THE ETHICAL AMBIGUITY OF AI DATA ENRICHMENT: MEASURING GAPS IN RESEARCH ETHICS NORMS AND PRACTICES Anonymous authors Paper under double-blind review

Abstract

The technical progression of artificial intelligence (AI) research has been built on breakthroughs in fields such as computer science, statistics, and mathematics. However, in the past decade AI researchers have increasingly looked to the social sciences, turning to human interactions to solve the challenges of model development. Paying crowdsourcing workers to generate or curate data, or 'data enrichment', has become indispensable for many areas of AI research, from natural language processing to inverse reinforcement learning. Other fields that routinely interact with crowdsourcing workers, such as Psychology, have developed common governance requirements and norms to ensure research is undertaken ethically. This study explores how, and to what extent, comparable research ethics requirements and norms have developed for AI research and data enrichment. We focus on the approach taken by two leading conferences: ICLR and NeurIPS, and journal publisher Springer. In a longitudinal study of accepted papers, and via. a comparison with Psychology and CHI papers, this work finds that leading AI venues have begun to establish protocols for human data collection, but these are are inconsistently followed by authors. Whilst Psychology papers engaging with crowdsourcing workers frequently disclose ethics reviews, payment data, demographic data and other information, similar disclosures are far less common in leading AI venues despite similar guidance. The work concludes with hypotheses to explain these gaps in research ethics practices and considerations for its implications.

1. INTRODUCTION

When the creators of the seminal image recognition benchmark, ImageNet, pronounced that the use of Amazon's Mechanical Turk (MTurk) was a "godsend" for their research, they foreshadowed the monumental impact crowdsourcing platforms were set to have on AI research (Li, 2019) . In the decade that has followed, crowdsourced workers, or 'crowdworkers' have been a central contributor to machine learning research, enabling low-cost human data collection at scale. Ethics questions posed by research involving human participants are traditionally overseen by governance groups, such as Institutional Review Boards (IRBs) in the United States (US). Whilst medical fields and social sciences have a long history of IRB engagement, the relatively recent rise of crowdsourcing tasks in AI research means guidelines and norms have been developed in recent years to consider research ethics. The proliferation of guidelines and publication policies have risen alongside critiques of AI crowdsourced work focused on issues such as payment and worker maltreatment. In response, this study seeks to understand how AI research involving crowdworkers engages with research ethics. It does this via an assessment of the expectations put forward by publication venues on researchers, and by analysing how these expectations translate into practices. To make this determination the policies and practices of major AI conferences, ICLR and NeurIPS, along with AI research submitted to Springer journals, are reviewed. This is compared with other benchmarks to understand whether AI research at these venues follows norms within more established disciplines. The results show that AI research at these venues involving crowdworkers lacks robust research ethics norms, with venue policies not translated into practice. Whilst ICLR, NeurIPS and Springer

