Democratizing AI: Non-expert design of prediction tasks

TL;DR

This paper explores how non-experts can independently design prediction tasks across various domains, and demonstrates that with proper guidance, they can contribute valuable data for training effective predictive models, expanding the democratization of AI.

Contribution

It introduces a method for non-experts to create prediction tasks and collect data, showing that useful datasets and models can be developed without prior ML experience.

Findings

Non-experts can design diverse, useful prediction tasks across multiple domains.

Predictive models trained on crowdsourced data perform reasonably well.

Instruction clarity significantly influences the types of tasks proposed.

Abstract

Non-experts have long made important contributions to machine learning (ML) by contributing training data, and recent work has shown that non-experts can also help with feature engineering by suggesting novel predictive features. However, non-experts have only contributed features to prediction tasks already posed by experienced ML practitioners. Here we study how non-experts can design prediction tasks themselves, what types of tasks non-experts will design, and whether predictive models can be automatically trained on data sourced for their tasks. We use a crowdsourcing platform where non-experts design predictive tasks that are then categorized and ranked by the crowd. Crowdsourced data are collected for top-ranked tasks and predictive models are then trained and evaluated automatically using those data. We show that individuals without ML experience can collectively construct useful datasets and that predictive models can be learned on these datasets, but challenges remain. The prediction tasks designed by non-experts covered a broad range of domains, from politics and current events to health behavior, demographics, and more. Proper instructions are crucial for non-experts, so we also conducted a randomized trial to understand how different instructions may influence the types of prediction tasks being proposed. In general, understanding better how non-experts can contribute to ML can further leverage advances in Automatic ML and has important implications as ML continues to drive workplace automation.