AI, Labor, and the Economy Case Study Compendium

PAI Staff

April 30, 2019

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.

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

See Acknowledgements for more information
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.
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.)
Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements, (Cambridge, UK: Cambridge University Press, 2010).
Quoted figures are reported from subject organizations, not independent analyses
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.
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).
For more, see “Is the Solow Paradox Back?”, McKinsey Quarterly, June 2018.
We do not have a measure of hours worked to estimate the increase in labor productivity precisely.
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.
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.
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.
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.
Natural Language Processing, a popular subfield of AI
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).
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.
Our definition draws on the classic articulation of automation described by Parasuraman, Sheridan, and Wickens (2000): https://ieeexplore.ieee.org/document/844354