Workstream: Promoting Workforce Well-being in the AI-Integrated Workplace

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

PAI Staff
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Executive Summary

Executive 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 well-being 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 paper categorizes aspects of workforce well-being that should be prioritized and protected throughout AI integration into six pillars. 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 a set of recommendations that organizations can use to guide organizational thinking about promoting well-being throughout the integration of AI in the workplace.

The Framework is designed to initiate and inform discussions on the impact of AI that strengthen the reciprocal obligations between workers and employers, while grounding that discourse in six pillars of worker well-being.

We recognize that the impacts of AI are still emerging and often difficult to distinguish from the impact of broader digital transformation, leading to organizations being challenged to address the unknown and potentially fundamental changes that AI may bring to the workplace. We strongly advise that management collaborate with workers directly or with worker representatives in the development, integration, and use of AI systems in the workplace, as well as in the discussion and implementation of this Framework.

We acknowledge that the contexts for having a dialogue on worker well-being may differ. For instance, in some countries there are formal structures in place such as workers’ councils that facilitate the dialogue between employers and workers. In other cases, countries or sectors do not have these institutions in place, nor a tradition for dialogue between the two parties. In all cases, the aim of this Framework is to be a useful tool for all parties to collaboratively ensure that the introduction of AI technologies goes hand in hand with a commitment to worker well-being. The importance of making such commitment 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.

Recommendations

Recommendations

This section articulates a set of recommendations to guide organizational approaches and thinking on what to promote, what to be cognizant of, and what to protect against, in terms of worker and workforce well-being while integrating AI into the workplace. These recommendations are organized along the six well-being pillars identified above, and are meant to serve as a starting place for organizations seeking to apply the present Framework to promote workforce well-being throughout the process of AI integration. Ideally, these can be recognized formally as organizational commitments at the board level and subsequently discussed openly and regularly with the entire organization.

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, labor 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.

Acknowledgements

The Partnership on AI is deeply grateful for the input of many colleagues and partners, especially Elonnai Hickok, Ann Skeet, Christina Colclough, Richard Zuroff, Jonnie Penn as well as the participants of the August 2019 workshop co-hosted by PAI and the Ford Foundation. We thank Arindrajit Basu, Pranav Bidaire, and Saumyaa Naidu for the research support.

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AI, Labor, and the Economy Case Study Compendium

PAI Staff
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Preface

Preface

The AI, Labor, and Economy Case Studies Compendium is a work product of the Partnership on AI’s “AI, Labor, and the Economy” (AILE) Working Group, formed through a collaborative process of research scoping and iteration. Though this work product reflects the inputs of many members of PAI, it should not be read as representing the views of any particular organization or individual within this Working Group, or an entity within PAI at-large.

The Partnership on AI (PAI) is a 501(c)3 nonprofit organization established to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.

One of PAI’s significant program lines is a series of Working Groups reflective of its Thematic Pillars, which are a driving force in research and best practice generation. The Partnership’s activities are deliberately determined by its coalition of over 80 members, including civil society groups, corporate users of AI, and numerous academic artificial intelligence research labs, but from the outset of the organization, the intention has been to create a place for open critique and reflection. Crucially, the Partnership is an independent organization; though supported and shaped by our Partner community, the Partnership is ultimately more than the sum of its parts and will make independent determinations to which its Partners will collectively contribute, but never individually dictate. PAI provides staff administrative and project management support to Working Groups, oversees project selection, and provides financial resources or direct research support to projects as needed.

AI, Labor, and the Economy Case Study Compendium

Preface

Objectives and Scope

Subject Diversity and Common Motifs 

Themes and Observations

Terms and AI techniques used

Methodology

Limitations and Further Work

Conclusion

Appendix

Sources Cited

  1. See Acknowledgements for more information
  2. Researchers have argued for the need for “more systematic collection of the use of these technologies at the firm level.” The case study project intends to provide quantitative and qualitative data at the firm level. For more, see “AI, Labor, Productivity and the Need for Firm-Level Data,”Manav Raj and Robert Seamans, April 2018.
  3. In business circles, many pre-established techniques such as pattern-matching heuristics, or linear regression and other forms of statistical data analysis, have recently been rebranded as “AI” (and in the case of statistical regression, also as “ML”). We accept these expansive definitions not because they are fashionable, but because they are more useful for understanding the economic consequences of present forms of automation. See section “Terms and AI techniques used” for more details on how these terms are defined.)
  4. Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements, (Cambridge, UK: Cambridge University Press, 2010).
  5. Quoted figures are reported from subject organizations, not independent analyses
  6. As the case illustrates, the social and labor impacts can often cascade beyond the location of the AI implementation. Kate Crawford and Vladan Joler explore this concept extensively as it relates to the “vast planetary network” of labor, energy, and data to support small interactions with an Amazon Echo. See more at www.anatomyof.ai.
  7. An ‘AI-native’ refers to a company that was founded with a stated mission of leveraging artificial intelligence or machine learning as a key enabling technology. ‘AI-natives’ can build infrastructure from the ground-up without the need to shift from legacy systems (e.g., on-premise to cloud-based storage).
  8. For more, see “Is the Solow Paradox Back?”, McKinsey Quarterly, June 2018.
  10. We do not have a measure of hours worked to estimate the increase in labor productivity precisely.
  11. Some have argued that inequality could increase with the proliferation of AI in the long term. While we do not address this question, please see Joseph Stiglitz and Anton Korinek’s paper for more: “Artificial Intelligence and Its Implications for Income Distribution and Unemployment,” December 2017.
  12. It is not clear what the net-impact of AI on jobs will be in the near future. The McKinsey Global Institute estimates that “total full-time-equivalent-employment demand might remain flat, or even that there could be a slightly negative net impact on jobs by 2030,” yet demand for new types of jobs may increase, as seen with the advent of the personal computer in the late 20th century.
  13. This only includes scientists and research associates and does not account for data scientists, automation engineers, and lab technicians that support teams with their services.
  14. Zymergen is an “AI-native” company that was founded in 2013. As such, the company started its data storage in the cloud. All data infrastructure could be built with a clean slate and modern toolchains, making data exportation and analysis on cloud systems easier than it might be for an incumbent (such as Tata Steel Europe). The latter might be dependent on proprietary or embedded on-premise systems that were installed without these objectives in mind.
  15. Natural Language Processing, a popular subfield of AI
  16. CNN’s were tested as part of Zymergen’s broader recommendation engine and were also used in isolated cases within the lab (e.g. computer vision for plate readers).
  17. During the time of writing the case study in fall 2018, the company had raised $174M. On December 13, 2018, the company announced a $400M Series C round from multiple investors. See coverage of the announcement on Bloomberg and the Wall Street Journal.
  18. Our definition draws on the classic articulation of automation described by Parasuraman, Sheridan, and Wickens (2000): https://ieeexplore.ieee.org/document/844354
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