# Hybrid Intelligence arXiv: https://arxiv.org/abs/2105.00691 ยท HTML: https://mlsanity.com/paper/2105.00691/ ## Key facts - Cited by 55 papers in the map (data through 14 Oct 2026). - Builds on 2 of its 33 references that are in the map. - First posted on arXiv 3 May 2021. ## Builds on (most cited first, 2 of 2) - [Building machines that adapt and compute like brains](https://mlsanity.com/paper/1711.04203/) (2017) - [Machine Teaching: A New Paradigm for Building Machine Learning Systems](https://mlsanity.com/paper/1707.06742/) (2017) ## Built on by (most cited first, 30 of 55) - [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023) - [Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles](https://mlsanity.com/paper/2210.16955/) (2022) - [Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI](https://mlsanity.com/paper/2105.05424/) (2021) - [Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence](https://mlsanity.com/paper/2404.00029/) (2024) - [Learning to Defer to a Population: A Meta-Learning Approach](https://mlsanity.com/paper/2403.02683/) (2024) - [On the Influence of Explainable AI on Automation Bias](https://mlsanity.com/paper/2204.08859/) (2022) - [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021) - [Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda](https://mlsanity.com/paper/2507.21928/) (2025) - [Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints](https://mlsanity.com/paper/2403.06906/) (2024) - [AI-enhanced Collective Intelligence](https://mlsanity.com/paper/2403.10433/) (2024) - [Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice](https://mlsanity.com/paper/2310.02108/) (2023) - [Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation](https://mlsanity.com/paper/2404.12056/) (2024) - [Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer](https://mlsanity.com/paper/2207.00497/) (2022) - [Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond](https://mlsanity.com/paper/2309.00064/) (2023) - [Factors that influence the adoption of human-AI collaboration in clinical decision-making](https://mlsanity.com/paper/2204.09082/) (2022) - [Learning to Partially Defer for Sequences](https://mlsanity.com/paper/2502.01459/) (2025) - [Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft](https://mlsanity.com/paper/2112.03482/) (2021) - [Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming](https://mlsanity.com/paper/2308.16785/) (2023) - [FiFAR: A Fraud Detection Dataset for Learning to Defer](https://mlsanity.com/paper/2312.13218/) (2023) - [Transferring Domain Knowledge with (X)AI-Based Learning Systems](https://mlsanity.com/paper/2406.01329/) (2024) - [Towards properly implementing Theory of Mind in AI systems: An account of four misconceptions](https://mlsanity.com/paper/2503.16468/) (2025) - [Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights](https://mlsanity.com/paper/2505.18385/) (2025) - [Seeing Eye to Eye: Enabling Cognitive Alignment Through Shared First-Person Perspective in Human-AI Collaboration](https://mlsanity.com/paper/2603.12701/) (2026) - [Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning](https://mlsanity.com/paper/2207.06835/) (2022) - [Facilitating Opinion Diversity through Hybrid NLP Approaches](https://mlsanity.com/paper/2405.09439/) (2024) - [Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems](https://mlsanity.com/paper/2506.05370/) (2025) - [A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data](https://mlsanity.com/paper/2506.06083/) (2025) - [No Need for Learning to Defer? A Training Free Deferral Framework to Multiple Experts through Conformal Prediction](https://mlsanity.com/paper/2509.12573/) (2025) - [The Epistemological Consequences of Large Language Models: Rethinking collective intelligence and institutional knowledge](https://mlsanity.com/paper/2512.19570/) (2025) - [Rethinking and Recomputing the Value of Machine Learning Models](https://mlsanity.com/paper/2209.15157/) (2022) ## Abstract (arXiv) Research has a long history of discussing what is superior in predicting certain outcomes: statistical methods or the human brain. This debate has repeatedly been sparked off by the remarkable technological advances in the field of artificial intelligence (AI), such as solving tasks like object and speech recognition, achieving significant improvements in accuracy through deep-learning algorithms (Goodfellow et al. 2016), or combining various methods of computational intelligence, such as fuzzy logic, genetic algorithms, and case-based reasoning (Medsker 2012). One of the implicit promises that underlie these advancements is that machines will 1 day be capable of performing complex tasks or may even supersede humans in performing these tasks. This triggers new heated debates of when machines will ultimately replace humans (McAfee and Brynjolfsson 2017). While previous research has proved that AI performs well in some clearly defined tasks such as playing chess, playing Go or identifying objects on images, it is doubted that the development of an artificial general intelligence (AGI) which is able to solve multiple tasks at the same time can be achieved in the near future (e.g., Russell and Norvig 2016). Moreover, the use of AI to solve complex business problems in organizational contexts occurs scarcely, and applications for AI that solve complex problems remain mainly in laboratory settings instead of being implemented in practice. Since the road to AGI is still a long one, we argue that the most likely paradigm for the division of labor between humans and machines in the next decades is Hybrid Intelligence. This concept aims at using the complementary strengths of human intelligence and AI, so that they can perform better than each of the two could separately (e.g., Kamar 2016).