# Human-Centered Artificial Intelligence and Machine Learning

arXiv: https://arxiv.org/abs/1901.11184 · HTML: https://mlsanity.com/paper/1901.11184/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 30 of 37)

- [BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation](https://mlsanity.com/paper/2403.09227/) (2024)
- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration](https://mlsanity.com/paper/2109.14591/) (2021)
- [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)
- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [Mechanistic?](https://mlsanity.com/paper/2410.09087/) (2024)
- [Rationalization for Explainable NLP: A Survey](https://mlsanity.com/paper/2301.08912/) (2023)
- [HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation](https://mlsanity.com/paper/2505.11454/) (2025)
- [XAIR: A Framework of Explainable AI in Augmented Reality](https://mlsanity.com/paper/2303.16292/) (2023)
- [Mutual Theory of Mind for Human-AI Communication](https://mlsanity.com/paper/2210.03842/) (2022)
- [Towards More Human-like AI Communication: A Review of Emergent Communication Research](https://mlsanity.com/paper/2308.02541/) (2023)
- [KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems](https://mlsanity.com/paper/2209.02886/) (2022)
- [Users are the North Star for AI Transparency](https://mlsanity.com/paper/2303.05500/) (2023)
- [Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces](https://mlsanity.com/paper/2306.10606/) (2023)
- [AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals](https://mlsanity.com/paper/2412.15444/) (2024)
- [Combining Transformers with Natural Language Explanations](https://mlsanity.com/paper/2110.00125/) (2021)
- [Uncertainty Quantification for Local Model Explanations Without Model Access](https://mlsanity.com/paper/2301.05761/) (2023)
- [A Review of Cybersecurity Incidents in the Food and Agriculture Sector](https://mlsanity.com/paper/2403.08036/) (2024)
- [Students' Perceived Roles, Opportunities, and Challenges of a Generative AI-powered Teachable Agent: A Case of Middle School Math Class](https://mlsanity.com/paper/2409.06721/) (2024)
- [Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Study](https://mlsanity.com/paper/2504.19848/) (2025)
- [Comparing Human and AI Rater Effects Using the Many-Facet Rasch Model](https://mlsanity.com/paper/2505.18486/) (2025)
- [Can Information Behaviour Inform Machine Learning?](https://mlsanity.com/paper/2205.00538/) (2022)
- [What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps](https://mlsanity.com/paper/2210.03506/) (2022)
- [UX Heuristics and Checklist for Deep Learning powered Mobile Applications with Image Classification](https://mlsanity.com/paper/2307.05513/) (2023)
- [Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations](https://mlsanity.com/paper/2402.07933/) (2024)
- [Beyond Following: Mixing Active Initiative into Computational Creativity](https://mlsanity.com/paper/2409.16291/) (2024)

## Abstract (arXiv)

Humans are increasingly coming into contact with artificial intelligence and machine learning systems. Human-centered artificial intelligence is a perspective on AI and ML that algorithms must be designed with awareness that they are part of a larger system consisting of humans. We lay forth an argument that human-centered artificial intelligence can be broken down into two aspects: (1) AI systems that understand humans from a sociocultural perspective, and (2) AI systems that help humans understand them. We further argue that issues of social responsibility such as fairness, accountability, interpretability, and transparency.