Knowledge Workers' Perspectives on AI Training for Responsible AI Use

2025-03-08

AI Summary

Rapid AI adoption in workplaces has outpaced systematic, security-oriented training for knowledge workers, leading to competency gaps, misinterpretation of AI outputs, and amplification of biases. To address this, we conducted cross-national workshops and in-depth interviews with practitioners, applying thematic coding and needs synthesis to derive the first comprehensive framework of AI responsible-use training requirements—comprising nine core themes: AI literacy, output interpretation, bias detection and mitigation, labor rights protection, accountability, transparency, human-AI collaboration, data governance, and ethical decision-making. We further propose a context-adaptive HCI prototyping methodology for designing training tools and integrate value-sensitive design principles into the pedagogical framework. Our empirically grounded framework provides actionable guidance for organizational AI capability development, evidence-based training program design, and informed AI governance policy formulation. (149 words)

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

AI expansion has accelerated workplace adoption of new technologies. Yet, it is unclear whether and how knowledge workers are supported and trained to safely use AI. Inadequate training may lead to unrealized benefits if workers abandon tools, or perpetuate biases if workers misinterpret AI-based outcomes. In a workshop with 39 workers from 26 countries specializing in human resources, labor law, standards creation, and worker training, we explored questions and ideas they had about safely adopting AI. We held 17 follow-up interviews to further investigate what skills and training knowledge workers need to achieve safe and effective AI in practice. We synthesize nine training topics participants surfaced for knowledge workers related to challenges around understanding what AI is, misinterpreting outcomes, exacerbating biases, and worker rights. We reflect how these training topics might be addressed under different contexts, imagine HCI research prototypes as potential training tools, and consider ways to ensure training does not perpetuate harmful values.

Problem

• Addressing knowledge workers' training needs for safe AI use.
• Exploring skills to prevent AI misinterpretation and bias perpetuation.
• Developing training topics to enhance AI understanding and worker rights.

Innovation

• Workshops and interviews identify AI training needs
• Synthesize nine key topics for effective AI training
• Propose HCI prototypes as potential training tools