# Human-AI Symbiosis: A Survey of Current Approaches

arXiv: https://arxiv.org/abs/2103.09990 · HTML: https://mlsanity.com/paper/2103.09990/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure](https://mlsanity.com/paper/1809.07424/) (2018)
- [Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior](https://mlsanity.com/paper/1811.09722/) (2018)
- [Plan Explicability and Predictability for Robot Task Planning](https://mlsanity.com/paper/1511.08158/) (2015)
- [Investigating Human + Machine Complementarity for Recidivism Predictions](https://mlsanity.com/paper/1808.09123/) (2018)
- [On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems](https://mlsanity.com/paper/1611.08309/) (2016)
- [A Unified Framework for Planning in Adversarial and Cooperative Environments](https://mlsanity.com/paper/1802.06137/) (2018)
- [Expressing Robot Incapability](https://mlsanity.com/paper/1810.08167/) (2018)
- [Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility](https://mlsanity.com/paper/1905.10672/) (2019)
- [Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork](https://mlsanity.com/paper/2004.13102/) (2020)
- [Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning](https://mlsanity.com/paper/1903.07269/) (2019)
- [Balancing Explicability and Explanation in Human-Aware Planning](https://mlsanity.com/paper/1708.00543/) (2017)
- [Nonverbal Robot Feedback for Human Teachers](https://mlsanity.com/paper/1911.02320/) (2019)
- [Model Elicitation through Direct Questioning](https://mlsanity.com/paper/2011.12262/) (2020)

## Built on by (most cited first, 8 of 8)

- [LEGO-Prover: Neural Theorem Proving with Growing Libraries](https://mlsanity.com/paper/2310.00656/) (2023)
- [Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration](https://mlsanity.com/paper/2109.14591/) (2021)
- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming](https://mlsanity.com/paper/2308.16785/) (2023)
- [Modeling Human Behavior Part I -- Learning and Belief Approaches](https://mlsanity.com/paper/2205.06485/) (2022)
- [Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance](https://mlsanity.com/paper/2308.05619/) (2023)
- [The Morality of Probability: How Implicit Moral Biases in LLMs May Shape the Future of Human-AI Symbiosis](https://mlsanity.com/paper/2509.10297/) (2025)
- [Expansion of situations theory for exploring shared awareness in human-intelligent autonomous systems](https://mlsanity.com/paper/2406.04956/) (2024)

## Abstract (arXiv)

In this paper, we aim at providing a comprehensive outline of the different threads of work in human-AI collaboration. By highlighting various aspects of works on the human-AI team such as the flow of complementing, task horizon, model representation, knowledge level, and teaming goal, we make a taxonomy of recent works according to these dimensions. We hope that the survey will provide a more clear connection between the works in the human-AI team and guidance to new researchers in this area.