# Qualitative Analysis for Human Centered AI

arXiv: https://arxiv.org/abs/2112.03784 · HTML: https://mlsanity.com/paper/2112.03784/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2021.

## Built on by (most cited first, 4 of 4)

- [Levels of AGI for Operationalizing Progress on the Path to AGI](https://mlsanity.com/paper/2311.02462/) (2023)
- [Reflections on Inductive Thematic Saturation as a potential metric for measuring the validity of an inductive Thematic Analysis with LLMs](https://mlsanity.com/paper/2401.03239/) (2024)
- [Concerns on Bias in Large Language Models when Creating Synthetic Personae](https://mlsanity.com/paper/2405.05080/) (2024)
- [Needs and Artificial Intelligence](https://mlsanity.com/paper/2203.03715/) (2022)

## Abstract (arXiv)

Human-centered artificial intelligence (AI) posits that machine learning and AI should be developed and applied in a socially aware way. In this article, we argue that qualitative analysis (QA) can be a valuable tool in this process, supplementing, informing, and extending the possibilities of AI models. We show this by describing how QA can be integrated in the current prediction paradigm of AI, assisting scientists in the process of selecting data, variables, and model architectures. Furthermore, we argue that QA can be a part of novel paradigms towards Human Centered AI. QA can support scientists and practitioners in practical problem solving and situated model development. It can also promote participatory design approaches, reveal understudied and emerging issues in AI systems, and assist policy making.