Their discussion comes in the wake of recent research suggesting women have left the workforce at a record pace last year. Kaniura said part of her responsibility as a female leader is to address the backlash against diversity, equity, and inclusion (DEI) principles and to “fully empower” female leaders.
“We are thinking about how we can help the next generation of women leaders stay in the workforce and fight for what they deserve today,” she said. “We have a big responsibility now.”
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Panelists said a big part of their leadership role will be to address concerns young people feel about introducing AI into the workplace. An April Gallup poll found that while 51% of U.S. Gen Zers use AI at least once a week, negative sentiment toward the technology has skyrocketed. 31% of Gen Z are openly angry about AI, up from 22% last year.
“This is part of the challenge for us as business owners to train new employees within our company to use AI so they can benefit from all the great efficiencies it brings,” Kim said. “But we also need to make sure we keep things relevant. people to do. ”
Shi said young people should be encouraged to engage with AI through education, and that the “anger” Gen Z feels towards AI will not help them find jobs in a cutthroat market.
“They’re not taking advantage of it. They’re intentionally not learning. It’s really not in their own interest or our society’s benefit,” Shi said. “We need to change the narrative and convince more young people to take ownership of their future, because AI is neither inherently good nor bad. It all depends on how we treat it.”
However, panelists acknowledged that the industry’s lack of inclusivity and use of biased datasets has left marginalized communities distrustful of AI. Shih believes that to foster inclusivity, representation needs to be present at “every step” of AI model development.
“It is paramount that there is representation at each stage, because the model codifies any bias that exists there. This is absolutely important because it creates a situation where inequality increases over time,” she said.
In the pharmaceutical industry, this comes down to datasets representing people of all races, genders and backgrounds, but this is not necessarily the case in clinical trials, Kim said.
“Historically, it’s been very, very lopsided. [to] “It’s white men who respond to all diseases. This is an important factor to look at when enrolling in clinical trials. So the underlying data used to train all AI algorithms in terms of disease detection, treatment, etc. includes representative data,” Kim said.
