# We Urgently Need Intrinsically Kind Machines

arXiv: https://arxiv.org/abs/2411.04126 · HTML: https://mlsanity.com/paper/2411.04126/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals](https://mlsanity.com/paper/2210.01790/) (2022)
- [Learning Altruistic Behaviours in Reinforcement Learning without External Rewards](https://mlsanity.com/paper/2107.09598/) (2021)

## Abstract (arXiv)

Artificial Intelligence systems are rapidly evolving, integrating extrinsic and intrinsic motivations. While these frameworks offer benefits, they risk misalignment at the algorithmic level while appearing superficially aligned with human values. In this paper, we argue that an intrinsic motivation for kindness is crucial for making sure these models are intrinsically aligned with human values. We argue that kindness, defined as a form of altruism motivated to maximize the reward of others, can counteract any intrinsic motivations that might lead the model to prioritize itself over human well-being. Our approach introduces a framework and algorithm for embedding kindness into foundation models by simulating conversations. Limitations and future research directions for scalable implementation are discussed.