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As part of TechCrunch’s ongoing Women in AI series, which seeks to give AI-focused female academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch interviewed Dr. Rebecca Portnoff, who serves as Vice President of Data Science at Non-profit organization forkwhich builds technology to protect children from sexual abuse.
She attended Princeton University before earning her PhD in computer science from the University of California, Berkeley. She’s working her way up the ladder at Thorne, where she has worked since 2016. She started as a volunteer research scientist, and now, eight years later, she leads a team that may be one of the only teams in the world dedicated to building machines. Learning and artificial intelligence to stop, prevent and defend child sexual abuse.
“During my senior year at Princeton, while I was thinking about what I would do after graduation, my sister recommended I read the book Half the Sky by Nicholas Kristof and Cheryl Wooden, which introduced me to the topic of child sexual abuse,” she told TechCrunch. Saying that the book inspired her to study how to make a difference in this field. She went on to write her doctoral thesis with a particular focus on the use of machine learning and artificial intelligence in this field.
The mission of protecting children
At Thorn, Portnoff’s team helps identify victims, stop revictimization, and prevent the viral spread of sexual assault material. She led Thorn and All Tech Is Human’s joint Safety by Design initiative last year, which strives to prevent people from using generative AI to sexually harm children.
“It was a tremendous push, collaboratively defining principles and mitigation measures to prevent generative models from producing material of abuse, make detection of that material more reliable, and prevent the distribution of those models, services and applications used to produce that material of abuse,” she noted, and then aligned industry leaders. To adhere to these standards, she said she has met many people dedicated to the cause, “but I also have more gray hair than at the beginning of the whole thing.”
The use of AI to create non-consensual sexual images has become a topic of great debate, especially as porn AI generations become more sophisticated, as TechCrunch previously reported. There is currently no Comprehensive federal law In place it protects or prohibits AI sexual images created of other people without their consent, although individual states, such as Florida, Louisiana, and New Mexico, have passed their own legislation to specifically target AI child abuse.
In fact, she said this is one of the most pressing issues facing AI as it develops. “One in 10 minors reported that they were aware of instances where their peers had generated nude images of other children,” she said.
“We don’t have to live in this reality, and it’s unacceptable that we’ve allowed it to get to this point already.” She said there are mitigating measures that can be put in place to prevent and reduce misuse. For example, Thorne calls for technology companies to embrace safety-by-design principles and mitigation measures, and publicly share how they prevent their generative AI technologies and products from being misused to promote child sexual abuse, in collaboration with professional organizations like Amnesty International. The Institute of Electrical and Electronics Engineers (IEEE) and the National Institute of Standards and Technology (NIST) to support the development of standards that companies can use to review progress, as well as engage with policymakers to communicate how important this is.
“Impact-focused legislation will be essential to include all businesses and stakeholders,” she said.
Working as a woman in artificial intelligence
As she moved up the career ladder in building AI, Portnov recalls people ignoring her advice, asking instead to speak with someone with a technical background. “My answer? Don’t worry, you’re talking to someone with a technical background,” she said.
She said a few things helped her navigate working in such a male-dominated field: being prepared, acting confident, and assuming good intentions. She added that being prepared helps her enter rooms with greater confidence, while confidence allows her to overcome challenges with curiosity and boldness, “striving first to understand and then to be understood.”
“Assuming good faith helps me deal with challenges with grace rather than defensiveness,” she said. “If that good will isn’t really there, it will eventually show up.”
Her advice to women wanting to get into AI is to always believe in your ability and its meaning. She said it’s easy to fall into the trap of letting people’s assumptions about you determine your potential, but everyone’s voice will be needed in today’s AI revolution.
“As machine learning/AI becomes more integrated into our human systems, we all need to work together to ensure we do this in a way that advances our collective prosperity and prioritizes the most vulnerable among us.”
Building ethical AI
Portnov said there are many aspects to responsible AI, including the need for transparency, fairness, reliability and safety. “But they all have one thing in common,” she continued. “Building machine learning/AI responsibly requires engaging with a greater number of stakeholders than just your fellow technologists.”
This means more active listening and collaboration. “If you’re following a roadmap for building responsible AI, and you find that you haven’t talked to anyone outside of your organization or engineering team in the process, you’re probably going in the wrong direction.”
As investors continue to pour billions of dollars into AI startups, Portnov suggested that investors could start looking at liability early in the due diligence phase, consider a company’s commitment to ethics before making an investment, and then demand certain standards must be adhered to. With it. meet. This can “prevent damage and enable positive growth.”
“There is a lot of work to be done,” she said, speaking generally. “And you can be the one to make it happen.”
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