AI Discussion Forum, at Oxford’s Saïd Business School: Agenda

Themes and pre-readings on artificial intelligence and business

THERE IS A NEED for fresh, serious thinking about how AI will reshape business. A three-part discussion series in spring 2017 aimed to bring new voices to the conversation. Hosted at Oxford’s Saïd Business School, the sessions considered AI and productivity, data and management.

At each session two guest speakers introduced the theme; one from Oxford, one from outside. Importantly the attendees were not the audience but the discussants. I served as moderator. (An overview of the initiative is here; a bit about me is here.)

A summary of each session will be posted shortly.

June 1: algorithmic management | May 11: productivity | May 18: data

Below is a description of the sessions, including themes and pre-readings. If you have any questions, please email me at (Also, if I’ve missed a good source that I should include, please let me know.)


June 1st 2017 — AI + Management: when your boss is a bot

  • As more activities in society run via algorithms, what is the role of humans to “manage” or “govern” the systems? How do we enshrine our goals and values in the technology, and ensure that ethics and transparency exist (especially as AI systems cloud causality in return for efficiency)? Privacy, antitrust, accountability, open data and the role of government to create markets are essential public and private sector interests. What recourse should individuals and companies have in the face of adverse decisions?

“Salvo” talk 1: Jonathan Trevor of Oxford’s Saïd Business School on adapting organizations for AI-augmented management.

“Salvo” talk 2: James Farrar, former Uber driver and founder of Networked Rights, on the human side of algorithmic management.


Andrew McAfee and Erik Brynjolfsson. “Human Work in the Robotic Future: Policy for the Age of Automation,” Foreign Affairs. July/August 2016.

Sarah O’Connor. “When your boss is an algorithm.”Financial Times, September 8, 2016.

Noam Scheiber. “How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons,” NYT, April 2, 2017.

Also recommended:

Thomas H. Davenport and Julia Kirby. “Beyond Automation,” Harvard Business Review. June 2015.

Min Kyung Lee, Daniel Kusbit, Evan Metsky, Laura Dabbish. “Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers,” Proceedings of the ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2015), 1603–1612.

Kroll, Joshua A. and Huey, Joanna and Barocas, Solon and Felten, Edward W. and Reidenberg, Joel R. and Robinson, David G. and Yu, Harlan. “Accountable Algorithms,” University of Pennsylvania Law Review, Vol. 165, 2017, March 2, 2016. Fordham Law Legal Studies Research Paper №2765268.

Reuben Binns. “Algorithmic Accountability and Public Reason,” Philosophy & Technology journal. May 2, 2017.


May 11th 2017 — AI + Productivity: Efficiency at algorithmic scale

  • AI may create a huge boost in productivity, as “intelligence” gets embedded into many objects and operations. Problems in society and business once depended solely on the human form or intellect to solve will now be done far more efficiently by algorithms and robots. Where is this happening, and what is the case for and against a productivity boom? How can we measure the productivity gains — are our tools up to the task, or do we need new approaches? And what is the effect on international trade and development?

“Salvo” talk 1: David Kelnar (of MMC Ventures) on the business impact of AI.


David Kelnar. “The fourth industrial revolution: a primer on Artificial Intelligence (AI),” Dec. 2, 2016.

Ajay Agrawal, Joshua Gans and Avi Goldfarb. “Managing the Machines: AI is making predictions cheap, posing new challenges for managers.” Working paper, University of Toronto Rothman School of Business, October 2016.

William H. Janeway. “Which Productivity Puzzle?,” Medium. April 1, 2017.

Also recommended:

Jason Furman. “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence.” Council of Economic Advisers. Remarks at AI Now: The Social and Economic Implications of Artificial Intelligence Technologies in the Near Term, New York University New York., July 7, 2016. (also see video of talk)

Tyler Cowen. “Economic development in an ‘Average is over’ world,” Working paper. George Mason University. May 2016.

Nicholas Chen, Lau Christensen, Kevin Gallagher, Rosamond Mate, Greg Rafert. “Global Economic Impacts Associated with Artificial Intelligence” Analysis Group. 2016.

Mark Purdy and Paul Daugherty. “Why AI is the future of growth.” Accenture, September 2016.

Harold Sirkin, Michael Zinser and Justin Rose. “How Robots Will Redefine Competitiveness.” The Boston Consulting Group. September 23, 2015.


May 18th 2017 — AI + Data: Scramble for a scarce resource

  • The most valuable asset is the “fuel” that enables AI to work: data. Yet a bottleneck is access to big data, and the lack of labeled data with which to train systems. The session will look at what companies are doing in their “great game” for access to data. How are companies putting a value on data and creating a market for it? Also, what are the new techniques that researchers are devising to overcome the need for abundant data?

“Salvo” talk 1: Nathan Benaich (AI investor and analyst) on training-data sources for AI and virtual environments.

“Salvo” talk 2: Greg Taylor (economist at OII) on data as a barrier to and enabler of competition.

“Salvo” talk 3: Niki Trigoni (computer scientist at Oxford) on trends in sensor data and commercial applications.


Kenneth Cukier. “Data and the secret scramble for AI’s soul,” Medium. May 2017.

“Data is giving rise to a new economy,” The Economist, May 6, 2017.

James E. Short and Steve Todd. “What’s Your Data Worth?,” MIT Sloan Management Review, Spring 2017, March 3, 2017.

Natasha Lomas. “We need to talk about AI and access to publicly funded data-sets,” TechCrunch, July 9, 2016.

Also recommended:

Drew Breunig. “The Business Implications of Machine Learning,” freeCodeCamp, June 23, 2016.

Hal Hodson. “Google’s new NHS deal is start of machine learning marketplace,” New Scientist, 6 July 2016.

Linden Tibbets. “AI is About Access, Not Interface,” Medium post. Oct 26, 2016.

Tom Simonite. “Algorithms That Learn with Less Data Could Expand AI’s Power.” MIT Technology Review. May 24, 2016.

Albert Opher, Alex Chou, Andrew Onda, and Krishna Sounderrajan. “The rise of the data economy and data monetization,” IBM White Paper 2016.

Q Ethan McCallum and Ken Gleason. “Business Models for the Data Economy.” O’Reilly Media, 2013.

Kenneth Cukier And Viktor Mayer-Schönberger. “The Financial Bonanza of Big Data,” Wall Street Journal, March 7, 2013.


A final note: what about jobs?!

It’s true that a gaping hole is the question of employment. The reason is that it gets a lot of attention already in other venues, whereas the issue of productivity, data and management is bereft of a home. That said, if the discussion forum evolves past the initial three sessions, jobs will certainly be a topic to take on.

Any comments or feedback? Please email me at

Why show a pic of Terminator when humanity wins in the end?