At Google, we recognize the importance of how AI is developed and used. In 2018, we
shared the AI Principles that guide our work and research. As the product manager for the
Bioacoustics team at Google, I relied on the AI Principles to help open source a machine
learning model that can detect marine mammals and could be socially beneficial, built and
tested for safety, while upholding high standards of scientific excellence for the marine biologists
and researchers worldwide. Back in 2019, we partnered up with the National
Oceanic and Atmospheric Administration (also known as NOAA) to train a machine learning
model that could automatically detect humpback whale calls given an audio recording.
Knowing where the whales are at a point in time, can enable researchers to understand
their migration patterns, where they feed, and where to place protected areas to successfully
protect the whales.
The following year, we worked with the Department of Fisheries and Ocean in Canada on a similar
project but this time to help automatically detect the presence of southern resident killer
whales — a critically endangered species of orcas —in real time in the Vancouver
harbor
We realized many other researchers could use these models for important research applications
in the sea — such as better understanding the oceans, or mapping ecosystems, and protecting
key biodiversity areas. So we turned to our AI Principles and submitted an AI Principles
review request.
We were struggling with a big dilemma within our team. On one hand, we knew our model could
be helpful to ocean scientists who have sound files they’d like to analyze. And we knew
that we had the ability to train and improve the model to encompass more than humpback
whales, including other large marine mammals. On the other hand, we were worried that poachers
or pirates might track down and hunt humpback whales or orcas based on the analysis our
model could provide them. What if somehow, bad actors used our model in ways we didn’t
intend? We wanted to do the right thing. We wanted
a consultation with experts in the field in order to help us responsibly open-source this
machine learning model while mitigating potential harms and amplifying social benefit.
The process was thorough and meticulous. One of the reviewers on the Responsible Innovation
Team reached back out to me to explain the process and provide our team with a list of
questions and next steps. Some of these questions included. Would open
sourcing this ML model be socially beneficial? With this model, how are we and our technology
accountable to people? How is this ML model built and tested for safety?
After consulting with the AI Principles Team and discussing this with third-party experts,
it became clear that this risk was low for a variety of reasons including how poachers
and bad actors don’t rely on ML models, furthermore, they would probably not be able
to use these models due to the scientific complexity and data expertise required. After
significant deliberation and consultation, the experts in the field felt that the benefits
outweighed the risk so we chose to go forward with open sourcing.
In evaluating our idea to open-source this model, Google wanted us to “be accountable
to people.” The questions the AI Principles Reviewers asked actually helped us improve
the usability and explainability of our model. They counseled us to release instructions
when we open source the model to help offer user-friendly explanations to researchers.
Once we received guidance and approval from the AI Principle experts, took action to implement
the guidance, we knew we could open source our model to the scientific community.
We really value reviews at any step in our research process. It was great to have the
support of the experts from the AI Principles Review team.
An AI ethics review is definitely an important process to complete, especially when you’re
creating something open-sourced