Why AI Is WEIRD and Should Not Be This Way: Towards AI For Everyone, With Everyone, By Everyone

TL;DR

This paper advocates for inclusive AI development that involves diverse data, evaluation, and developers to address biases and limitations inherent in current WEIRD-centric AI systems, aiming for equitable AI for all.

Contribution

It proposes a comprehensive framework emphasizing diversity in data, evaluation, and development teams to make AI systems more inclusive and representative.

Findings

Current AI systems suffer from lack of data diversity and biases.

Inclusive development can reduce biases and improve fairness.

Diverse stakeholder involvement enhances AI relevance and equity.

Abstract

This paper presents a vision for creating AI systems that are inclusive at every stage of development, from data collection to model design and evaluation. We address key limitations in the current AI pipeline and its WEIRD representation, such as lack of data diversity, biases in model performance, and narrow evaluation metrics. We also focus on the need for diverse representation among the developers of these systems, as well as incentives that are not skewed toward certain groups. We highlight opportunities to develop AI systems that are for everyone (with diverse stakeholders in mind), with everyone (inclusive of diverse data and annotators), and by everyone (designed and developed by a globally diverse workforce).