Content Type: Analysis

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.

Human-AI Collaboration Trust Literature Review: Key Insights and Bibliography

PAI Staff
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Key Insights from a Multidisciplinary Review of Trust Literature

Key Insights from a Multidisciplinary Review of Trust Literature

Understanding trust between humans and AI systems is integral to promoting the development and deployment of socially beneficial and responsible AI. Successfully doing so warrants multidisciplinary collaboration.

In order to better understand trust between humans and artificially intelligent systems, the Partnership on AI (PAI), supported by members of its Collaborations Between People and AI Systems (CPAIS) Expert Group, conducted an initial survey and analysis of the multidisciplinary literature on AI, humans, and trust. This project includes a thematically-tagged Bibliography with 78 aggregated research articles, as well as an overview document presenting seven key insights.

These key insights, themes, and aggregated texts can serve as fruitful entry points for those investigating the nuances in the literature on humans, trust, and AI, and can help align understandings related to trust between people and AI systems. This work can also inform future research, which should investigate gaps in the research and our bibliography to improve our understanding of how human-AI trust facilitates, or sometimes hinders, the responsible implementation and application of AI technologies.

Key Insights

Several high-level insights emerged when reflecting on the bibliography of submitted articles:

  1. There is a presupposition that trust in AI is a good thing, with limited consideration of distrust’s value.
    The original project proposal emphasized a need to understand the literature on humans, AI, and trust in order to eventually determine appropriate levels of trust and distrust between AI and humans in different contexts. However, the articles included in the bibliography are largely framed with the need and motivation towards trust – not distrust – between AI systems and humans. While certain instances may warrant facilitated trust between humans and AI, others may actually enable more socially beneficial outcomes if they prompt distrust or caution. Future literature should explore distrust as related, but not necessarily directly opposite, to the concept of trust. For example, an AI system that helps doctors detect cancer cells is only useful if the human doctor and patient trust that information. In contrast, individuals should remain skeptical of AI systems designed to induce trust for malevolent purposes, such as AI-generated malware that may use data to more realistically mimic the conversational style of a target’s closest friends.
  2. Many of the articles were published before the Internet’s ubiquity/the social implications of AI became a central research focus.
    It is important to contextualize recent literature on intelligent systems and humans with literature focused on social and cognitive mechanisms undergirding human to human, or human to organizational, trust. Future work can put many of the foundational, conceptual articles that were written before the 21st century in conversation with those specifically focused on the context of AI systems, and their different use cases. It can also compare foundational, early articles’ exploration of trust with how trust is seen specifically in relation to humans interacting with AI.
  3. Trust between humans and AI is not monolithic: Context is vital.
    Trust is not all or nothing. There often exist varying degrees of trust, and the level of trust sufficient to deploy AI in different contexts is therefore an important question for future exploration. There might also be several layers of trust to secure before someone might trust and perhaps ultimately use an AI tool. For example, one might trust the data upon which an intelligent system was trained, but not the organization using that data, or one might trust a recommender system or algorithm’s ability to provide useful information, but not the specific platform upon which it is delivered. The implications of this multifaceted trust between human and AI systems, as well as its implications on adoption and use, should be explored in future research.
  4. Promoting trust is often presented simplistically in the literature.
    The majority of the literature appears to assert that not only are AI systems inherently deserving of trust, but also that people need guidance in order to trust the systems. The basic formula is that explanation will demonstrate trustworthiness, and once understood to be deserving of trust, people will use AI. Both of these conceptual leaps are contestable. While explaining the internal logic of AI systems does, in some instances, improve confidence for expert users, in general, providing simplified models of the internal workings of AI has not been shown to be helpful or to increase trust.
  5. Articles make different assumptions about why trust matters.
    Within our corpus, we found a range of implicit assumptions about why fostering and maintaining trust is important and valuable. The dominant stance is that trust is necessary to ensure that people will use AI. The link between trust and adoption is tenuous at best, as people often use technologies without trusting them. What is largely consistent across the corpus – with the exception of some papers concerned about the dangers of overtrust in AI – is the goal of fostering more trust in AI, or stated differently, that more trust is inherently better than less trust. This premise needs challenging. A more reasonable goal would be that people are able to make individual assessments about which AI they ought to trust and which they ought not trust, in the service of their goals for what specifically and in which circumstances. This connects to insight 1: There is a presupposition that trust in AI is a good thing. It is important to think about context, person-level motivations and preferences, as well as instances in which trust might not be a precondition for use or adoption.
  6. AI definitions differ between publications.
    The lack of consistent definitions for AI within our corpus makes it difficult to compare findings. Most articles do not present a formal definition of AI, as they are concerned with a particular intelligent system applied in a specific domain. The systems in question differ in significant ways, in terms of the types of users who may need to trust the system, the types of outputs that a person may need to trust, and the contexts in which the AI is operating (e.g., high- vs. low-stakes environments). It is likely that these entail different strategies as they relate to trust. There is a need to develop a framework for understanding how these different contributions relate to each other, potentially looking not at trust in AI, but at trust in different facets and applications of AI. For a more detailed analysis of what questions to ask to differentiate particular types of human-AI collaboration, see the PAI CPAI Human-AI Collaboration Framework.
  7. Institutional trust is underrepresented.
    Institutional trust might be especially relevant in the context of AI, where there is often a competence or knowledge gap between everyday users and those developing the AI technologies. Everyday users, lacking high levels of technical capital and knowledge, may find it difficult to make informed judgments of particular AI technologies; in the absence of this knowledge, they may rely on generalized feelings of institutional trust.

About the Bibliography

About the Bibliography

The CPAI Trust Literature Bibliography includes 78 thematically tagged research articles (with references and abstracts). The article selection process sourced content from a multidisciplinary community all aligned around an interest and expertise in human-AI collaboration. Submitted articles were evaluated for inclusion and analyzed by members of a smaller project group from within the PAI Partner community. An analysis of the almost 80 initial articles resulted in the development of four thematic tags, highlighting the ways the article abstracts approached the issue of trust. Specifically:

Understanding – lays out a conceptual framework for trust or is primarily a survey of trust-related issues.
Promoting – focuses on means for increasing trust
Receiving – focuses on the entity (e.g., a robot, a system, a website) that is trusted
Impacting – focuses on the nature of changes due to trust being present (e.g., the impact on a group or an organization when it experiences trust)
Two individuals from the smaller project group undertook a thematic tagging exercise to assess inter-rater reliability and the distribution of themes across articles. They tagged themes as primary and secondary (first and second order) for each article from the four thematic options above.

The CPAIS Trust Literature Bibliography identifies thematic tags for each article, at levels 1 and 2. The “themes” column lists the first order themes and the second order themes, where applicable. The total tags for each article (at both levels) are also provided. “Understanding trust” was the most frequent theme – used with 61 articles (78% of the total). 50 articles (64%) were tagged with “promoting trust,” and 29 articles (37%) were tagged with “receiving trust”. Finally, 13 articles (16%) were tagged with a focus on impacting trust.

This bibliography and thematic tags serve as fruitful entry points for those investigating the nuances in the literature on humans, trust, and AI, especially when contextualized with the insights drawn from the corpus presented above.

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