Research Notes & References

Greenwald, A. G., & Banaji, M. R. (1995). “Implicit Social Cognition: Attitudes, Self-Esteem, and Stereotypes.” Psychological Review, 102(1), 4–27.

Useful for the broader proposition that previous experiences and learned associations can influence judgment outside deliberate conscious awareness. It should not be presented as evidence that every individual harbors hidden prejudice.

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). “Semantics Derived Automatically from Language Corpora Contain Human-like Biases.” Science, 356(6334), 183–186.

Useful for explaining how statistical patterns contained in human-produced language can become measurable within machine representations.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” Science, 366(6464), 447–453.

Used as one concise example of how bias may arise not through an explicit discriminatory instruction but through the choice of an inappropriate proxy.

Schwartz, R., et al. (2022). Towards a Standard for Identifying and Managing Bias in Artificial Intelligence. NIST Special Publication 1270.

Useful because NIST treats AI bias broadly, including systemic, computational/statistical and human cognitive sources rather than reducing AI bias to overt demographic prejudice.

ElSherief, M., et al. (2021). “Latent Hatred: A Benchmark for Understanding Implicit Hate Speech.” EMNLP 2021.

Hartvigsen, T., et al. (2022). “ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection.” ACL 2022.

These support discussion of the technical difficulty of identifying contextual or implicit harmful language without treating identity terms themselves as evidence of harmful intent.

Katsaros, M., Yang, K., & Fratamico, L. (2022). “Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content.” Proceedings of ICWSM.

Useful for the proposition that an intervention before transmission can measurably affect posting behavior without removing the user’s ability to decide whether to post.

Ivie, E. J., et al. (2020). “A Meta-Analysis of the Association Between Adolescent Social Media Use and Depressive Symptoms.” Journal of Affective Disorders.

Shannon, H., et al. (2022). “Problematic Social Media Use in Adolescents and Young Adults: Systematic Review and Meta-analysis.” JMIR Mental Health.

National Academies of Sciences, Engineering, and Medicine (2024). Social Media and Adolescent Health.

These sources support a cautious framing: digital and social-media experiences can be associated with psychological well-being outcomes, but the evidence should not be represented as showing that a single biased comment directly causes mental illness.

McCulloch, W. S., & Pitts, W. (1943). “A Logical Calculus of the Ideas Immanent in Nervous Activity.”

Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine.

McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955). “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.”

These provide the historical bridge from early computational models of neurons and cybernetics to the emergence of artificial intelligence as a named research field.

Maass, A., Salvi, C., Arcuri, L., & Semin, G. R. (1989). “Language use in intergroup contexts: The linguistic intergroup bias.” Journal of Personality and Social Psychology, 57, 981–993.

The research examined systematic differences in how favorable and unfavorable behaviors involving in-group and out-group members are linguistically described.

Sap, M., Gabriel, S., Qin, L., Jurafsky, D., Smith, N. A., & Choi, Y. (2020). “Social Bias Frames: Reasoning about Social and Power Implications of Language.”

The work examines how social bias frequently operates through implied meanings and pragmatic assumptions rather than only explicit derogatory language.

Brady, W. J., et al. (2021). “How social learning amplifies moral outrage expression in online social networks.” Science Advances.

The researchers found that social reinforcement and social norms can contribute to increased expressions of moral outrage in online environments.

Saveski, M., Roy, B., & Roy, D. (2021). “The Structure of Toxic Conversations on Twitter.”

Analysis of approximately 1.18 million conversations found that conversational structure and linguistic characteristics provided useful information about the emergence of toxicity and that toxicity was not restricted to a small population of uniformly toxic users.

Borkan, D., et al. (2019). “Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification.”

The research documented unintended identity-related bias in toxicity classification and proposed improved evaluation metrics.

Perspective API documentation.

Perspective describes toxicity scores probabilistically in terms of predicted reader perceptions rather than as definitive judgments about a speaker or text.

Grice’s work on speaker meaning and conversational implicature established an influential distinction among the literal meanings of words, what speakers say, and what speakers intend to communicate. Contemporary pragmatics continues to examine how context, intentions, shared knowledge, and inference shape interpretation.

Grice, H. P. (1975). Logic and Conversation. In P. Cole & J. L. Morgan (Eds.), Syntax and Semantics, Volume 3: Speech Acts. Academic Press.

Grice, H. P. (1957). Meaning. The Philosophical Review, 66(3), 377–388.

Ross’s classic work on attribution examined how people make causal judgments about others’ behavior and how those judgments can be distorted by systematic tendencies in human interpretation.

Ross, L. (1977). The intuitive psychologist and his shortcomings: Distortions in the attribution process. Advances in Experimental Social Psychology, 10, 173–220. https://doi.org/10.1016/S0065-2601(08)60357-3

Walther’s work demonstrates that interpersonal communication through computer-mediated environments cannot simply be treated as conventional face-to-face communication with a keyboard added; technological channels alter available social cues and how interpersonal impressions develop.

Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23(1), 3–43. https://doi.org/10.1177/009365096023001001

The National Institute of Standards and Technology’s AI Risk Management Framework identifies characteristics of trustworthy AI including validity and reliability, transparency, explainability, interpretability, accountability, fairness with harmful bias managed, safety, privacy enhancement, and human judgment in context-sensitive risk decisions.

National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1.

Li, Z., Tian, Y., Lan, T., Li, J., Duo, Z., Gao, G., & Su, X. (2026). “Born Pragmatic, Trained to Hallucinate? Quantifying the Origins of Contextual Bias in LLMs via the PaCE Benchmark.” Findings of ACL 2026.

Episode use: PaCE includes more than 3,000 manually verified context-flip samples and evaluates nine mainstream models. The paper reports a Context Sensitivity Gap in which literal accuracy lags pragmatic reasoning; attribution analysis implicates RLHF, and strict prompting can reverse the gap.

Guo, J., Guo, X., Hu, Y., Long, Z., Sui, X., Zhi, X., Huang, Y., He, H., Zhao, W., Zhao, Y., & Qin, B. (2026). “When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents.” Proceedings of ACL 2026.

Episode use: Introduces “intent legitimation” and PS-Bench. Across tested memory-augmented frameworks and base LLMs, personalization increased attack success rates by 15.8%–243.7% relative to stateless baselines; the authors also report a detection-reflection mitigation.

Jain, S. et al. (2026). Research summarized by MIT News, “Personalization features can make LLMs more agreeable,” February 18, 2026.

Episode use: A two-week study with 38 participants and five LLMs found that conversational context increased agreement sycophancy in four of five models studied; condensed user profiles had the strongest effect, while perspective mirroring depended on successful belief inference.

Phan, K. D., & Truong-Dinh, B. Q. (2026). “When conversational AI personalises too much: Refining privacy calculus for bundled interactional cues in AI-mediated disclosure.” Computers in Human Behavior. DOI: 10.1016/j.chb.2026.109061.

Episode use: Across four experiments, conversational personalization suppressed self-disclosure, heightened privacy-risk salience, and showed that perceived control conditions whether personalization backfires or stabilizes disclosure.

Katsaros, M., Yang, K., & Fratamico, L. (2022). Reconsidering Tweets: Intervening during Tweet Creation Decreases Offensive Content. Proceedings of the International AAAI Conference on Web and Social Media, 16, 477–487. https://doi.org/10.1609/icwsm.v16i1.19308

Use in episode: Randomized Twitter experiment. Prompted users posted about 6% fewer offensive tweets than controls; effects also appeared in later posting behavior and in offensive replies received.

Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention. Psychological Science, 31(7), 770–780. https://doi.org/10.1177/0956797620939054

Use in episode: Across two studies with more than 1,700 U.S. adults, redirecting attention toward accuracy improved truth discernment in subsequent sharing intentions.

Pennycook, G., & Rand, D. G. (2022). Nudging Social Media toward Accuracy. The ANNALS of the American Academy of Political and Social Science, 700(1). https://doi.org/10.1177/00027162221092342

Use in episode: Reviews evidence that inattention to accuracy contributes to misinformation sharing and that brief attention shifts can improve sharing discernment.

Brady, W. J., McLoughlin, K., Doan, T. N., & Crockett, M. J. (2021). How social learning amplifies moral outrage expression in online social networks. Science Advances, 7(33), eabe5641. https://doi.org/10.1126/sciadv.abe5641

Use in episode: Two observational Twitter studies covering 7,331 users and 12.7 million tweets, plus two behavioral experiments, found that social feedback and network norms shape future outrage expression.

Cutuli, D. (2014). Cognitive reappraisal and expressive suppression strategies role in the emotion regulation: an overview on their modulatory effects and neural correlates. Frontiers in Systems Neuroscience, 8, 175. https://doi.org/10.3389/fnsys.2014.00175

Use in episode: Review of reappraisal versus expressive suppression; useful for explaining why the timing and form of regulation can matter.

Horta Ribeiro, M., West, R., Lewis, R., & Kairam, S. (2025). Post Guidance for Online Communities. Proceedings of the ACM on Human-Computer Interaction, 9(2), Article CSCW148. https://doi.org/10.1145/3711046

Use in episode: Randomized field experiment with 97,616 posters in 33 subreddits over 63 days. Post Guidance increased successful posts, reduced manually reviewed reports, increased contribution quality, and did not reduce subsequent activity within participating communities.

Lally, P., van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2010). How are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology, 40, 998–1009. https://doi.org/10.1002/ejsp.674

Episode use: In a 12-week study of 96 volunteers, the estimated time to reach 95% of asymptotic automaticity ranged from 18 to 254 days among participants whose data fit the model, emphasizing large individual differences and the role of repeated behavior in stable contexts.

Wood, W., & Rünger, D. (2016). Psychology of Habit. Annual Review of Psychology, 67, 289–314. https://doi.org/10.1146/annurev-psych-122414-033417

Episode use: Review describing habits as efficient default responses that emerge through repeated responses in recurring contexts and interact with deliberate goal pursuit.

Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284.

Episode use: Meta-analysis of 607 effect sizes and 23,663 observations found an overall positive effect of feedback, while roughly one third of feedback interventions reduced subsequent performance — useful for the argument that feedback quality and attentional focus matter.

Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., Eccles, M. P., Cane, J., & Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 46(1), 81–95. https://doi.org/10.1007/s12160-013-9486-6

Episode use: Establishes a taxonomy of 93 distinct behavior-change techniques in 16 groups, supporting the point that prompts, feedback, self-monitoring, rehearsal, goal setting, cues and other mechanisms are different intervention ingredients rather than one generic form of “help.”

Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine, 52(6), 446–462. https://doi.org/10.1007/s12160-016-9830-8

Episode use: Defines JITAIs as adaptive interventions intended to deliver the right type or amount of support at the right time while avoiding support that is interruptive or not beneficial. Used here as a design analogy, not as evidence specific to communication systems.

Kron, F. W., Fetters, M. D., Scerbo, M. W., White, C. B., Lypson, M. L., Padilla, M. A., Gliva-McConvey, G. A., Belfore, L. A., West, T., Wallace, A. M., Guetterman, T. C., Schleicher, L. S., Kennedy, R. A., Mangrulkar, R. S., & Cleary, J. F. (2017). Using a computer simulation for teaching communication skills: A blinded multisite mixed methods randomized controlled trial. Patient Education and Counseling, 100(4), 748–759. https://doi.org/10.1016/j.pec.2016.10.024

Episode use: Randomized multisite trial with 421 second-year medical students. Virtual-human training with feedback improved performance within scenarios and produced higher communication scores on a subsequent clinically realistic assessment, providing evidence of transfer.

Saga, T., Tanaka, H., Matsuda, Y., Morimoto, T., Uratani, M., Okazaki, K., Fujimoto, Y., et al. (2023). Automatic evaluation-feedback system for automated social skills training. Scientific Reports, 13, 6856. https://doi.org/10.1038/s41598-023-33703-0

Episode use: Developed an automated social-skills evaluation-feedback system; user testing suggested a preferred feedback balance of approximately two positive comments to one corrective comment.

Tanaka, H., Saga, T., Iwauchi, K., Honda, M., Morimoto, T., Matsuda, Y., Uratani, M., Okazaki, K., & Nakamura, S. (2023). The Validation of Automated Social Skills Training in Members of the General Population Over 4 Weeks: Comparative Study. JMIR Formative Research, 7, e44857. https://doi.org/10.2196/44857

Episode use: Small four-week comparative study reported improvements in generalized self-efficacy, state anxiety presence, and speech clarity, illustrating the possibility of repeated automated training while also warranting caution because of the small sample.

Herschbach, L., Festl-Wietek, T., Stegemann-Philipps, C., Sonanini, A., Herrmann, B., Erschens, R., Herrmann-Werner, A., & Holderried, F. (2025). Evaluation of an AI-Based Chatbot Providing Real-Time Feedback in Communication Training for Mental Health Care Professionals: Proof-of-Concept Observational Study. Journal of Medical Internet Research, 27, e82818. https://doi.org/10.2196/82818

Episode use: Proof-of-concept work using AI-simulated patients and real-time feedback; most feedback was judged completely or partially correct, participants generally rated it accurate and helpful, and targeted communication techniques increased during practice. Long-term objective effects remain an open question.

Zhang, Y., Zhou, H., Huo, H., Yue, Y., Wang, J., & Zhu, J. (2026). AI-based virtual patient training for clinical communication skills in dental undergraduates: A randomized controlled mixed methods study. Medical Teacher. https://doi.org/10.1080/0142159X.2026.2681199

Episode use: Randomized trial with 60 fourth-year dental students over eight weeks. The AI virtual-patient group showed higher post-intervention communication and inquiry-completeness scores than peer role-play controls, with reported effects sustained at three months; self-reported empathy, motivation and confidence were also higher.

Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420

Episode use: Meta-analysis of 50 controlled evaluations found a median effect of 0.66 standard deviations for intelligent tutoring over conventional instruction, while also showing that effects depended strongly on implementation and outcome alignment.

Han, X., Peng, H., & Liu, M. (2025). The impact of GenAI on learning outcomes: A systematic review and meta-analysis of experimental studies. Educational Research Review, 48, 100714. https://doi.org/10.1016/j.edurev.2025.100714

Episode use: Recent meta-analytic evidence that GenAI interventions can improve learning outcomes, with effects varying across learning dimensions and contexts. Used cautiously as broader educational evidence rather than proof specific to communication learning.

Yan, L., Greiff, S., Teuber, Z., et al. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8, 1839–1850.

Episode use: Provides a learning-science perspective on GenAI opportunities and risks, including the tension between productive support and over-reliance.

Matias, J. N. (2019). Preventing harassment and increasing group participation through social norms in 2,190 online science discussions. Proceedings of the National Academy of Sciences, 116(20), 9785–9789. https://doi.org/10.1073/pnas.1813486116

Episode use: Large-scale field experiment in Reddit’s r/science community. Displaying community rules increased newcomer rule compliance by more than 8 percentage points and increased newcomer participation by about 70% on average.

Mertens, S., Herberz, M., Hahnel, U. J. J., & Brosch, T. (2022). The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proceedings of the National Academy of Sciences, 119(1), e2107346118. https://doi.org/10.1073/pnas.2107346118

Episode use: Meta-analysis of more than 200 studies and over two million participants. Reported an overall small-to-medium effect of choice architecture interventions, substantial heterogeneity by technique/domain, and evidence of publication bias.

Maier, M., Bartoš, F., Stanley, T. D., Shanks, D. R., Harris, A. J. L., & Wagenmakers, E.-J. (2022). No evidence for nudging after adjusting for publication bias. Proceedings of the National Academy of Sciences, 119(31), e2200300119. https://doi.org/10.1073/pnas.2200300119

Episode use: Critical reanalysis arguing that publication-bias adjustment eliminates evidence for an overall nudge effect. Included to keep the episode’s claims about choice architecture appropriately cautious.

Oz, M., & Greeves, S. (2024). Examining the impact of incivility and intolerance on participation in social media discussions: An experimental investigation. First Monday, 29(11). https://doi.org/10.5210/fm.v29i11.13640

Episode use: Online experiment with 1,192 participants. Intolerant discourse increased disgust and anxiety and reduced willingness to participate; incivility produced a more complex pattern in which anger could increase participation.

Gao, Y., et al. (2024). A Crisis of Civility? Modeling Incivility and Its Effects in Political Discourse Online. Proceedings of the International AAAI Conference on Web and Social Media, 18(1), 408–421. https://doi.org/10.1609/icwsm.v18i1.31323

Episode use: Large-scale analysis showing that conversations beginning with incivility tend to become more uncivil in subsequent responses, with different forms of incivility affecting engagement differently.

Bento-Silva, L. S., Cachero-Martínez, S., & García-Rodríguez, N. (2026). How Dark Patterns Shape Consumer Behavior on Social Media: A Systematic Literature Review and Research Agenda. International Journal of Consumer Studies, 50(5), e70240. https://doi.org/10.1111/ijcs.70240

Episode use: Systematic review of 56 peer-reviewed articles examining how interface design, social cues and platform architectures influence engagement, privacy-related decisions, purchasing behavior and digital well-being. The review also notes important methodological gaps and limited behavioral evidence in parts of the literature.

Lasser, J., & Poechhacker, N. (2025). Designing social media content recommendation algorithms for societal good. Annals of the New York Academy of Sciences, 1548(1), 20–28. https://doi.org/10.1111/nyas.15359

Episode use: Perspective framing social-media platforms as environments composed of affordances, visual design, language, terms, and content-recommendation systems. Episode 7 uses the broader design framing only; the recommendation-algorithm discussion is deliberately reserved for Episode 8.

Oswald, L., Schulz, W. S., & Lorenz-Spreen, P. (2025). Disentangling participation in online political discussions with a collective field experiment. Science Advances, 11(50), eady8022. https://doi.org/10.1126/sciadv.ady8022

Episode use: Collective field experiment with 520 U.S. participants in six private Reddit communities over four weeks. A civility-norm/moderation treatment did not reduce participation inequality, while financial incentives produced a limited reduction. Perceptions of toxicity and disrespect were associated both with lurking and, among active users, heavier participation — reinforcing the point that the same environment can affect different participants differently.

Glickman, M., & Sharot, T. (2025). How human–AI feedback loops alter human perceptual, emotional and social judgements. Nature Human Behaviour, 9, 345–359. https://doi.org/10.1038/s41562-024-02077-2

Episode use: Across experiments involving 1,401 participants, biased AI judgments could amplify human biases through repeated interaction; participants often underestimated the AI’s influence. The paper also shows that accurate AI can improve judgments, so the episode uses the finding as evidence for bidirectional feedback rather than as a claim that AI influence is inherently harmful.

Guess, A. M., et al. (2023). How do social media feed algorithms affect attitudes and behavior in an election campaign? Science, 381(6656), 398–404. https://doi.org/10.1126/science.abp9364

Episode use: During the 2020 U.S. election, assigning consenting users to reverse-chronological feeds reduced time spent and activity and changed content exposure, but did not significantly change several preregistered political-attitude outcomes over the three-month study period.

Nyhan, B., et al. (2023). Like-minded sources on Facebook are prevalent but not polarizing. Nature, 620, 137–144. https://doi.org/10.1038/s41586-023-06297-w

Episode use: Reducing exposure to like-minded content by about one-third increased cross-cutting exposure and reduced exposure to uncivil language but produced no measurable changes in eight preregistered attitudinal outcomes.

González-Bailón, S., et al. (2023). Asymmetric ideological segregation in exposure to political news on Facebook. Science, 381(6656), 392–398. https://doi.org/10.1126/science.ade7138

Episode use: Analysis of aggregated data for 208 million U.S. Facebook users found ideological segregation was high and increased from potential exposure to actual exposure to engagement, supporting the distinction among what is available, what algorithms surface, and what users choose.

Robertson, R. E., Green, J., Ruck, D. J., Ognyanova, K., Wilson, C., & Lazer, D. (2023). Users choose to engage with more partisan news than they are exposed to on Google Search. Nature, 618, 342–348. https://doi.org/10.1038/s41586-023-06078-5

Episode use: In studies pairing surveys with real exposure and engagement data, users chose more identity-congruent and unreliable news than appeared in their Google Search exposure, supporting the episode’s argument that algorithmic curation and human choice can jointly shape information diets.

Huszár, F., et al. (2022). Algorithmic amplification of politics on Twitter. Proceedings of the National Academy of Sciences, 119(1), e2025334119. https://doi.org/10.1073/pnas.2025334119

Episode use: A large randomized comparison of personalized and reverse-chronological timelines found systematic differences in amplification of political content. The research is used as evidence that ranking systems can alter visibility, not as a basis for making an electoral recommendation or partisan judgment.

Gauthier, G., Hodler, R., Widmer, P., et al. (2026). The political effects of X’s feed algorithm. Nature, 652, 416–423. https://doi.org/10.1038/s41586-026-10098-2

Episode use: Independent seven-week randomized field experiment with active U.S. X users. Switching some chronological-feed users to the algorithmic feed increased engagement and shifted several measured political attitudes and account-following behavior; no significant effects were found on self-reported partisanship or affective polarization. The study is contrasted with the Meta experiments to emphasize platform-specific effects.

Algorithmic recommendation and adolescent mental health: a grounded theory study of social and commercial determinants. (2026). BMC Public Health. https://doi.org/10.1186/s12889-026-28300-5

Episode use: Participants described recommendation reinforcement driven by likes, comments, follows, shares and viewing behavior, including cases where curiosity, disagreement or concern was interpreted as interest. Participants also described strategies for actively reshaping their feeds.

Möller, J., et al. (2023). Modeling news recommender systems’ conditional effects on selective exposure: evidence from two online experiments. Journal of Communication, 73(2), 138–152.

Episode use: Supports the conceptual discussion of recommendation systems as part of a self-reinforcing process in which user choices influence later options and those options influence future choices. Used cautiously because experimental effects depend on design and context.

Ibrahim, O. A. S., et al. (2025). Revisiting recommender systems: an investigative survey. Neural Computing and Applications, 37, 2145–2173. https://doi.org/10.1007/s00521-024-10828-5

Episode use: Background on common recommender approaches, personalization, cold-start challenges, and concerns about narrowing exposure. Used mainly as technical scaffolding rather than spoken attribution.

Geber, S., & Stahel, L. (2026). Recommended to you: an experimental study of normative influences from algorithmic and social recommendations on social media. AI & SOCIETY, 41, 5827–5840. https://doi.org/10.1007/s00146-026-02952-8

Episode use: Experimental survey with 1,021 participants comparing social, algorithmic, popularity-based algorithmic and no-recommendation conditions. Recommendation type did not significantly alter perceived social norms, providing a useful counterweight to deterministic claims about recommendation cues. Perceived social norms themselves were associated with intentions to engage.

Lee, N., Jung, C., Myung, J., Jin, J., Camacho-Collados, J., Kim, J., & Oh, A. (2024). Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis. NAACL 2024, 4205–4224. https://aclanthology.org/2024.naacl-long.236/

Episode use: Annotation study across multiple countries. Only 56.2% of posts received consensus labels across all countries; pairwise differences reached 26%. Disagreement frequently involved sarcasm and divisive topics. Supports the argument that culturally situated judgments should not be mistaken for universal ground truth.

Shahid, F., Elswah, M., & Vashistha, A. (2025). Think Outside the Data: Colonial Biases and Systemic Issues in Automated Moderation Pipelines for Low-Resource Languages. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1609/aies.v8i3.36719

Episode use: Interviews with 22 researchers/practitioners working in Tamil, Swahili, Maghrebi Arabic, and Quechua. Finds that data scarcity interacts with platform investment, data access, linguistic complexity, and systemic inequities.

Elswah, M., Bhatia, A., & Thakur, D. (2025). Content Moderation in the Global South: A Comparative Study of Four Low-Resource Languages. Center for Democracy & Technology. https://cdt.org/insights/content-moderation-in-the-global-south-a-comparative-study-of-four-low-resource-languages/

Episode use: Comparative work across Maghrebi Arabic, Kiswahili, Tamil, and Quechua; documents dialect/context challenges, cultural-competency gaps, and uneven moderation resources.

Okpala, E., & Cheng, L. (2025). Large Language Model Annotation Bias in Hate Speech Detection. Proceedings of ICWSM 2025, 1389–1418. https://doi.org/10.1609/icwsm.v19i1.35879

Episode use: Classifiers trained on LLM annotations showed higher negative-class assignments and higher false-positive rates for African American English than Standard American English.

Davidson, T. (2025/2026). Multimodal large language models can make context-sensitive hate speech evaluations aligned with human judgement. Nature Human Behaviour, 10, 514–530. https://doi.org/10.1038/s41562-025-02360-w

Episode use: Benchmarks model judgments against 1,854 human participants. Larger models show substantial context sensitivity and alignment with human judgments, while demographic and lexical biases remain, especially in smaller models.

Villate-Castillo, G., Del Ser, J., & Sanz, B. (2025). A collaborative content moderation framework for toxicity detection based on multitask neural networks and conformal estimates of annotation disagreement. Neurocomputing, 647, 130542. https://doi.org/10.1016/j.neucom.2025.130542

Episode use: Treats annotator disagreement as information about ambiguity and uses uncertainty estimates to identify cases that may require human review.

Calabrese, A., Neves, L., Shah, N., Bos, M., Ross, B., Lapata, M., & Barbieri, F. (2024). Explainability and Hate Speech: Structured Explanations Make Social Media Moderators Faster. ACL 2024. https://aclanthology.org/2024.acl-short.38/

Episode use: With real-world moderators, structured explanations reduced decision time by 7.4%, while generic explanations did not improve speed and were often ignored.

Qureshi, M. D. M., Qureshi, M. A., & Rashwan, W. (2026). Explainable AI for Hate Speech Moderation: A Stakeholder-Centered and Sociotechnical Review. WIREs Data Mining and Knowledge Discovery, 16(1), e70076. https://doi.org/10.1002/widm.70076

Episode use: Argues that meaningful explainability depends on stakeholder needs and requires cultural sensitivity, accountability, inclusive design, and mechanisms beyond technical feature attribution.

Park, J., Jeong, S., Song, S., Lee, Y., & Oh, A. (2025). LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation. C3NLP 2025, 71–88. https://aclanthology.org/2025.c3nlp-1.7/

Episode use: Cultural-context annotations improved non-native moderator accuracy in the study, and the hybrid pipeline reduced human workload while preserving targeted human review for cases without model consensus.

Huang, T. (2025). Content moderation by LLM: from accuracy to legitimacy. Artificial Intelligence Review, 58, 320. https://doi.org/10.1007/s10462-025-11328-1

Episode use: Provides the governance framing that moderation cannot be evaluated only by accuracy; difficult cases involve normative judgment, legitimacy, and procedural concerns.

Vaccaro, M., Almaatouq, A., & Malone, T. W. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8, 2293-2303. https://doi.org/10.1038/s41562-024-02024-1

Episode use: Preregistered systematic review and meta-analysis of 106 experiments and 370 effect sizes. Human-AI combinations performed better than humans alone on average, but worse than the better of human-alone or AI-alone performance. Decision tasks showed performance losses on average, while creation tasks were more promising. Supports the episode’s argument that human-AI collaboration must be designed rather than assumed to produce synergy.

National Institute of Standards and Technology (NIST). Human-Centered AI program and AI Risk Management Framework resources. https://www.nist.gov/programs-projects/human-centered-ai and https://airc.nist.gov/

Episode use: Supports discussion of keeping humans central to AI design and of documenting oversight, overrides, errors, complaints, escalations and organizational accountability around deployed AI systems.

Mitigating Automation Bias in Generative AI Through Nudges: A Cognitive Reflection Test Study. Procedia Computer Science, 270 (2025), 2106-2114. https://doi.org/10.1016/j.procs.2025.09.331

Episode use: Experimental conditions comparing no support, faulty AI support, and faulty AI support plus a warning nudge. Faulty AI assistance substantially reduced performance relative to no support; a warning nudge improved performance relative to faulty AI alone but did not exceed the no-support group. Supports the distinction between nominal human oversight and active critical engagement.

Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making. PLOS ONE (2024). https://doi.org/10.1371/journal.pone.0298037

Episode use: Experimental evidence that retaining human oversight can increase willingness to use automated recommendations while human monitors may still exhibit automation bias and make adjustments that reduce accuracy. Supports the episode’s warning that a human being technically present in a workflow does not guarantee effective oversight.

Göndöcs, D., Horváth, S., & Dörfler, V. (2025). Uncovering the dynamics of human-AI hybrid performance: A qualitative meta-analysis of empirical studies. International Journal of Human-Computer Studies, 205, 103622. https://doi.org/10.1016/j.ijhcs.2025.103622

Episode use: Review of empirical human-AI studies identifies interacting AI, human, interaction and task-related factors, including transparency, trust, reliance, expertise, decision authority, workflow configuration and task complexity. Supports the argument that successful human-AI collaboration is a system-design problem rather than a simple matter of adding AI assistance.

Zuboff, S. (1988). In the Age of the Smart Machine: The Future of Work and Power. Basic Books.

Episode use: Referenced in the discussion of earlier technological transformation, particularly the shift associated with computerization and changing organizational work, as a historical comparison point for today’s transition toward AI-mediated work and decision-making.