ReDDDoT Phase 1: Planning Grant: Facilitating Responsible, Ethical, and Explainable Ergonomic Exposure Assessments when using Artificial Intelligence Methods

Work-related musculoskeletal disorders (WMSDs), such as back pain, sprains and strains, continue to impose an economic burden to the United States, amounting to around $54 billion annually. These economic burdens are around compensation costs for the employer, lost wages for the employee, and decreased productivity. The rapid technological advances in artificial intelligence (AI) offers new opportunities for WMSD risk assessment by enabling automated analysis of WMSD risk factors. However, there are concerns regarding potential biases in AI, particularly across workforce demographics. This project aims to investigate these algorithmic biases to ensure the ethical design and deployment of responsible and ethical AI technologies for the benefit of employees and employers across the United States. This aligns with NSF's mission to promote the progress of science, and advance the national health, prosperity and welfare. This project seeks to: (1) obtain stakeholder perspectives (benefits and concerns) on AI-based ergonomic assessments; (2) identify sources of algorithmic biases in AI-based ergonomic assessments; (3) develop and evaluate methods to mitigate algorithmic biases; and (4) hold focus groups and sessions with workers to understand AI biases and effects on worker health and explainability. This project aims to bring about substantial contributions to advance worker health and safety by addressing critical issues related to AI bias and fairness. The work has potential benefits for the United States economy and society, through improving worker health and well-being and reducing employer compensation costs, and to enhance diversity in the workforce, especially for women and older workers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.