| Sparks of artificial general intelligence: Early experiments with gpt-4 S Bubeck, V Chadrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... ArXiv, 2023 | 3773 | 2023 |
| Machine behaviour I Rahwan, M Cebrian, N Obradovich, J Bongard, JF Bonnefon, C Breazeal, ... Nature 568 (7753), 477-486, 2019 | 1257 | 2019 |
| Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence P Stone, R Brooks, E Brynjolfsson, R Calo, O Etzioni, G Hager, ... arXiv preprint arXiv:2211.06318, 2022 | 1188 | 2022 |
| Software engineering for machine learning: A case study S Amershi, A Begel, C Bird, R DeLine, H Gall, E Kamar, N Nagappan, ... 2019 IEEE/ACM 41st International Conference on Software Engineering …, 2019 | 1055 | 2019 |
| Does the whole exceed its parts? the effect of ai explanations on complementary team performance G Bansal, T Wu, J Zhou, R Fok, B Nushi, E Kamar, MT Ribeiro, D Weld Proceedings of the 2021 CHI conference on human factors in computing systems …, 2021 | 669 | 2021 |
| Combining human and machine intelligence in large-scale crowdsourcing. E Kamar, S Hacker, E Horvitz Aamas 12, 467-474, 2012 | 529 | 2012 |
| Beyond accuracy: The role of mental models in human-AI team performance G Bansal, B Nushi, E Kamar, WS Lasecki, DS Weld, E Horvitz Proceedings of the AAAI conference on human computation and crowdsourcing 7 …, 2019 | 520 | 2019 |
| Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection T Hartvigsen, S Gabriel, H Palangi, M Sap, D Ray, E Kamar arXiv preprint arXiv:2203.09509, 2022 | 418 | 2022 |
| Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff G Bansal, B Nushi, E Kamar, DS Weld, WS Lasecki, E Horvitz Proceedings of the AAAI conference on artificial intelligence 33 (01), 2429-2437, 2019 | 360 | 2019 |
| Interpretable & explorable approximations of black box models H Lakkaraju, E Kamar, R Caruana, J Leskovec arXiv preprint arXiv:1707.01154, 2017 | 360 | 2017 |
| Faithful and customizable explanations of black box models H Lakkaraju, E Kamar, R Caruana, J Leskovec Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 131-138, 2019 | 342 | 2019 |
| Revolt: Collaborative crowdsourcing for labeling machine learning datasets JC Chang, S Amershi, E Kamar Proceedings of the 2017 CHI conference on human factors in computing systems …, 2017 | 332 | 2017 |
| Software engineering for machine learning: a case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) S Amershi, A Begel, C Bird, R DeLine, H Gall, E Kamar, N Nagappan, ... IEEE, 2019 | 271 | 2019 |
| Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence. E Kamar IJCAI, 4070-4073, 2016 | 262 | 2016 |
| Collaboration and Shared Plans in the Open World: Studies of Ridesharing. E Kamar, E Horvitz IJCAI 9, 187, 2009 | 207 | 2009 |
| Identifying unknown unknowns in the open world: Representations and policies for guided exploration H Lakkaraju, E Kamar, R Caruana, E Horvitz Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 206 | 2017 |
| Learning to complement humans B Wilder, E Horvitz, E Kamar arXiv preprint arXiv:2005.00582, 2020 | 197 | 2020 |
| Volunteering versus work for pay: Incentives and tradeoffs in crowdsourcing A Mao, E Kamar, Y Chen, E Horvitz, M Schwamb, C Lintott, A Smith Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 …, 2013 | 180 | 2013 |
| Is the most accurate ai the best teammate? optimizing ai for teamwork G Bansal, B Nushi, E Kamar, E Horvitz, DS Weld Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 11405 …, 2021 | 179 | 2021 |
| Towards accountable ai: Hybrid human-machine analyses for characterizing system failure B Nushi, E Kamar, E Horvitz Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 6 …, 2018 | 166 | 2018 |
