AI already has proven its potential for tremendous good. But there are important problems to address.
“AI is not an ethical thinker,” Delaware State’s Tawiah said. “But you are. You need to check. AI will hallucinate. It should supplement your work, not do your work for you.”
Yonamine reinforced these observations, noting that this is an area where academia needs to “carry the flag.”
The ruthless pursuit of the truth is a core area that research universities must do, he said, before adding that “universities need to hold themselves to a higher standard.”
There are certain tensions that arise when academia, industry and/or government entities collaborate. Research timelines often extend much farther than corporate deadlines and it can be uncomfortable when research points to problems within industry or government practices. Access to good data is another sticking point.
“Research is independent of data ownership,” Tawiah said. “And when you have two partners — a small one and a giant one — the giant is going to have control. I don’t know if industry will allow academics to have that power.”
The data-sharing problem was a hard challenge even before AI emerged, Shilton said.
“When we worked with Meta, they were very serious about allowing academics to study elections,” she said. “Yet the data sharing was very hard.”
Aiming for collaboration and the mutual benefit to researchers and industry is easy to accept and cheer, UD’s Powers said.
“The hard part comes when industry gets a message they don’t want to hear,” he said. “Having a temporary in-house researcher who gives you bad news — I worry there is not a lot of tolerance for those kinds of messages.”
Yonamine said he could see a world where it gets tighter, with everyone working in their own interests and “no one is asking what’s best for the consumer in the short-, medium- and long term?” Academic research is an important way to address some of that, he said.
Holtman said he sees many junior data scientists who are highly motivated to deal with ethical issues.
“They believe in it and really want to contribute,” he said. “Maybe we need to find ways to incentivize that.”
On the other hand, Shilton noted that while students are taught ethics from the “get-go,” many go into the workforce and face a boss that says, ‘No. Build it.’”
Where do you draw the ethical boundaries?
“I like to think ethics occupies the space between public opinion and the law,” Powers said. “In some ways, this can be predictive of law or changes in law by policy. Ethical reasoning is accessible to everyone who takes the time to read and think. It’s not written down in law the way this question — does this violate copyright or not? — is. That’s not true in ethics. It’s hard. The outcomes are often uncertain. In a university setting, that skill is practiced or at least identified.”
The public expects researchers to hold themselves to high ethical standards, Shilton said.
“But finding out how to guide it?” she said. “When trying to figure out AI guidelines, data ethics questions almost always wind up with ‘it depends.’ Where did you get the dataset? How do you plan to use it?”
Do users need to have the full recipe behind an AI menu? Do they need to know all the ingredients, how they were sourced and selected? Are ethics baked into every dish or offered à la carte? Who monitors all of that?
The questions seem endless. And that’s an important reason to have many perspectives in the development of AI systems.
Diverse teams bring broader knowledge and experience and that helps to keep teams accountable, Shilton said.
Diverse perspectives
The symposium drew participants from diverse fields.
Laurie Christianson, a computational chemist and data scientist, said she was glad she attended.
“I look at large datasets of chemistry and biological screening data, analyze it and use it to come up with better product concepts and research decisions,” said Christianson, Associate Global R&D Fellow with FMC, an agricultural sciences firm. “It’s interesting to see so much focus on ethical considerations. Concerns are clearly coming to the forefront. These are very powerful tools. It feels hopeful that people are focused more on these elements rather than just data, data, data and push, push, push.”
Abdourahim Sylla, a junior computer science major at Lincoln University, was among those who submitted posters for the event. He loves to explore data science and the potential benefits AI brings to so many aspects of research.
