Implications for Local and Global Biases in

  80. [82]

    Nature Medicine , year=

    Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot , author=. Nature Medicine , year=

Showing first 80 references.