AI has already proven its potential for tremendous good. But there are important issues that need to be addressed.
“AI is not an ethical thinker,” said Delaware State’s Tawiah. “But it is. You have to control it. AI will hallucinate. It should complement your work and not do your work for you.”
Yonamine reinforced these observations, noting that this is an area where academia must “carry the flag.”
The relentless pursuit of the truth is a core area that research universities must do, he said, before adding that “universities must hold themselves to a higher standard.”
Certain tensions arise when academia, industry and/or government agencies work together. Research timelines often extend well beyond company deadlines and it can be uncomfortable when research points to problems within industry or government practices. Access to good data is another bottleneck.
“Research is separate from data ownership,” says Tawiah. “And if you have two partners – one small and one giant – the giant will be in control. I don’t know if the industry will allow academics to have that power.”
The problem of data sharing was already a difficult challenge before the advent of AI, Shilton said.
“When we worked with Meta, they were very serious about allowing academics to study elections,” she said. “Yet sharing data was very difficult.”
The commitment to collaboration and mutual benefit for researchers and industry is easy to accept and applaud, UD’s Powers said.
“The hardest part comes when the industry gets a message they don’t want to hear,” he said. “Since I have a temporary internal investigator who brings you bad news, I’m afraid there isn’t much tolerance for that kind of news.”
Yonamine said he could see an increasingly tight world, where everyone works in their own interest and “no one asks what is best for the consumer in the short, medium and long term?” Academic research is an important way to address some of this, 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 says. “Maybe we should find ways to encourage that.”
On the other hand, Shilton noted that although students are taught ethics from the very beginning, many enter the job market and are confronted with a boss who says, “No. Build it. ”
Where do you draw the ethical lines?
“I like to think that ethics occupies the space between public opinion and the law,” Powers said. “In some ways this can be a predictor of the law or changes in the law through policy. Ethical reasoning is accessible to anyone who takes the time to read and think. It is not written into law in the way that this question (does this violate copyright or not?) is. That is not true in ethics. It is difficult. The outcomes are often uncertain. In a university setting, that skill is practiced or at least identified.”
The public expects researchers to adhere to high ethical standards, Shilton said.
‘But do you know how to guide that?’ she said. “When you’re trying to figure out AI guidelines, questions about data ethics almost always end with ‘it depends.’ Where did you get the dataset from? How do you plan to use it?”
Should users have the full recipe behind an AI menu? Do they need to know all the ingredients and how they are obtained and selected? Are ethics baked into every dish or is it offered à la carte? Who controls all that?
The questions seem endless. And that is an important reason to have many perspectives in the development of AI systems.
Diverse teams bring broader knowledge and experience, which helps hold teams accountable, Shilton said.
Diverse perspectives
The symposium attracted participants from various fields.
Laurie Christianson, a computational chemist and data scientist, said she was glad she attended.
“I look at large datasets of chemical and biological screening data, analyze it and use it to drive better product concepts and research decisions,” says Christianson, Associate Global R&D Fellow at FMC, an agricultural science company. “It’s interesting to see so much focus on ethical considerations. Concerns are clearly raised. These are very powerful tools. It feels hopeful that people are focusing on these elements more than just data, data, data and push, push, push.”
Abdourahim Sylla, a junior computer science major at Lincoln University, was one of them those who submitted posters for the event. He loves exploring data science and the potential benefits AI brings to so many aspects of research.
