he papers behind the human–AI interaction pages, in date order, each with two or three sentences on what it did and why it matters. Every DOI was checked against Crossref before it went on the shelf; the one technical memo without a DOI links to its official archive. The notes are our summaries, not quotations.

1. Licklider (1960). Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics. doi:10.1109/THFE2.1960.4503259
   A psychologist's case that computers should be interactive partners in thinking, each side doing what it does best. The founding statement of the hope that human–AI teams beat either alone. Used in: complementarity  Licklider
2. Weizenbaum (1966). ELIZA—A Computer Program For the Study of Natural Language Communication Between Man and Machine. Communications of the ACM. doi:10.1145/365153.365168
   Describes ELIZA's keyword-and-transformation design in full. Ends by noting how easily people credit the program with understanding, which became the ELIZA effect. Used in: anthropomorphism  Weizenbaum
3. Colby, Weber & Hilf (1971). Artificial Paranoia. Artificial Intelligence. doi:10.1016/0004-3702(71)90002-6
   PARRY, a program simulating a person with paranoia, and early tests of whether psychiatrists could tell its interview transcripts from real patients'. A forerunner of every "can judges tell?" study. Used in: oddities
4. Cerf (1973). PARRY encounters the DOCTOR. RFC 439, Network Working Group (a transcript of a session on 18 September 1972). www.rfc-editor.org/rfc/rfc439
   A transcript of PARRY and ELIZA's DOCTOR script talking to each other over the ARPANET. Two machines that answer, answering each other; short and very readable. Used in: timeline
5. Nass, Steuer & Tauber (1994). Computers are social actors. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. doi:10.1145/191666.191703
   The "computers are social actors" paper: people applied politeness and other social rules to computers in controlled experiments, while denying they would. Launched decades of work. Used in: anthropomorphism
6. Reeves & Nass (1996). The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press.
   The book-length summary of the social-actors programme: people respond to media socially and automatically. Accessible and still widely cited. Used in: anthropomorphism
7. Parasuraman & Riley (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society. doi:10.1518/001872097778543886
   Defines use, misuse, disuse and abuse of automation, with examples from aviation and process control. The vocabulary every later paper on reliance uses. Used in: automation bias
8. Horvitz (1999). Principles of mixed-initiative user interfaces. Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI '99. doi:10.1145/302979.303030
   Twelve principles for interfaces in which system and user share initiative, built around the system's uncertainty about the user's goals. A design classic for deciding when an assistant should act. Used in: complementarity
9. Skitka, Mosier & Burdick (1999). Does automation bias decision-making?. International Journal of Human-Computer Studies. doi:10.1006/ijhc.1999.0252
   Experiments showing omission and commission errors when people work with an automated aid. The standard empirical reference for automation bias. Used in: automation bias
10. Lee & See (2004). Trust in Automation: Designing for Appropriate Reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society. doi:10.1518/hfes.46.1.50_30392
    A long review arguing for calibrated, not maximal, trust in automation, and analysing what trust is built from. The theoretical centre of the reliance literature. Used in: automation bias  calibration
11. Epley, Waytz & Cacioppo (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review. doi:10.1037/0033-295X.114.4.864
    A three-factor theory of when people anthropomorphize: accessible human knowledge, the need to explain and predict, and the need for social connection. Makes testable predictions, several since confirmed. Used in: anthropomorphism
12. Waytz, Heafner & Epley (2014). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology. doi:10.1016/j.jesp.2014.01.005
    Giving a simulated self-driving car a name, gender and voice raised trust and reduced blame. Anthropomorphism with consequences. Used in: anthropomorphism
13. Dietvorst, Simmons & Massey (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General. doi:10.1037/xge0000033
    Algorithm aversion: after watching an algorithm err, people preferred a human forecaster even when the algorithm was better. Widely cited, and often over-generalized. Used in: automation bias
14. Logg, Minson & Moore (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes. doi:10.1016/j.obhdp.2018.12.005
    The counterweight: in many tasks lay people weighted algorithmic advice more than human advice; experts did not. Read with Dietvorst et al. Used in: automation bias
15. Bansal et al. (2021). Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. doi:10.1145/3411764.3445717
    AI explanations increased acceptance of the AI's advice whether it was right or wrong, so they did not create complementary performance. A sobering result for explainability. Used in: complementarity
16. Buçinca, Malaya & Gajos (2021). To Trust or to Think. Proceedings of the ACM on Human-Computer Interaction. doi:10.1145/3449287
    Cognitive forcing designs reduced overreliance on AI suggestions, at a cost in how much people liked the system. Useful for anyone designing review workflows. Used in: complementarity
17. Steyvers et al. (2022). Bayesian modeling of human–AI complementarity. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.2111547119
    A Bayesian framework and data showing when combining human and machine classifications yields complementarity. Emphasizes differing errors and confidence. Used in: complementarity
18. Noy & Zhang (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science. doi:10.1126/science.adh2586
    A preregistered experiment: access to a chat model reduced time and raised quality on professional writing tasks, helping weaker writers most. Used in: complementarity
19. Vaccaro, Almaatouq & Malone (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour. doi:10.1038/s41562-024-02024-1
    A meta-analysis of human–AI combination experiments: on average the combination did worse than the better partner alone, with losses in decision tasks and gains in creation tasks. The key current reference. Used in: complementarity  automation bias
20. Costello, Pennycook & Rand (2024). Durably reducing conspiracy beliefs through dialogues with AI. Science. doi:10.1126/science.adq1814
    Brief dialogues with a language model reduced conspiracy beliefs, with effects lasting months. A case of AI doing persuasive work people find hard. Used in: complementarity
21. Brynjolfsson, Li & Raymond (2025). Generative AI at Work. The Quarterly Journal of Economics. doi:10.1093/qje/qjae044
    Field evidence from customer support: an AI assistant raised productivity, most for novice and less-skilled workers. A large, careful workplace study. Used in: complementarity

see also

	research areas — where these papers are put to work
	annotated: metascience — how much of any of this will replicate
	mirror — from the reading room: pages that answer