AIと統合された職場における労働者の福祉を促進するためのフレームワーク

こんにちは、丸山満彦です。

Partnership on AIから「AIと統合された職場における労働者の福祉を促進するためのフレームワーク」に関する報告書が公表されていますね。。。

● Partnership on AI

・2020.09.15 Framework for Promoting Workforce Well-being in the AI-Integrated Workplace

・[PDF] Full Report

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Table Of Contents

Executive Summary

Acknowledgements

About The Partnership on AI

Introduction

• Applications of AI in the Workplace
• Ethical Questions Associated with AI Integration
• Why Workforce Well-being
• About the Framework

PART 1

Six Pillars of Workforce Well-being and AI Impact

1. Human Rights
2. Physical Well-being
3. Financial Well-being
4. Intellectual Well-being
5. Emotional Well-being
6. Purpose and Meaning

Summary Chart: AI Impact and the Six Pillars of Workforce Well-being

PART 2

Framework for Promoting Workforce Well-being in the AI Integrated Workplace

1. Key Considerations
2. Recommendations
3. Discussion Questions
4. Worker Survey

Conclusion and Future Work

Appendix 1 - Definitions

Appendix 2 - Literature Review

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Exective Summary

The Partnership on AI’s “Framework for Promoting Workforce Well-being in the AI- Integrated Workplace” provides a conceptual framework and a set of tools to guide employers, workers, and other stakeholders towards promoting workforce well-being throughout the process of introducing AI into the workplace.

As AI technologies become increasingly prevalent in the workplace, our goal is to place workforce wellbeing at the center of this technological change and resulting metamorphosis in work, well-being, and society, and provide a starting point to discuss and create pragmatic solutions. The importance of making a commitment to worker well-being in earnest has been highlighted by the COVID-19 public health and economic crises which exposed and exacerbated the long-standing inequities in the treatment of workers. Making sure those are not perpetuated further with the introduction of AI systems into the workplace requires deliberate efforts and will not happen automatically.

The Framework is designed to initiate and inform discussions on the impact of AI that strengthen the reciprocal obligations between workers and employers, specifically focusing on worker well-being.¹ The four tools that make up the Framework are both interrelated and independent. Use of these tools will differ by implementing organization and will depend on multiple considerations, such as funding, ability to dedicate time, stage of AI integration, etc.

This paper draws upon existing work by academics, labor unions, and other institutions to explain why organizations should prioritize worker well-being. In doing so, it explores the need for a coherent AI and workforce well-being framework. It also attempts to account for different forms of AI integration into the workplace, outlines the different instances in which workers may encounter AI, and the technological aspects of AI that may impact workers.

Relevant literature has been synthesized into Six Pillars of Workforce Well-being that should be prioritized and protected throughout AI integration. Human rights is the first pillar, and supports all aspects of workforce well-being. The five additional pillars of well-being include physical, financial, intellectual, emotional well-being, as well as purpose and meaning.

The Framework presents four tools for organizational leaders, workers and other stakeholders as part of the section entitled “Promoting Workforce Well-being in the AI Integrated Workplace”:

1. Key Considerations for organizations to keep in mind when implementing the Framework. These are meant to promote trust amongst stakeholders, facilitate discussion and negotiation of competing interests, and ensure that the Framework is used to facilitate bottom up engagement, build evidence, and create solutions.
2. Recommendations that organizations can use to guide organizational thinking about promoting well-being throughout the integration of AI in the workplace.
3. Discussion Questions to guide organizational best practices throughout the AI lifecycle, which are intended for decision makers and can be useful for all stakeholders.
4. A Worker Survey to help organizations assess the impact of AI on the well-being of the workforce.

The “Framework for Promoting Workforce Well-Being in the AI-Integrated Workplace” is a product of the Partnership on AI’s AI, Labor, and the Economy (AILE) Expert Group, formed through a collaborative process of research, scoping, and iteration. In August 2019, at a workshop called “Workforce Well-being in the AI-Integrated Workplace” co-hosted by PAI and the Ford Foundation, this work received additional input from experts, academics, industry, labour unions, and civil society. Though this document reflects the inputs of many PAI Partner organizations, it should not under any circumstances be read as representing the views of any particular organization or individual within this Expert Group, or any specific PAI Partner.

¹ For example, the International Labour Organization in 2006 created recommendations guiding the employment relationship towards ensuring that labour law and practice promotes decent work. For more information see: International Labour Organization. Employment Relationship Recommendation, 2006. Available at: https://www.ilo.org/public/english/standards/relm/ilc/ilc95/pdf/rep-v-1.pdf and https://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_INSTRUMENT_ID:312535

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Conclusion and Future Work

A clear priority for future work in this area is the continued refinement and specification of the Framework for Promoting Workforce Well-being in the AI Integrated Workplace, with the goal of developing a robust model for assessing and promoting well-being in an AI-enabled workplace. Such a model must draw from an interdisciplinary perspective and incorporate the views of both management and workers. As part of this mission, it is necessary to better understand and articulate the core constructs of well-being in the AI integrated workplace, the technological factors impacting these constructs, and the varying degrees of independence and interdependence among these variables. Further consultations with key stakeholders, use of evidence collected through implementation, and additional research will be key in developing and refining this model.