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Women in AI: Claire Leibowicz, AI and media integrity expert at PAI

Image Credits: TechCrunch

To give AI-focused women academics and others their well-deserved -- and overdue -- time in the spotlight, TechCrunch is launching a series of interviews focusing on remarkable women who’ve contributed to the AI revolution. We’ll publish several pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here.

Claire Leibowicz is the head of the AI and media integrity program at the Partnership on AI (PAI), the industry group backed by Amazon, Meta, Google, Microsoft and others committed to the "responsible" deployment of AI tech. She also oversees PAI’s AI and media integrity steering committee.

In 2021, Leibowicz was a journalism fellow at Tablet Magazine, and in 2022, she was a fellow at the Rockefeller Foundation’s Bellagio Center focused on AI governance. Leibowicz -- who holds a BA in psychology and computer science from Harvard and a master’s degree from Oxford -- has advised companies, governments and nonprofit organizations on AI governance, generative media and digital information.

Q&A

Briefly, how did you get your start in AI? What attracted you to the field?

It may seem paradoxical, but I came to the AI field from an interest in human behavior. I grew up in New York, and I was always captivated by the many ways people there interact and how such a diverse society takes shape. I was curious about huge questions that affect truth and justice, like how do we choose to trust others? What prompts intergroup conflict? Why do people believe certain things to be true and not others? I started out exploring these questions in my academic life through cognitive science research, and I quickly realized that technology was affecting the answers to these questions. I also found it intriguing how artificial intelligence could be a metaphor for human intelligence.

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That brought me into computer science classrooms where faculty -- I have to shout out Professor Barbara Grosz, who is a trailblazer in natural language processing, and Professor Jim Waldo, who blended his philosophy and computer science background -- underscored the importance of filling their classrooms with non-computer science and -engineering majors to focus on the social impact of technologies, including AI. And this was before "AI ethics" was a distinct and popular field. They made clear that, while technical understanding is beneficial, technology affects vast realms, including geopolitics, economics, social engagement and more, thereby requiring people from many disciplinary backgrounds to weigh in on seemingly technological questions.

Whether you’re an educator thinking about how generative AI tools affect pedagogy, a museum curator experimenting with a predictive route for an exhibit or a doctor investigating new image detection methods for reading lab reports, AI can impact your field. This reality, that AI touches many domains, intrigued me: There was intellectual variety inherent to working in the AI field, and this brought with it a chance to impact many facets of society.

What work are you most proud of in the AI field?

I’m proud of the work in AI that brings disparate perspectives together in a surprising and action-oriented way -- that not only accommodates, but [also] encourages, disagreement. I joined the PAI as the organization’s second staff member six years ago and sensed right away the organization was trailblazing in its commitment to diverse perspectives. PAI saw such work as a vital prerequisite to AI governance that mitigates harm and leads to practical adoption and impact in the AI field. This has proven true, and I have been heartened to help shape PAI’s embrace of multidisciplinarity and watch the institution grow alongside the AI field.

Our work on synthetic media over the past six years started well before generative AI became part of the public consciousness, and exemplifies the possibilities of multistakeholder AI governance. In 2020, we worked with nine different organizations from civil society, industry and media to shape Facebook’s Deepfake Detection Challenge, a machine learning competition for building models to detect AI-generated media. These outside perspectives helped shape the fairness and goals of the winning models -- showing how human rights experts and journalists can contribute to a seemingly technical question like deepfake detection. Last year, we published a normative set of guidance on responsible synthetic media -- PAI’s Responsible Practices for Synthetic Media -- that now has 18 supporters from extremely different backgrounds, ranging from OpenAI to TikTok to Code for Africa, Bumble, BBC and WITNESS. Being able to put pen to paper on actionable guidance that is informed by technical and social realities is one thing, but it’s another to actually get institutional support. In this case, institutions committed to providing transparency reports about how they navigate the synthetic media field. AI projects that feature tangible guidance, and show how to implement that guidance across institutions, are some of the most meaningful to me.

How do you navigate the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry?

I have had both wonderful male and female mentors throughout my career. Finding people who simultaneously support and challenge me is key to any growth I have experienced. I find that focusing on shared interests and discussing the questions that animate the field of AI can bring people with different backgrounds and perspectives together. Interestingly, PAI’s team is made up of more than half women, and many of the organizations working on AI and society or responsible AI questions have many women on staff. This is often in contrast to those working on engineering and AI research teams and is a step in the right direction for representation in the AI ecosystem.

What advice would you give to women seeking to enter the AI field?

As I touched on in the previous question, some of the primarily male-dominated spaces within AI that I have encountered have also been those that are the most technical. While we should not prioritize technical acumen over other forms of literacy in the AI field, I have found that having technical training has been a boon to both my confidence and effectiveness in such spaces. We need equal representation in technical roles and an openness to the expertise of folks who are experts in other fields like civil rights and politics that have more balanced representation. At the same time, equipping more women with technical literacy is key to balancing representation in the AI field.

I have also found it enormously meaningful to connect with women in the AI field who have navigated balancing family and professional life. Finding role models to talk to about big questions related to career and parenthood -- and some of the unique challenges women still face at work -- has made me feel better equipped to handle some those challenges as they arise.

What are some of the most pressing issues facing AI as it evolves?

The questions of truth and trust online -- and offline -- become increasingly tricky as AI evolves. As content ranging from images to videos to text can be AI-generated or modified, is seeing still believing? How can we rely on evidence if documents can easily and realistically be doctored? Can we have human-only spaces online if it’s extremely easy to imitate a real person? How do we navigate the trade-offs that AI presents between free expression and the possibility that AI systems can cause harm? More broadly, how do we ensure the information environment is not only shaped by a select few companies and those working for them but [also] incorporates the perspectives of stakeholders from around the world, including the public?

Alongside these specific questions, PAI has been involved in other facets of AI and society, including how we consider fairness and bias in an era of algorithmic decision-making, how labor impacts and is impacted by AI, how to navigate responsible deployment of AI systems and even how to make AI systems more reflective of myriad perspectives. At a structural level, we must consider how AI governance can navigate vast trade-offs by incorporating varied perspectives.

What are some issues AI users should be aware of?

First, AI users should know that if something sounds too good to be true, it probably is.

The generative AI boom over the past year has, of course, reflected enormous ingenuity and innovation, but it has also led to public messaging around AI that is often hyperbolic and inaccurate.

AI users should also understand that AI is not revolutionary, but exacerbating and augmenting existing problems and opportunities. This does not mean they should take AI less seriously, but rather use this knowledge as a helpful foundation for navigating an increasingly AI-infused world. For example, if you are concerned about the fact that people could miscontextualize a video before an election by changing the caption, you should be concerned about the speed and scale at which they can mislead using deepfake technology. If you are concerned about the use of surveillance in the workplace, you should also consider how AI will make such surveillance easier and more pervasive. Maintaining a healthy skepticism about the novelty of AI problems, while also being honest about what is distinct about the current moment, is a helpful frame for users to bring to their encounters with AI.

What is the best way to responsibly build AI?

Responsibly building AI requires us to broaden our notion of who plays a role in "building" AI. Of course, influencing technology companies and social media platforms is a key way to affect the impact of AI systems, and these institutions are vital to responsibly building technology. At the same time, we must acknowledge how diverse institutions from across civil society, industry, media, academia and the public must continue to be involved to build responsible AI that serves the public interest.

Take, for example, the responsible development and deployment of synthetic media.

While technology companies might be concerned about their responsibility when navigating how a synthetic video can influence users before an election, journalists may be worried about imposters creating synthetic videos that purport to come from their trusted news brand. Human rights defenders might consider responsibility related to how AI-generated media reduces the impact of videos as evidence of abuses. And artists might be excited by the opportunity to express themselves through generative media, while also being concerned about how their creations might be leveraged without their consent to train AI models that produce new media. These diverse considerations show how vital it is to involve different stakeholders in initiatives and efforts to responsibly build AI, and how myriad institutions are affected by -- and affecting -- the way AI is integrated into society.

How can investors better push for responsible AI?

Years ago, I heard DJ Patil, the former chief data scientist in the White House, describe a revision to the pervasive "move fast and break things" mantra of the early social media era that has stuck with me. He suggested the field "move purposefully and fix things."

I loved this because it didn’t imply stagnation or an abandonment of innovation, but intentionality and the possibility that one could innovate while embracing responsibility. Investors should help induce this mentality -- allowing more time and space for their portfolio companies to bake in responsible AI practices without stifling progress. Oftentimes, institutions describe limited time and tight deadlines as the limiting factor for doing the "right" thing, and investors can be a major catalyst for changing this dynamic.

The more I have worked in AI, the more I have found myself grappling with deeply humanistic questions. And these questions require all of us to answer them.