Definitions and terms used
While we acknowledge that there is no consensus on the definition of terms such as AI and automation, we would like to explain how these terms are used in the compendium:
Artificial intelligence/AI is a notoriously nebulous term. Following the Stanford 100 Year Study on Artificial Intelligence, we embrace a broad and evolving definition of AI. As Nils J. Nilsson has articulated, artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment. (Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements, (Cambridge, UK: Cambridge University Press, 2010).
Our definition of automation is based on the classic human factors engineering definition put forward by Parasuraman, Sheridan, and Wickens in 2000: https://ieeexplore.ieee.org/document/844354, in which automation refers to the full or partial replacement of a function previously carried out by a human operator.Our definition draws on the classic articulation of automation described by Parasuraman, Sheridan, and Wickens (2000): https://ieeexplore.ieee.org/document/844354 Following Parasuraman et al.’s definition, levels of automation also exist on a spectrum, ranging from simple automation requiring manual input to a high level of automation requiring little to no human intervention in the context of a defined activity.
Explainable AI or Explainability is an emerging area of interest in communities ranging from DARPA to criminal justice advocates. Broadly, the terms refer to a system that has not been “black-boxed,” but rather produces outputs that are interpretable, legible, transparent, or otherwise explainable to some set of stakeholders.
In this compendium, a model refers to a simplified representation of formalized relations between economic, engineering, manufacturing, social, or other types of situations and natural phenomena, simulated with the help of a computer system.
PAI is deeply grateful for the collaboration of so many colleagues in this endeavor, including project leadership from AILE Working Group member Madeleine Clare Elish (Data & Society) and Working Group Co-Chairs Michael Chui (McKinsey) and Elonnai Hickok (Centre for Internet and Society).
In addition to this holistic leadership, authorship of the Tata Steel Europe, Axis Bank, and Zymergen cases was co-led by Lars Jensen (United Nations Development Programme), Elonnai Hickok, and Tithi Chattopadhyay (Center for Information Technology Policy, Princeton University) respectively, with the support of Arif Cam, Turker Coskun, Travers Nisbet, and Eric Schreiber (all from McKinsey).
The authors are grateful to members of the Partnership on AI for their thoughtful feedback and reflections.
The authors also wish to thank the many additional minds and talents without whom this work would not have been possible – particularly Aayush Rathi (Centre for Internet and Society), Margaret Steen, Sue Wunder, and KNI Design.
The authors offer a special thanks to Tata Steel Europe, Axis Bank, and Zymergen for participating in this research, as well as to Rachel Dinh, Claire Leibowicz, Steven Adler, Peter Eckersley, Peter Lo, and Terah Lyons for their efforts in bringing it to fruition.