Collaborative Intelligence in Sequential Experiments: A Human-in-the-Loop Framework for Drug Discovery
References
- (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.Link, Google Scholar
- (2018) Feature-based and string-based models for predicting RNA-protein interaction. Molecules 23(3):697.Crossref, Google Scholar
- (2014) Power to the people: The role of humans in interactive machine learning. AI Magazine 35(4):105–120.Crossref, Google Scholar
- (2022) Improving human-algorithm collaboration: Causes and mitigation of over-and under-adherence. Preprint, submitted December 21, https://doi.org/10.2139/ssrn.4298669.Google Scholar
- (2017) Comparing visual-interactive labeling with active learning: An experimental study. IEEE Trans. Visualization Comput. Graphics 24(1):298–308.Crossref, Google Scholar
- (2018) Towards user-centered active learning algorithms. Comput. Graphics Forum 37(3):121–132.Crossref, Google Scholar
- (2024) Human and machine: The impact of machine input on decision making under cognitive limitations. Management Sci. 70(2):1258–1275.Link, Google Scholar
- (2020) Transfer learning for drug discovery. J. Medicinal Chemistry 63(16):8683–8694.Crossref, Google Scholar
- (2024) The philosopher’s stone for science—The catalyst change of AI for scientific creativity. Proc. Pacific Asia Conf. Inform. Systems (PACIS 2024) (Association for Information Systems, Atlanta), 1770.Google Scholar
- (2021) Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nature Biomedical Engrg. 5(6):613–623.Crossref, Google Scholar
- (2019) Hybrid intelligence. Bus. Inform. Systems Engrg. 61(5):637–643.Crossref, Google Scholar
- (2018) A conceptual model of team dynamical behaviors and performance in human-autonomy teaming. Cognitive Systems Res. 52:497–507.Crossref, Google Scholar
- (2017) Team situation awareness within the context of human-autonomy teaming. Cognitive Systems Res. 46:3–12.Crossref, Google Scholar
- (2017) Active learning with multiple localized regression models. INFORMS J. Comput. 29(3):503–522.Link, Google Scholar
- (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64(3):1155–1170.Link, Google Scholar
- (2022) Human-algorithm collaboration: Achieving complementarity and avoiding unfairness. Proc. 2022 ACM Conf. Fairness Accountability Transparency (ACM, New York), 1639–1656. Google Scholar
- (2012) Digital science and knowledge boundaries in complex innovation. Organ. Sci. 23(5):1467–1484.Link, Google Scholar
- (2000) Drug discovery: A historical perspective. Science 287(5460):1960–1964.Crossref, Google Scholar
- (2018) A review of user interface design for interactive machine learning. ACM Trans. Interactive Intelligent Systems 8(2):1–37.Crossref, Google Scholar
- (2019) Exploiting machine learning for end-to-end drug discovery and development. Nature Materials 18(5):435–441.Crossref, Google Scholar
- (1984) Heuristic and analytic processes in reasoning. British J. Psych. 75(4):451–468.Crossref, Google Scholar
- (2011) Metaknowledge. Science 331(6018):721–725.Crossref, Google Scholar
- (2013) Dual-process theories of higher cognition: Advancing the debate. Perspect. Psych. Sci. 8(3):223–241.Crossref, Google Scholar
- (2021) Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Quart. 45(3):1527–1556.Crossref, Google Scholar
- (2022) Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Inform. Systems Res. 33(2):678–696.Link, Google Scholar
- (2023) A survey of uncertainty in deep neural networks. Artificial Intelligence Rev. 56:1513–1589.Crossref, Google Scholar
- (2021) Human–robot interaction: When investors adjust the usage of robo-advisors in peer-to-peer lending. Inform. Systems Res. 32(3):774–785.Link, Google Scholar
- (2021) Who is a better decision maker? Data-driven expert ranking under unobserved quality. Production Oper. Management 30(1):127–144.Crossref, Google Scholar
- (2021) Explainable active learning (XAL) toward AI explanations as interfaces for machine teachers. Proc. ACM Human Comput. Interaction, vol. 4 (ACM, New York), 1–28.Crossref, Google Scholar
- (2011) Practical variational inference for neural networks. Adv. Neural Inform. Processing Systems, vol. 24 (Curran Associates Inc., Red Hook, NY), 2348–2356.Google Scholar
- (2024) An explainable artificial intelligence approach using graph learning to predict intensive care unit length of stay. Inform. Systems Res. 36(3):1478–1501.Google Scholar
- (2021) Calibration of voting-based helpfulness measurement for online reviews: An iterative Bayesian probability approach. INFORMS J. Comput. 33(1):246–261.Link, Google Scholar
- (2019) Agency plus automation: Designing artificial intelligence into interactive systems. Proc. Natl. Acad. Sci. USA 116(6):1844–1850.Crossref, Google Scholar
- (2002) Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Machine Intelligence 24(3):289–300.Crossref, Google Scholar
- (2005) Joint Cognitive Systems: Foundations of Cognitive Systems Engineering (CRC Press, Boca Raton).Crossref, Google Scholar
- (2016) Interactive machine learning for health informatics: When do we need the human-in-the-loop? Brain Inform. 3(2):119–131.Crossref, Google Scholar
- (2021) Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learn. 110(3):457–506.Crossref, Google Scholar
- (2021) Eliciting human judgment for prediction algorithms. Management Sci. 67(4):2314–2325.Link, Google Scholar
- (2021) Editorial for the special section on humans, algorithms, and augmented intelligence: The future of work, organizations, and society. Inform. Systems Res. 32(3):675–687.Link, Google Scholar
- (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589.Crossref, Google Scholar
- (2021) Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Inform. Systems Res. 32(3):713–735.Link, Google Scholar
- (2011) Thinking, Fast and Slow (Macmillan, New York).Google Scholar
- (2021) When will workers follow an algorithm? A field experiment with a retail business. Management Sci. 67(3):1670–1695.Link, Google Scholar
- (2022) Active learning for human-in-the-loop customs inspection. IEEE Trans. Knowledge Data Engrg. 35(12):12039–12052.Crossref, Google Scholar
- (2024) Timely, granular, and actionable: Designing a social listening platform for public health 3.0. MIS Quart. 48(3):899–930.Crossref, Google Scholar
- (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Adv. Neural Inform. Processing Systems, vol. 30 (Curran Associates Inc., Red Hook, NY), 6405–6416.Google Scholar
- (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2019) Diagnostic doubt and artificial intelligence: An inductive field study of radiology work. ICIS 2019 Proc. (New York University, New York), 11.Google Scholar
- (2021) Is AI ground truth really true? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quart. 45(3):1501–1526.Crossref, Google Scholar
- (2024) Explainable deep learning for false information identification: An argumentation theory approach. Inform. Systems Res. 35(2):890–907.Link, Google Scholar
- (1995) A sequential algorithm for training text classifiers: Corrigendum and additional data. ACM SIGIR Forum 29(2):13–19.Crossref, Google Scholar
- (2022) When more data lead us astray: Active data acquisition in the presence of label bias. Proc. AAAI Conf. Human Comput. Crowdsourcing, 133–146.Google Scholar
- (2020) TheoryOn: A design framework and system for unlocking behavioral knowledge through ontology learning. MIS Quart. 44(4):1733–1772.Crossref, Google Scholar
- (2018) A knowledge gradient policy for sequencing experiments to identify the structure of RNA molecules using a sparse additive belief model. INFORMS J. Comput. 30(4):750–767.Link, Google Scholar
- (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637):1123–1130.Crossref, Google Scholar
- (2024) Position: A call to action for a human-centered AutoML paradigm. Proc. 41st Internat. Conf. Machine Learn., vol. 235 (PMLR, New York), 30566–30584.Google Scholar
- (2025) Automating in high-expertise, low-label environments: Evidence-based medicine by expert-augmented few-shot learning. MIS Quart. 49(3):1049–1094.Crossref, Google Scholar
- (2019) How complex is your classification problem? A survey on measuring classification complexity. ACM Comput. Surveys 52(5):1–34.Crossref, Google Scholar
- (2021) AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms. MIS Quart. 45(3):1451–1482.Crossref, Google Scholar
- (2025) 1+ 1 > 2? Information, humans, and machines. Inform. Systems Res. 36(1):394–418.Link, Google Scholar
- (2018) Architectural framework for exploring adaptive human-machine teaming options in simulated dynamic environments. Systems 6(4):44.Crossref, Google Scholar
- (2020) Design considerations for real-time collaboration with creative artificial intelligence. Organised Sound 25(1):41–52.Crossref, Google Scholar
- (2014) Decisions about knowledge in medical practice: The effect of temporal features of a task. Amer. J. Sociol. 120(3):701–749.Crossref, Google Scholar
- (2018) Integrating knowledge in the face of epistemic uncertainty: Dialogically drawing distinctions. Management Learn. 49(5):595–612.Crossref, Google Scholar
- (2023) Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Rev. 56(4):3005–3054.Crossref, Google Scholar
- (2017) The media inequality: Comparing the initial human-human and human-AI social interactions. Comput. Human Behav. 72:432–440.Crossref, Google Scholar
- (2021) Advances in de novo drug design: From conventional to machine learning methods. Internat. J. Molecular Sci. 22(4):1676.Crossref, Google Scholar
- (2024) Human-in-the-loop active learning for goal-oriented molecule generation. J. Cheminformatics 16(1):1–24.Crossref, Google Scholar
- (2022) Dynamic programming for response-adaptive dose-finding clinical trials. INFORMS J. Comput. 34(2):1176–1190.Link, Google Scholar
- (2011) The knowledge-gradient algorithm for sequencing experiments in drug discovery. INFORMS J. Comput. 23(3):346–363.Link, Google Scholar
- (2005) The pharmaceutical sector, Chapter 2. The Business of Healthcare Innovation (Cambridge University Press, Cambridge, UK), 27–102Crossref, Google Scholar
- (2022) Machine learning in information systems research. MIS Quart. 46(1):iii–xix.Google Scholar
- (2022) AI assistants: A framework for semi-automated data wrangling. IEEE Trans. Knowledge Data Engrg. 35(9):9295–9306.Crossref, Google Scholar
- (2024) From explainable to interactive AI: A literature review on current trends in human-AI interaction. Internat. J. Human Comput. Stud. 189:103301.Crossref, Google Scholar
- (2024) Direct preference optimization: Your language model is secretly a reward model. Adv. Neural Inform. Processing Systems, vol. 36 (Curran Associates Inc., Red Hook, NY), 53728–53741.Google Scholar
- (2017) Editor’s comments: Diversity of design science research.Google Scholar
- (2011) The Evolution of Drug Discovery: From Traditional Medicines to Modern Drugs (John Wiley & Sons, Hoboken, NJ).Google Scholar
- (2020) Machine learning applications in drug development. Comput. Structural Biotechnology J. 18:241–252.Crossref, Google Scholar
- (2015) Active-learning strategies in computer-assisted drug discovery. Drug Discovery Today 20(4):458–465.Crossref, Google Scholar
- (2019) Human-centered artificial intelligence and machine learning. Human Behav. Emerging Tech. 1(1):33–36.Crossref, Google Scholar
- (2020) Breaking Eroom’s law. Nature Rev. Drug Discovery 19(12):833–835.Crossref, Google Scholar
- (2007) Decision-centric active learning of binary-outcome models. Inform. Systems Res. 18(1):4–22.Link, Google Scholar
- (2009) Active feature-value acquisition. Management Sci. 55(4):664–684.Link, Google Scholar
- (2021) Estimating the impact of “humanizing” customer service chatbots. Inform. Systems Res. 32(3):736–751.Link, Google Scholar
- (2018) Automating drug discovery. Nature Rev. Drug Discovery 17(2):97–113.Crossref, Google Scholar
- (2009) Active Learning Literature Survey.Google Scholar
- (2024) Misinformation and algorithmic bias, Chapter 2. Artificial Misinformation: Exploring Human-Algorithm Interaction Online (Springer, Berlin), 15–47.Crossref, Google Scholar
- (2017) Why human-autonomy teaming? Internat. Conf. Appl. Human Factors Ergonomics (Springer, New York), 3–11.Google Scholar
- (2008) Incorporating domain knowledge into data mining classifiers: An application in indirect lending. Decision Support Systems 46(1):287–299.Crossref, Google Scholar
- (2011) Contextual bandits with similarity information. Proc. 24th Annual Conf. Learn. Theory (PMLR, Cambridge, MA), 679–702.Google Scholar
- (2021) Coordinating human and machine learning for effective organizational learning. MIS Quart. 45(3):1581–1602.Crossref, Google Scholar
- (2022) Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations. Management Sci. 68(2):846–865.Link, Google Scholar
- (2022) Human-in-the-loop assisted de novo molecular design. J. Cheminformatics 14(1):86.Crossref, Google Scholar
- (2022) Autonomous drug design with multi-armed bandits. 2022 IEEE Internat. Conf. Big Data (IEEE Computer Society, Washington, DC), 5584–5592.Google Scholar
- (2023) Reciprocal human-machine learning: A theory and an instantiation for the case of message classification. Management Sci., ePub ahead of print November 14, https://doi.org/10.1287/mnsc.2022.03518.Link, Google Scholar
- (2019) Applications of machine learning in drug discovery and development. Nature Rev. Drug Discovery 18(6):463–477.Crossref, Google Scholar
- (2021) When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quart. 45(3):1557–1580.Crossref, Google Scholar
- (2017) Cost-effective quality assurance in crowd labeling. Inform. Systems Res. 28(1):137–158.Link, Google Scholar
- (2023) Human-AI co-creation in product ideation: The dual view of quality and diversity. Preprint, submitted December 20, https://doi.org/10.2139/ssrn.4668241.Google Scholar
- (2023) Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI. Internat. J. Human Comput. Interaction 39(3):494–518.Crossref, Google Scholar
- (2021) Learning from crowdsourced multi-labeling: A variational Bayesian approach. Inform. Systems Res. 32(3):752–773.Abstract, Google Scholar
- (2017) Human-in-the-loop optimization of exoskeleton assistance during walking. Science 356(6344):1280–1284.Crossref, Google Scholar
- (2006) Selectively acquiring customer information: A new data acquisition problem and an active learning-based solution. Management Sci. 52(5):697–712.Link, Google Scholar
- (2017) Hybrid-augmented intelligence: Collaboration and cognition. Frontiers Inform. Tech. Electronic Engrg. 18(2):153–179.Crossref, Google Scholar
- (2019) How do tumor cytogenetics inform cancer treatments? Dynamic risk stratification and precision medicine using multi-armed bandits. Preprint, submitted July 12, https://doi.org/10.2139/ssrn.3405082.Google Scholar
- (2023) Spoiled for choice? Personalized recommendation for healthcare decisions: A multiarmed bandit approach. Inform. Systems Res. 34(4):1493–1512.Link, Google Scholar
- (2019) Machine learning-powered antibiotics phenotypic drug discovery. Sci. Rep. 9(1):5013.Crossref, Google Scholar

