# Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration

arXiv: https://arxiv.org/abs/1801.09854 · HTML: https://mlsanity.com/paper/1801.09854/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Task](https://mlsanity.com/paper/2410.11860/) (2024)
- [A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems](https://mlsanity.com/paper/2104.11600/) (2021)
- [Toward Human-AI Alignment in Large-Scale Multi-Player Games](https://mlsanity.com/paper/2402.03575/) (2024)
- [Trustworthy Human Computation: A Survey](https://mlsanity.com/paper/2210.12324/) (2022)
- [Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer Agents](https://mlsanity.com/paper/2601.13479/) (2026)

## Abstract (arXiv)

Effective collaboration between humans and AI-based systems requires effective modeling of the human in the loop, both in terms of the mental state as well as the physical capabilities of the latter. However, these models can also open up pathways for manipulating and exploiting the human in the hopes of achieving some greater good, especially when the intent or values of the AI and the human are not aligned or when they have an asymmetrical relationship with respect to knowledge or computation power. In fact, such behavior does not necessarily require any malicious intent but can rather be borne out of cooperative scenarios. It is also beyond simple misinterpretation of intents, as in the case of value alignment problems, and thus can be effectively engineered if desired. Such techniques already exist and pose several unresolved ethical and moral questions with regards to the design of autonomy. In this paper, we illustrate some of these issues in a teaming scenario and investigate how they are perceived by participants in a thought experiment.