# Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning

arXiv: https://arxiv.org/abs/2608.12372 · HTML: https://mlsanity.com/paper/2608.12372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 46 of its 123 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 30 of 46)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Deliberative Alignment: Reasoning Enables Safer Language Models](https://mlsanity.com/paper/2412.16339/) (2024)
- [Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety](https://mlsanity.com/paper/2507.11473/) (2025)
- [Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters](https://mlsanity.com/paper/2212.10001/) (2022)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [Algorithms to estimate Shapley value feature attributions](https://mlsanity.com/paper/2207.07605/) (2022)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [The Off-Switch Game](https://mlsanity.com/paper/1611.08219/) (2016)
- [Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning](https://mlsanity.com/paper/1902.04783/) (2019)
- [A Voting-Based System for Ethical Decision Making](https://mlsanity.com/paper/1709.06692/) (2017)
- [The Who in XAI: How AI Background Shapes Perceptions of AI Explanations](https://mlsanity.com/paper/2107.13509/) (2021)
- [Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives](https://mlsanity.com/paper/2411.04991/) (2024)
- [Adapting a Kidney Exchange Algorithm to Align with Human Values](https://mlsanity.com/paper/2005.09755/) (2020)
- [Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction](https://mlsanity.com/paper/1802.09548/) (2018)
- [Conservative Agency via Attainable Utility Preservation](https://mlsanity.com/paper/1902.09725/) (2019)
- [A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective](https://mlsanity.com/paper/2304.08795/) (2023)
- [Cognitive Model Priors for Predicting Human Decisions](https://mlsanity.com/paper/1905.09397/) (2019)
- [Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values](https://mlsanity.com/paper/2206.15465/) (2022)
- [From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning](https://mlsanity.com/paper/2505.17117/) (2025)
- [Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses](https://mlsanity.com/paper/2406.05659/) (2024)
- [Large Language Models Do Not Simulate Human Psychology](https://mlsanity.com/paper/2508.06950/) (2025)
- [Capturing the Complexity of Human Strategic Decision-Making with Machine Learning](https://mlsanity.com/paper/2408.07865/) (2024)
- [A Computational Model of Commonsense Moral Decision Making](https://mlsanity.com/paper/1801.04346/) (2018)
- [C3AI: Crafting and Evaluating Constitutions for Constitutional AI](https://mlsanity.com/paper/2502.15861/) (2025)

## Abstract (arXiv)

AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers. This position paper argues that in many settings, particularly high-stakes decision-making, we need accurate cognitively-aligned AI systems that reason similarly to their users, and faithfully communicate their reasoning. We review evidence that cognitive alignment improves understandability and trustworthiness, and provide new survey data showing that many users find cognitive alignment "essential" when an AI's rationale for a judgment or action is important to them. We outline the gaps between existing alignment methods and what is needed to achieve cognitive alignment, and present a research agenda to address these gaps. We argue that cognitive misalignment represents a likely impediment to AI adoption in many envisioned applications, and that addressing it is important for creating AI systems on which users are both willing and justified to rely.