Can machine-learning models overcome biased datasets?
Date:
February 21, 2022
Source:
Massachusetts Institute of Technology
Summary:
Researchers have applied the tools of neuroscience to study
when and how an artificial neural network can overcome bias in
a dataset. They found that data diversity, not dataset size,
is key and that the emergence of certain types of neurons during
training plays a major role in how well a neural network is able
to overcome dataset bias.
FULL STORY ========================================================================== Artificial intelligence systems may be able to complete tasks quickly,
but that doesn't mean they always do so fairly. If the datasets used
to train machine- learning models contain biased data, it is likely the
system could exhibit that same bias when it makes decisions in practice.
==========================================================================
For instance, if a dataset contains mostly images of white men, then a
facial- recognition model trained with this data may be less accurate
for women or people with different skin tones.
A group of researchers at MIT, in collaboration with researchers at
Harvard University and Fujitsu, Ltd., sought to understand when and how
a machine- learning model is capable of overcoming this kind of dataset
bias. They used an approach from neuroscience to study how training
data affects whether an artificial neural network can learn to recognize objects it has not seen before. A neural network is a machine-learning
model that mimics the human brain in the way it contains layers of interconnected nodes, or "neurons," that process data.
The new results show that diversity in training data has a major influence
on whether a neural network is able to overcome bias, but at the same
time dataset diversity can degrade the network's performance. They also
show that how a neural network is trained, and the specific types of
neurons that emerge during the training process, can play a major role
in whether it is able to overcome a biased dataset.
"A neural network can overcome dataset bias, which is encouraging. But the
main takeaway here is that we need to take into account data diversity. We
need to stop thinking that if you just collect a ton of raw data, that is
going to get you somewhere. We need to be very careful about how we design datasets in the first place," says Xavier Boix, a research scientist in
the Department of Brain and Cognitive Sciences (BCS) and the Center for
Brains, Minds, and Machines (CBMM), and senior author of the paper.
Co-authors include former graduate students Spandan Madan, a corresponding author who is currently pursuing a PhD at Harvard, Timothy Henry,
Jamell Dozier, Helen Ho, and Nishchal Bhandari; Tomotake Sasaki, a
former visiting scientist now a researcher at Fujitsu; Fre'do Durand,
a professor of electrical engineering and computer science and a member
of the Computer Science and Artificial Intelligence Laboratory; and
Hanspeter Pfister, the An Wang Professor of Computer Science at the
Harvard School of Enginering and Applied Sciences. The research appears
today in Nature Machine Intelligence.
========================================================================== Thinking like a neuroscientist Boix and his colleagues approached the
problem of dataset bias by thinking like neuroscientists. In neuroscience,
Boix explains, it is common to use controlled datasets in experiments,
meaning a dataset in which the researchers know as much as possible
about the information it contains.
The team built datasets that contained images of different objects
in varied poses, and carefully controlled the combinations so some
datasets had more diversity than others. In this case, a dataset had less diversity if it contains more images that show objects from only one
viewpoint. A more diverse dataset had more images showing objects from
multiple viewpoints. Each dataset contained the same number of images.
The researchers used these carefully constructed datasets to train a
neural network for image classification, and then studied how well it
was able to identify objects from viewpoints the network did not see
during training (known as an out-of-distribution combination).
For example, if researchers are training a model to classify cars in
images, they want the model to learn what different cars look like. But if every Ford Thunderbird in the training dataset is shown from the front,
when the trained model is given an image of a Ford Thunderbird shot from
the side, it may misclassify it, even if it was trained on millions of
car photos.
==========================================================================
The researchers found that if the dataset is more diverse -- if more
images show objects from different viewpoints -- the network is better
able to generalize to new images or viewpoints. Data diversity is key
to overcoming bias, Boix says.
"But it is not like more data diversity is always better; there is a
tension here. When the neural network gets better at recognizing new
things it hasn't seen, then it will become harder for it to recognize
things it has already seen," he says.
Testing training methods The researchers also studied methods for training
the neural network.
In machine learning, it is common to train a network to perform multiple
tasks at the same time. The idea is that if a relationship exists between
the tasks, the network will learn to perform each one better if it learns
them together.
But the researchers found the opposite to be true -- a model trained
separately for each task was able to overcome bias far better than a
model trained for both tasks together.
"The results were really striking. In fact, the first time we did this experiment, we thought it was a bug. It took us several weeks to realize
it was a real result because it was so unexpected," he says.
They dove deeper inside the neural networks to understand why this occurs.
They found that neuron specialization seems to play a major role. When the neural network is trained to recognize objects in images, it appears that
two types of neurons emerge -- one that specializes in recognizing the
object category and another that specializes in recognizing the viewpoint.
When the network is trained to perform tasks separately, those specialized neurons are more prominent, Boix explains. But if a network is trained
to do both tasks simultaneously, some neurons become diluted and don't specialize for one task. These unspecialized neurons are more likely to
get confused, he says.
"But the next question now is, how did these neurons get there? You train
the neural network and they emerge from the learning process. No one told
the network to include these types of neurons in its architecture. That
is the fascinating thing," he says.
That is one area the researchers hope to explore with future work. They
want to see if they can force a neural network to develop neurons with
this specialization. They also want to apply their approach to more
complex tasks, such as objects with complicated textures or varied illuminations.
Boix is encouraged that a neural network can learn to overcome bias,
and he is hopeful their work can inspire others to be more thoughtful
about the datasets they are using in AI applications.
This work was supported, in part, by the National Science Foundation,
a Google Faculty Research Award, the Toyota Research Institute, the
Center for Brains, Minds, and Machines, Fujitsu Laboratories Ltd.,
and the MIT-Sensetime Alliance on Artificial Intelligence.
========================================================================== Story Source: Materials provided by
Massachusetts_Institute_of_Technology. Original written by Adam
Zewe. Note: Content may be edited for style and length.
========================================================================== Journal Reference:
1. Spandan Madan, Timothy Henry, Jamell Dozier, Helen Ho, Nishchal
Bhandari,
Tomotake Sasaki, Fre'do Durand, Hanspeter Pfister, Xavier
Boix. When and how CNNs generalize to out-of-distribution
category-viewpoint combinations. Nature Machine Intelligence,
2022 DOI: 10.1038/s42256-021- 00437-5 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/02/220221115403.htm
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