People Dev AI
AI Literacy · Daftar pustaka

Referensi kursus

Kursus AI Literacy disusun dari 43 sumber: 42 paper dan jurnal peer-reviewed, ditambah regulasi Indonesia. Setiap sumber sudah dicek: DOI atau tautannya bisa dibuka, dan angka yang dikutip di modul diambil dari paper aslinya.

Cara kami memakai sumber

Per modul:

Semua sumber (APA 7)

  1. ProsidingBender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). doi.org/10.1145/3442188.3445922
  2. ProsidingBianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., Hashimoto, T., Jurafsky, D., Zou, J., & Caliskan, A. (2023). Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 1493–1504). doi.org/10.1145/3593013.3594095
  3. ProsidingBrown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … Amodei, D. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems 33 (pp. 1877–1901). doi.org/10.48550/arXiv.2005.14165
  4. JurnalBrynjolfsson, E. (2022). The Turing trap: The promise & peril of human-like artificial intelligence. Daedalus, 151(2), 272–287. doi.org/10.1162/daed_a_01915
  5. JurnalBrynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. doi.org/10.1093/qje/qjae044
  6. JurnalCaliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. doi.org/10.1126/science.aal4230
  7. ProsidingCarlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, Ú., Oprea, A., & Raffel, C. (2021). Extracting training data from large language models. In Proceedings of the 30th USENIX Security Symposium (pp. 2633–2650). www.usenix.org/conference/usenixsecurity21/presentation/carlini-extracting
  8. JurnalDell’Acqua, F., McFowland, E., Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Organization Science, 37(2), 403–423. doi.org/10.1287/orsc.2025.21838
  9. JurnalEloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306–1308. doi.org/10.1126/science.adj0998
  10. JurnalGoddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121–127. doi.org/10.1136/amiajnl-2011-000089
  11. JurnalHuang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2025). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems, 43(2), 1–55. doi.org/10.1145/3703155
  12. JurnalJi, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. doi.org/10.1145/3571730
  13. ProsidingKojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems 35 (pp. 22199–22213). doi.org/10.52202/068431-1613
  14. ProsidingKong, A., Zhao, S., Chen, H., Li, Q., Qin, Y., Sun, R., Zhou, X., Wang, E., & Dong, X. (2024). Better zero-shot reasoning with role-play prompting. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 4099–4113). doi.org/10.18653/v1/2024.naacl-long.228
  15. JurnalKrizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. doi.org/10.1145/3065386
  16. JurnalLeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. doi.org/10.1038/nature14539
  17. ProsidingLee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1–22). doi.org/10.1145/3706598.3713778
  18. JurnalLiu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024). Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics, 12, 157–173. doi.org/10.1162/tacl_a_00638
  19. JurnalLiu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1–35. doi.org/10.1145/3560815
  20. ProsidingLong, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). doi.org/10.1145/3313831.3376727
  21. JurnalMai, K. T., Bray, S., Davies, T., & Griffin, L. D. (2023). Warning: Humans cannot reliably detect speech deepfakes. PLOS ONE, 18(8), e0285333. doi.org/10.1371/journal.pone.0285333
  22. JurnalMcCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12–14. doi.org/10.1609/aimag.v27i4.1904
  23. ProsidingMishra, S., Khashabi, D., Baral, C., Choi, Y., & Hajishirzi, H. (2022). Reframing instructional prompts to GPTk’s language. In Findings of the Association for Computational Linguistics: ACL 2022 (pp. 589–612). doi.org/10.18653/v1/2022.findings-acl.50
  24. JurnalMosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Review, 56(4), 3005–3054. doi.org/10.1007/s10462-022-10246-w
  25. JurnalNg, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. doi.org/10.1016/j.caeai.2021.100041
  26. JurnalNightingale, S. J., & Farid, H. (2022). AI-synthesized faces are indistinguishable from real faces and more trustworthy. Proceedings of the National Academy of Sciences, 119(8), e2120481119. doi.org/10.1073/pnas.2120481119
  27. JurnalNoy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. doi.org/10.1126/science.adh2586
  28. JurnalObermeyer, 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. doi.org/10.1126/science.aax2342
  29. ProsidingOuyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., … Lowe, R. (2022). Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems 35 (pp. 27730–27744). doi.org/10.52202/068431-2011
  30. JurnalParasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. doi.org/10.1177/0018720810376055
  31. JurnalParasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 30(3), 286–297. doi.org/10.1109/3468.844354
  32. JurnalRaisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. doi.org/10.5465/amr.2018.0072
  33. RegulasiRepublik Indonesia. (2022). Undang-Undang Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi. peraturan.bpk.go.id/Details/229798/uu-no-27-tahun-2022
  34. ProsidingSchick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. In Advances in Neural Information Processing Systems 36 (pp. 68539–68551). doi.org/10.52202/075280-2997
  35. JurnalShneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. doi.org/10.1080/10447318.2020.1741118
  36. JurnalTuring, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. doi.org/10.1093/mind/LIX.236.433
  37. ProsidingVaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30 (pp. 5998–6008). doi.org/10.48550/arXiv.1706.03762
  38. JurnalWalters, W. H., & Wilder, E. I. (2023). Fabrication and errors in the bibliographic citations generated by ChatGPT. Scientific Reports, 13, 14045. doi.org/10.1038/s41598-023-41032-5
  39. JurnalWang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., & Wen, J. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6), 186345. doi.org/10.1007/s11704-024-40231-1
  40. ProsidingWei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems 35 (pp. 24824–24837). doi.org/10.52202/068431-1800
  41. ProsidingYao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR 2023). doi.org/10.48550/arXiv.2210.03629
  42. ProsidingZamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023). Why Johnny can’t prompt: How non-AI experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–21). doi.org/10.1145/3544548.3581388
  43. ProsidingZheng, M., Pei, J., Logeswaran, L., Lee, M., & Jurgens, D. (2024). When “a helpful assistant” is not really helpful: Personas in system prompts do not improve performances of large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 15126–15154). doi.org/10.18653/v1/2024.findings-emnlp.888