Good Intentions, Risky Inventions: A Method for Assessing the Risks and Benefits of AI in Mobile and Wearable Uses

TL;DR

This paper introduces a semi-automatic LLM-based method for assessing risks and benefits of AI in mobile and wearable tech, validated with over 85% accuracy, highlighting both opportunities and risks.

Contribution

It presents a novel semi-automatic approach leveraging LLMs to evaluate AI applications in mobile and wearables, incorporating risk classification and benefit alignment with sustainability goals.

Findings

Mobile AI applications can improve well-being, safety, and social equality.

Risks involve sensitive data, vulnerable groups, and automated decisions.

The method achieves over 85% validation accuracy.

Abstract

Integrating Artificial Intelligence (AI) into mobile and wearables offers numerous benefits at individual, societal, and environmental levels. Yet, it also spotlights concerns over emerging risks. Traditional assessments of risks and benefits have been sporadic, and often require costly expert analysis. We developed a semi-automatic method that leverages Large Language Models (LLMs) to identify AI uses in mobile and wearables, classify their risks based on the EU AI Act, and determine their benefits that align with globally recognized long-term sustainable development goals; a manual validation of our method by two experts in mobile and wearable technologies, a legal and compliance expert, and a cohort of nine individuals with legal backgrounds who were recruited from Prolific, confirmed its accuracy to be over 85\%. We uncovered that specific applications of mobile computing hold significant potential in improving well-being, safety, and social equality. However, these promising uses are linked to risks involving sensitive data, vulnerable groups, and automated decision-making. To avoid rejecting these risky yet impactful mobile and wearable uses, we propose a risk assessment checklist for the Mobile HCI community.