A neural network learns when it should not be trusted A faster way to estimate uncertainty in AI-assisted decision-making could lead to safer outcomes.

MIT researchers have developed a way for deep learning neural
networks to rapidly estimate confidence levels in their output. The
advance could enhance safety and efficiency in AI-assisted decision
making.     Image: iStock image edited by MIT News

Increasingly, artificial intelligence systems known as deep
learning neural networks are used to inform decisions vital to
human health and safety, such as in autonomous driving or medical
diagnosis. These networks are good at recognizing patterns in
large, complex datasets to aid in decision-making. But how do we
know they’re correct? Alexander Amini and his colleagues at MIT
and Harvard University wanted to find out.

They’ve developed a quick way for a neural network to crunch
data, and output not just a prediction but also the model’s
confidence level based on the quality of the available data. The
advance might save lives, as deep learning is already being
deployed in the real world today. A network’s level of certainty
can be the difference between an autonomous vehicle determining
that “it’s all clear to proceed through the intersection” and
“it’s probably clear, so stop just in case.” 

Current methods of uncertainty estimation for neural networks
tend to be computationally expensive and relatively slow for
split-second decisions. But Amini’s approach, dubbed “deep
evidential regression,” accelerates the process and could lead to
safer outcomes. “We need the ability to not only have
high-performance models, but also to understand when we cannot
trust those models,” says Amini, a PhD student in Professor
Daniela Rus’ group at the MIT Computer Science and Artificial
Intelligence Laboratory (CSAIL).

“This idea is important and applicable broadly. It can be used
to assess products that rely on learned models. By estimating the
uncertainty of a learned model, we also learn how much error to
expect from the model, and what missing data could improve the
model,” says Rus.

Amini will present the research at next month’s NeurIPS
conference, along with Rus, who is the Andrew and Erna Viterbi
Professor of Electrical Engineering and Computer Science, director
of CSAIL, and deputy dean of research for the MIT Stephen A.
Schwarzman College of Computing; and graduate students Wilko
Schwarting of MIT and Ava Soleimany of MIT and Harvard.

Efficient uncertainty

After an up-and-down
, deep learning has demonstrated remarkable performance
on a variety of tasks, in some cases even surpassing human
accuracy. And nowadays, deep learning seems to go wherever
computers go. It fuels search engine results, social media feeds,
and facial recognition. “We’ve had huge successes using deep
learning,” says Amini. “Neural networks are really good at
knowing the right answer 99 percent of the time.” But 99 percent
won’t cut it when lives are on the line.

“One thing that has eluded researchers is the ability of these
models to know and tell us when they might be wrong,” says Amini.
“We really care about that 1 percent of the time, and how we can
detect those situations reliably and efficiently.”

Neural networks can be massive, sometimes brimming with billions
of parameters. So it can be a heavy computational lift just to get
an answer, let alone a confidence level. Uncertainty analysis in
neural networks isn’t new. But previous approaches, stemming from
Bayesian deep learning, have relied on running, or sampling, a
neural network many times over to understand its confidence. That
process takes time and memory, a luxury that might not exist in
high-speed traffic.

The researchers devised a way to estimate uncertainty from only
a single run of the neural network. They designed the network with
bulked up output, producing not only a decision but also a new
probabilistic distribution capturing the evidence in support of
that decision. These distributions, termed evidential
distributions, directly capture the model’s confidence in its
prediction. This includes any uncertainty present in the underlying
input data, as well as in the model’s final decision. This
distinction can signal whether uncertainty can be reduced by
tweaking the neural network itself, or whether the input data are
just noisy.

Confidence check

To put their approach to the test, the researchers started with
a challenging computer vision task. They trained their neural
network to analyze a monocular color image and estimate a depth
value (i.e. distance from the camera lens) for each pixel. An
autonomous vehicle might use similar calculations to estimate its
proximity to a pedestrian or to another vehicle, which is no simple

Their network’s performance was on par with previous
state-of-the-art models, but it also gained the ability to estimate
its own uncertainty. As the researchers had hoped, the network
projected high uncertainty for pixels where it predicted the wrong
depth. “It was very calibrated to the errors that the network
makes, which we believe was one of the most important things in
judging the quality of a new uncertainty estimator,” Amini

To stress-test their calibration, the team also showed that the
network projected higher uncertainty for “out-of-distribution”
data — completely new types of images never encountered during
training. After they trained the network on indoor home scenes,
they fed it a batch of outdoor driving scenes. The network
consistently warned that its responses to the novel outdoor scenes
were uncertain. The test highlighted the network’s ability to
flag when users should not place full trust in its decisions. In
these cases, “if this is a health care application, maybe we
don’t trust the diagnosis that the model is giving, and instead
seek a second opinion,” says Amini.

The network even knew when photos had been doctored, potentially
hedging against data-manipulation attacks. In another trial, the
researchers boosted adversarial noise levels in a batch of images
they fed to the network. The effect was subtle — barely
perceptible to the human eye — but the network sniffed out those
images, tagging its output with high levels of uncertainty. This
ability to sound the alarm on falsified data could help detect and
deter adversarial attacks, a growing concern in the age of deepfakes.

Deep evidential regression is “a simple and elegant approach
that advances the field of uncertainty estimation, which is
important for robotics and other real-world control systems,”
says Raia Hadsell, an artificial intelligence researcher at
DeepMind who was not involved with the work. “This is done in a
novel way that avoids some of the messy aspects of other approaches
—  e.g. sampling or ensembles — which makes it not only elegant
but also computationally more efficient — a winning

Deep evidential regression could enhance safety in AI-assisted
decision making. “We’re starting to see a lot more of these
[neural network] models trickle out of the research lab and into
the real world, into situations that are touching humans with
potentially life-threatening consequences,” says Amini. “Any
user of the method, whether it’s a doctor or a person in the
passenger seat of a vehicle, needs to be aware of any risk or
uncertainty associated with that decision.” He envisions the
system not only quickly flagging uncertainty, but also using it to
make more conservative decision making in risky scenarios like an
autonomous vehicle approaching an intersection.

“Any field that is going to have deployable machine learning
ultimately needs to have reliable uncertainty awareness,” he

Originally published by
Daniel Ackerman | MIT News
| November 20, 2020

This work was supported, in part, by the National Science
Foundation and Toyota Research Institute through the Toyota-CSAIL
Joint Research Center.