This summer, the American Association of Port Authorities (AAPA) convened members from across the country for its Smart Ports Seminar and Expo in Seattle, to have a conversation on how ports are integrating technology and emerging tech like AI and machine learning into their operations.
I was invited to deliver a main stage conversation. When it comes to these emerging technologies, as a commissioner this is what I keep in mind and encourage my peers to consider:
- If your agency wants to be on the leading edge of adopting these technologies, the smart investment is in your people; and
- Technology is value neutral — it’s neither inherently good nor bad. As a leader, you must ensure that it’s implemented in ways that reflect your organization’s values.
A smart port isn’t a port without people, but one that uses its people better. AI and related technology should first and foremost support safety, grow productivity, and engage workers. All of this makes ports more competitive, and it can make us a more desirable employer or place to do business. I see AI, machine learning, and automation as ways to augment work to make jobs safer and to increase productivity, never to supplant workers.
We have an example of machine learning being used at the Port of Seattle: our kelp forest research in partnership with the Seattle Aquarium. Together we are researching and mapping the presence of kelp forests along the urban waterfront and our east and west waterways around Seattle. Survey work done by scuba divers is augmented by AI and remotely operated vehicles. This approach allows greater coverage to collect data which then gets verified by divers and staff.
This technology allows us to better understand the natural resources in and around port facilities; we use machine learning and AI to recognize what’s kelp, what’s crab, what’s fish. Port and Aquarium staff and volunteers are training the algorithms to identify kelp more accurately, so we can be more productive and efficient in our research. It’s a great example of how technology should be adopted and deployed.
In four years, we have made big strides in advancing our understanding of kelp. We have not just advanced the work but built more relationships and partnerships to do this work. We have brought smaller organizations into our kelp research partnership, putting them in the spotlight and helping them secure funding. The growth of this research would not be possible without smart staff leading the work to lean into research opportunities, create partnerships, identify applicable technology, and develop ways to deploy it.
Emerging technologies must also be viewed through an equity lens. These tools deliver results only as good as the data we input, following the principle of “garbage in, garbage out.” For example, we need to check for biases when developing biometrics. A National Institute of Standards and Technology study found that facial recognition algorithms falsely identified African American and Asian faces 10-100x more than Caucasian faces, resulting in people of color being subjected to more secondary screenings than white people. Analysis pointed to this being an issue with the datasets used for machine learning, not the technology itself. When technology fails to be inclusive because of how it is programmed or deployed, it can replicate and uphold biases that have negative impacts for people. For me as an elected official, that is not technology worth investing in or pursuing.
I’ve shared about the ways that AI and related technology should support jobs and workers. But the adoption of these technologies will also create job opportunities for people who can support the technology, optimizing the impact and capabilities, and also provide oversight, because the impacts of technology can be nuanced or unclear. Our successful use of these technologies will require having people who can work with data and understand its limitations and capacities. We will also need smart analysts who can make sense of the technology used to collect data or model scenarios, to extract the “So what?” and guide decision-making to enhance safety and productivity on the job.
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