The Case for Evidence in Government – Doug Criscitello

Doug Criscitello, Executive Director of MIT’s Center for Finance and Policy

Doug Criscitello, Executive Director of MIT’s Center for Finance and Policy

From Government Executive

Although the U.S. government presides over what collectively must be one of the world’s largest data repositories, its capacity to use that data to build citizen trust and make informed, evidence-based decisions is severely constrained. As explained in an enlightening report recently issued by the bipartisan Commission on Evidence-Based Policymaking (CEP), the mere existence of data is a necessary but not sufficient condition for creating empirical evidence to inform decisions throughout the full lifecycle of public programs—enactment, funding, operation, reform, termination.

The digitization of many facets of various activities the government funds through its $4 trillion annual budget has resulted in a data explosion at federal agencies. But that data needs to be synthesized into actionable information to satisfy taxpayers’ demands for better results and greater transparency. The CEP report makes clear that much remains to be done to achieve that goal and provides a comprehensive plan to improve access to federal data, strengthen privacy protections and expand the public, private and academic research communities’ capacity to analyze data.

CEP provides an insightful list of recommendations such as establishing a National Secure Data Service to enable and leverage capabilities across government, addressing statutory impediments that obstruct smart data use, and streamlining processes used to grant researchers access to data. The report appropriately emphasizes strong privacy protections and advocates for comprehensive risk assessments for publicly released data and for the use of better technology and greater coordination across government. To prioritize efficient evidence building, CEP points out the need to coordinate statistical activities, evaluation and policy research within and between departments and across levels of government.

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New study offers hope for commuters caught in traffic – Ioannis Ch. Paschalidis

Ioannis Ch. Paschalidis, a former Visiting Professor at MIT Sloan

If you live in Boston, Los Angeles or any other major U.S. city, you know this fact: traffic is a nightmare. Sometimes it seems that traffic is all anyone talks about and each delayed meeting or event begins with a story about how bad it was.

The average commuter in the U.S. spends 42 hours in traffic per year. The cost of commuter delays has risen by 260 percent over the past 25 years and 28 percent of U.S. primary energy is now used in transportation. Road congestion is responsible for about 20% of fuel consumption in urban areas. According to one estimate, the cumulative cost of traffic congestion in the U.S. will reach $2.8 trillion by 2030. At the individual citizen level, traffic congestion cost $1,740 per driver during 2014. If unchecked, this number is expected to grow by more than 60 percent, to $2,900 annually, by 2030.

It’s a problem with a classic common and tragic root.  No individual driver has an incentive to make changes that would make the entire system better.  In other words, each driver seeks to make the best time or take the most convenient route, but no one is in charge of making the system work better as a whole.  As a result, traffic just keeps getting worse.

But technology, which in the form of the automobile gave us this problem, may now offer up the faintest hope of a solution for this problem—that is, the global positioning system, the pervasive use of cell phones, and the advent of the self-driving vehicle could bring new solutions to this seemingly intractable problem. Read More »

The business of artificial intelligence – Erik Brynjolfsson and Andrew McAfee

Professor of Information Technology, Director, The MIT Initiative on the Digital Economy

Director of the MIT Initiative on the Digital Economy, Erik Brynjolfsson

Co-Director of the MIT Initiative on the Digital Economy, Andrew McAfee

From Harvard Business Review

For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models.

The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own.

Why is this such a big deal? Two reasons. First, we humans know more than we can tell: We can’t explain exactly how we’re able to do a lot of things — from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn’t automate many tasks. Now we can.

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Young doctors struggle to learn robotic surgery, so they are practicing in the shadows – Matt Beane

Matt Beane
Research Affiliate in
Management Science

From The Conversation

Artificial intelligence and robotics spell massive changes to the world of work. These technologies can automate new tasks, and we are making more of them, faster, better and cheaper than ever before.

Surgery was early to the robotics party: Over a third of U.S. hospitals have at least one surgical robot. Such robots have been in widespread use by a growing variety of surgical disciplines, including urology and gynecology, for over a decade. That means the technology has been around for least two generations of surgeons and surgical staff.

I studied robotic surgery for over two years to understand how surgeons are adapting. I observed hundreds of robotic and “traditional” procedures at five hospitals and interviewed surgeons and surgical trainees at another 13 hospitals around the country. I found that robotic surgery disrupted approved approaches surgical training. Only a minority of residents found effective alternatives.

Like the surgeons I studied, we’re all going to have to adapt to AI and robotics. Old hands and new recruits will have to learn new ways to do their jobs, whether in construction, lawyering, retail, finance, warfare or childcare – no one is immune. How will we do this? And what will happen when we try?

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IOT’S people problem – Peter Hirst

MIT Sloan Associate Dean of Executive Education Peter Hirst

MIT Sloan Associate Dean of Executive Education Peter Hirst

From Insight

This spring, I participated in three major IoT-focused events and came away with mixed feelings about the state of the industry. The first was the Internet of Things World in Santa Clara, California. The conference tends to focus on more technological aspects of IoT and draws thousands of attendees. A few days later, I flew to London to take part in the Internet of Things World Forum (IoTWF), an invitation-only event that caters to the C-suite audience. The third was a board meeting and strategy workshop for the Internet of Things Talent Consortium, a spinoff from the IoTWF, of which MIT Sloan Executive Education is a founding member. The three events were highly educational, thought provoking and inspirational in their own right, but all shared a common theme—we, the people, are the main barrier to faster and wider IoT adoption. Moreover, it’s the very nature of humanity, our habits and idiosyncrasies that seem to be stalling the robots’ march toward making our lives wonderfully better—or toward total world domination, depending on how you look at it. More seriously, here are some themes that emerged in my mind once the conference excitement wore off.

Stop Resisting Change
People are creatures of habit. We are comfortable with what we know. It’s not laziness—it’s an evolutionary trick we’ve developed to survive as a species. New is scary and we tend to resist it, willfully or subconsciously, but this resistance can hinder progress. For example, as we heard from the main stage at IoT World Forum in London, GE— who is one of the earliest and relatively successful entrants into IoT—sees organizational inertia as one of the biggest problems in digital transformation. Culture clashes between different kinds of businesses within an organization are dragging down the entire enterprise. Traditional engineers and digital engineers are not speaking the same language. Business leaders are not yet adept in the ways of leading required to drive a digitally-enabled transformation. At IoT World, I was on a panel discussing the future leadership needs around IoT. The panel featured professionals in different industries from education to talent recruitment to defense. And what everybody was saying is that when you look at executives, the need to have agility and resilience is just as great as the need for people who can understand both the technology and the business. In the IoT era, leaders need to have awareness of all sorts of business-environment issues, as well as other important concerns, such as privacy and cyber security, regulation and public policy. Whether it’s strictly IoT or not, you could see that in examples like Uber or Airbnb, as they’ve run into public policy, regulatory and other kinds of situations. I think it’s fairly self-evident that it would be hard for those businesses to succeed without having leaders who are able to take on those kinds of aspects as well.

I’ve touched upon this subject previously, and hearing from companies like GE only reinforced my impression that there is an immediate need to not only train the workers who will work on the IoT implementation, but also to educate leaders on how to lead the transformation. In light of this, the IoT Talent Consortium is redoubling its efforts to help organizations understand and analyze that question. The group sees itself as a community where digital-transformation pioneers and people who believe that they need to or want to go through this kind of journey can share experiences and identify successful practices.

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