Yesterday, I attended a webinar by the Vector Institute, a Canadian not-for-profit collaboration between leading research universities, the government, and private sector dedicated to advancing AI, machine learning, and deep learning research. The topic was “Using AI to guide re-opening of workplaces in the wake of COVID-19.”
The webinar consisted of a presentation by Avi Goldfarb, professor at the Rotman School of Management at the University of Toronto (U of T), Vector Faculty Affiliate, and chief data scientist at the tech incubator Creative Destruction Lab, and a follow-up discussion with:
The session was moderated by Garth Gibson, president and CEO of Vector Institute.
Professor Goldfarb’s presentation was based on his recent article in MIT Technology review (jointly with Ajay Agrawal, Joshua Gans, and Mara Lederman). It framed succinctly the problem that many CEOs are facing now or will be facing shortly as nations and economies are exiting the lockdowns: Should we reopen and, if so, how?
So far, managers’ decisions have been easy, said Professor Goldfarb, because they were not made by the managers but by politicians. If politicians say that your workplace is closed, it is closed. But as the economy is opening up, these decisions, he said, and the responsibility are being shifted back to the business leaders. And they will need to make some tough choices: who goes to work, when and in what sequence, what mechanisms should be put in place to protect employees and customers, etc.
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More importantly, managers will need to decide what level of risk they would be willing to tolerate with people’s lives and their business reputation at stake. (Not to downplay the risk to their careers, too.)
All of this is particularly challenging because nobody knows when the pandemic will end. And managers, Professor Goldfarb said, have no control over it, while workplaces are “opportunities for infected people to infect others.” Employers, he continued, “don’t know who has the virus, who had it, and who has never had it. And this creates an economic crisis. If we did know, there would be no economic crisis.”
There is of course an option to never open up — or at least not for some time — but for many businesses, “doing nothing” is not a viable option. They are bleeding money despite all government support, and many will not last much longer.
An old management cliché says that every crisis has a silver lining in the form of an opportunity. And since this economic crisis is driven by the lack of information, said Professor Goldfarb, it has created an opportunity for AI (machine learning specifically), which is a technology that “enable[s] increasingly complex predictions from a wide variety of data sources.”
But before we get to AI, let’s look at other solutions. There are two, he said:
- Always-on solutions, such as PPE, physical barriers, frequent sanitizing, redesigned workspaces and physical flow of people, redesigned management processes, etc.
- Information-based solutions (meaning AI-based).
Always-on solutions are a blunt instrument that is applied to everyone, said Professor Goldfarb, and the most extreme always-on solution is the lockdown which we have lived with for the past three months.
Information-based solutions are those that involve “predicting who is infectious and who is immune and then developing tools to leverage this information for contingent decision making” (his emphasis). This contingent decision making usually takes shape of an “if-then” formula: “If X is predicted to be true, then do A; otherwise, do B.”
The example he gave was of a system detecting that an employee has elevated temperature. In that case, security should be notified and either direct the employee to a nearby COVID-19 testing station or send the person home. No fever — no action.
So, where can AI help?
Some of the information-based solutions that Professor Goldfarb proposed include:
- “Tests that predict whether the coronavirus is present in an individual
- Contact tracing
- Image analysis of people density or proximity
- Symptom monitoring
- Workplace monitoring of air, sewage, distancing, etc.”
Unfortunately, many of these solutions do not yet exist, and those that do are in the early experimental stages. CDL Recovery, a new program at the Creative Destruction Lab where Professor Goldfarb is chief data scientist, is focusing on developing such information-based solutions, and they hope to help about a dozen startup companies to create such solutions by the end of July, said Professor Goldfarb.
Meanwhile, the best “models” (as in machine learning models or decision-making models) we have today are the guidelines from public health authorities: wearing masks in public, frequent hand washing, maintaining a distance of two meters/six feet, staying at home as much as possible, and so on.
Assuming that some tools will become available soon to guide business leaders in decisions concerning the reopening of their businesses (and putting aside the questionable viability of some of these tools in the context of an average organization, which is a large topic in itself), managers will face another hurdle: the probabilistic nature of AI.
When decisions are based on probabilities, a mental shift is required: from deterministic and binary thinking (yes/no) to probabilistic and less certain. For example, what does it mean that a likelihood of something is 95% or 82% or 70.4%? How would that translate into action? What actions would those be? Do decision makers understand this? Are they ready to operate with this new thought model?
“So it is with coronavirus,” writes professor Goldfarb and his coauthors: “Should your business keep operating if there is a 1% chance an infected person gets through the door? What about a 5% chance or a 0.1% chance? The answer depends on the benefits relative to the costs — on the importance of opening the physical workplace versus the risk of infection.”
We have seen some of this in action with many essential businesses — grocery stores, pharmacies, supermarkets, etc. — staying open throughout the lockdown. It would be hard for society to function and people to survive if they weren’t.
During the reopening phase, “many CEOs of large enterprises will begin to behave like presidents and prime ministers,” said Professor Goldfarb. “They will report their number of cases (infections and deaths), they will explain their strategies for keeping their curves flat, and they will swing into crisis management mode when their organizations experience an outbreak.”
And they should be prepared to have their decisions to be heavily scrutinized. “Their challenge is that every decision involves a trade-off between short-term profit and safety that by design assumes some risk.” And if (when?) a tragedy strikes, he continued, “the central question will be not of a simple assignment of blame but whether the risk they took was wise.”
To make these tough decisions, managers will need to choose from a range of strategies, including a mix of general always-on solutions and probabilistic information-based solutions powered by AI.
The best part of the webinar was the discussion after Professor Goldfarb’s presentation concluded. It touched upon several topics:
Should organizations share data to support reopening? RBC’s Arthur Berrill said yes, assuming proper controls, such as data anonymization. He also spoke about huge regulatory pressures that financial institutions are under and internal frameworks, and he gave an example of using live data for just-in-time decisions (non-COVID-19 related). Imagine, he said, if we could establish such live data links for COVID-19.
There was a long discussion about data privacy. Many employees will be against contact tracing apps, and does privacy trump public good? Professor Hadfield offered her perspective from reviewing relevant law. She said the law does not say that privacy trumps all and that we need to acknowledge that information is not just a personal asset — it is a public resource as well (to be used for public safety). But boundaries are not clear, and all these debates that are taking place now are about figuring out where to draw that line.
There was a question from the audience about whether one could exclude older employees from coming back to the office, because the risk of death is higher for them. (Or what if they do not want to return to the office themselves? How should you handle that?) This question prompted a good discussion about ethics, discrimination, and the nature of risk: are we discussing the risk of employee death from COVID-19 or the risk of further transmission — of spreading the infection via the workplace — with all its consequences?
Mr. Berrill of RBC had a very strong position on this: We should not exclude or penalize older employees, he said. The risk (of getting infected) is the same for all. We need to worry about the risk of catching and spreading the disease, and we need to implement appropriate protection mechanisms in the workplace.
Another audience member asked about information-based tools to support reopening decisions. Whose responsibility is it to ensure that these tools are accurate? And should they be better than, say, government tools? The panel’s response was that the responsible thing is to do what the law requires and then to go over and above the law, because the law really tells you the minimum of what you should do. And the ultimate liability and responsibility lies with the business leaders, who should do the right thing.
Whatever that right thing is will differ, as the multitude of opinions, actions, and behaviors — some of which are acceptable, some not, some borderline grey cases between ethical/unethical, moral/immoral, lawful/unlawful, and so on — in business, politics, and personal lives illustrate.
I think we need a series of panel discussions to sort out these thorny ethical issues around reopening businesses. For many of these questions, we do not yet have the answers, but we are reopening the economy. And we are making these decisions as we go.
We have public health guidelines to help protect ourselves, each other, and those who are vulnerable. We have similar sets of government guidelines for workplaces, such as the number of customers in a restaurant at any one time, spacing of tables, etc., just to take one example.
To support managers who are making tough decisions now, we need a forum where business leaders can discuss the ethical ramifications of the reopening, decisions and trade-offs they are making, risk frameworks they are using, and lessons they are learning. Is Vector Institute the right place to coordinate and host such a forum? A business school like Rotman? The new Schwartz Reisman Institute for Technology and Society? An NFP thinktank? An industry group such as the CIO Strategy Council?
Where are these topics being discussed in your industry? And in your state, province, and country? (Besides mass media and social media channels.) Please reach out and let us know! (Or if you want to discuss any of the topics in this note.) What kinds of questions you are grappling with? What would be most helpful to you right now?