Qualitative Analysis for Human Centered AI

TL;DR

This paper advocates for the integration of qualitative analysis in human-centered AI development, highlighting its role in data selection, model design, participatory approaches, and policy making to create socially aware AI systems.

Contribution

It introduces qualitative analysis as a valuable supplement to AI development, emphasizing its potential to enhance human-centered and participatory AI paradigms.

Findings

QA can assist in data and model selection.

QA supports participatory design and policy making.

QA reveals understudied issues in AI systems.

Abstract

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.