AI Case Study
Boston Public Schools's plan to reconfigure start times for high school students using an optimisation algorithm backfires
Boston Public Schools intended to leverage traditional AI methods of planning and analytics to reduce sleep deprivation of teenagers due to early school start times. Two MIT graduates were appointed to the task by officials, facing the trade off of minimising the number of school buses required, maximise the number of students starting school after 8am and increasing parental happiness and satisfaction. However, the proposed solution resulted in fury amongst parents as the updated schedule would affect middle and elementary students' start times as well. Although BPS aimed to reduce inequities, with almost 85 percent of the district getting new start times, many black and brown families would be negatively affected. The proposed change by the algorithm was not implemented.
Public And Social Sector
Education And Academia
In 2017, "the Boston Public Schools asked MIT graduate students Sébastien Martin and Arthur Delarue to build an algorithm that could do the enormously complicated work of changing start times at dozens of schools — and rerouting the hundreds of buses that serve them.
Sorting through 1 novemtrigintillion options — that’s 1 followed by 120 zeroes — the algorithm landed on a plan that would trim the district’s $100 million-plus transportation budget while shifting the overwhelming majority of high school students into later start times.
The data showed that schools in whiter, better-off sections of Boston were more likely to have the school start times that parents prize most — between 8 and 9 a.m. The mere act of redistributing start times, if aimed at solving the sleep deprivation problem and saving money, could bring some racial equity to the system, too.
As the graduate students and district dug into the work, they confronted these baseline realities: Only a quarter of high school students started school after 8 a.m…
Less than half of BPS parents were happy with their children’s start times. And the district was using 600 buses to transport kids to school at a cost of tens of millions of dollars.
Officials faced a number of difficult trade-offs. They could calibrate start times to minimize the number of buses required and save as much money as possible. But fewer parents would be happy with the results.
Goal 1 = Minimize Cost: 455 BUSES OPERATED, 37% PARENTS SATISFIED, 56% HS STUDENTS START LATER, $14.5M MINIMUM SAVINGS
The district could start every high schooler after 8 a.m., and give more elementary and middle school parents start times they’d be happy with. But that would require more buses and higher costs. Goal 2 = 935 BUSES OPERATED, 56% PARENTS SATISFIED, 100% HS STUDENTS START LATER, $33.5M MINIMUM COST
Ultimately, officials picked a solution, from one of thousands generated by the algorithm, that attempted to balance several goals — student health, cost savings, and parental happiness. But opponents said the formula had some big blind spots.
Goal 3 = 480 BUSES OPERATED, 40% PARENTS SATISFIED, 94% HS STUDENTS START LATER, $12M MINIMUM SAVINGS
District officials expected some pushback when they released the new school schedule on a Thursday night in December, with plans to implement in the fall of 2018. After all, they’d be messing with the schedules of families all over the city.
But no one anticipated the crush of opposition that followed. Angry parents signed an online petition and filled the school committee chamber, turning the plan into one of the biggest crises of Mayor Marty Walsh’s tenure. The city summarily dropped it. The failure would eventually play a role in the superintendent’s resignation.
Just before the release of the new bell times, the school committee laid out the algorithm’s four guiding principles: increase the number of high school students starting school after 8 a.m.; decrease the number of elementary school students dismissed after 4 p.m., so they wouldn’t have to travel home in the dark; accommodate the needs of special education students wherever possible; and generate transportation savings that could be reinvested in the schools.
But in retrospect, it’s clear that the school officials who communicated with the public about the algorithm fell short in at least one crucial respect.
Big districts stagger their start times so a single fleet of buses can serve every school: dropping off high school students early in the morning, then circling back to get the elementary and middle school kids.
If you’re going to push high school start times back, then you’ve probably got to move a lot of elementary and middle schools into earlier time slots. The district knew that going in, and officials dutifully quizzed thousands of parents and teachers at every grade level about their preferred start times.
But they never directly confronted constituents with the sort of dramatic change the algorithm would eventually propose — shifting school start times at some elementary schools by as much as two hours. Even more.
"Hundreds of families were facing a 9:30 to 7:15 a.m. shift. And for many, that was intolerable. They’d have to make major changes to work schedules or even quit their jobs. And because their kids would have to go to bed so early, they’d miss out on valuable family time in the evening.
And a couple of days after the school committee meeting, the NAACP and the Lawyers’ Committee for Civil Rights and Economic Justice actually came out against the plan.
Even if the algorithm promised to reduce inequities, the upheaval involved — with nearly 85 percent of the district getting new start times — would hit black and brown families especially hard, the groups argued.
“We know our parents of color are disproportionately likely to have lower-wage jobs that will make it harder for them to change schedules to meet the new demands of BPS, let alone pay more money for additional child care after school,” said Matt Cregor, education project director at the Lawyers’ Committee, in an interview with the Globe."
While optimisation algorithms are used, the methodology used to route the buses does not appear to constitute "machine learning" as there is no evidence the algorithms learned from the supplied data. Rather the case study falls under traditional AI methods of planning and analytics.
"Years of research have shown that teenagers need their sleep. Yet high schools often start very early in the morning. Starting them later in Boston would require tinkering with elementary and middle school schedules, too — a Gordian knot of logistics, pulled tight by the weight of inertia, that proved impossible to untangle.
1 novemtrigintillion options of bus routes and start times