May 22, 2024
Mutale Nkonde's nonprofit is working to make AI less biased

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.

Mutale Nkonde is the founding CEO of the nonprofit AI for the People (AFP), which seeks to increase the amount of Black voices in tech. Before this, she helped introduce the Algorithmic and Deep Fakes Algorithmic Acts, in addition to the No Biometric Barriers to Housing Act, to the U.S. House of Representatives. She is currently a Visiting Policy Fellow at the Oxford Internet Institute.

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

I started to become curious about how social media worked after a friend of mine posted that Google Pictures, the precursor to Google Image, labeled two Black people as gorillas in 2015. I was involved with a lot of “Blacks in tech” circles, and we were outraged, but I did not begin to understand this was because of algorithmic bias until the publication of “Weapons of Math Destruction” in 2016. This inspired me to start applying for fellowships where I could study this further and ended with my role as a co-author of a report called Advancing Racial Literacy in Tech, which was published in 2019. This was noticed by folks at the McArthur Foundation and kick-started the current leg of my career.

I was attracted to questions about racism and technology because they seemed under-researched and counterintuitive. I like to do things other people do not, so learning more and disseminating this information within Silicon Valley seemed like a lot of fun. Since Advancing Racial Literacy in Tech I have started a nonprofit called AI for the People that focuses on advocating for policies and practices to reduce the expression of algorithmic bias.

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

I am really proud of being the leading advocate of the Algorithmic Accountability Act, which was first introduced to the House of Representatives in 2019. It established AI for the People as a key thought leader around how to develop protocols to guide the design, deployment and governance of AI systems that comply with local nondiscrimination laws. This has led to us being included in the Schumer AI Insights Channels as part of an advisory group for various federal agencies and some exciting upcoming work on the Hill.

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

I have actually had more issues with academic gatekeepers. Most of the men I work with in tech companies have been charged with developing systems for use on Black and other nonwhite populations, and so they have been very easy to work with. Principally because I am acting as an external expert who can either validate or challenge existing practices.

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

Find a niche and then become one of the best people in the world at it. I had two things that have helped me build credibility. The first was I was advocating for policies to reduce algorithmic bias, while people in academia began to discuss the issue. This gave me a first-mover advantage in the “solutions space” and made AI for the People an authority on the Hill five years before the executive order. The second thing I would say is look at your deficiencies and address them. AI for the People is four years old and I have been gaining the academic credentials I need to ensure I am not pushed out of thought leader spaces. I cannot wait to graduate with a Masters from Columbia in May and hope to continue researching in this field.

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

I am thinking heavily about the strategies that can be pursued to involve more Black and people of color in the building, testing and annotating of foundational models. This is because the technologies are only as good as their training data, so how do we create inclusive datasets at a time that DEI is being attacked, Black venture funds are being sued for targeting Black and female founders, and Black academics are being publicly attacked, who will do this work in the industry?

What are some issues AI users should be aware of?

I think we should be thinking about AI development as a geopolitical issue and how the United States could become a leader in truly scalable AI by creating products that have high efficacy rates on people in every demographic group. This is because China is the only other large AI producer, but they are producing products within a largely homogenous population, and even though they have a large footprint in Africa. The American tech sector can dominate that market if aggressive investments are made into developing anti-bias technologies.

What is the best way to responsibly build AI?

There needs to be a multi-prong approach, but one thing to consider would be pursuing research questions that center on people living on the margins of the margins. The easiest way to do this is by taking notes of cultural trends and then considering how this impacts technological development. For example, asking questions like how do we design scalable biometric technologies in a society where more people are identifying as trans or nonbinary?

How can investors better push for responsible AI?

Investors should be looking at demographic trends and then ask themselves will these companies be able to sell to a population that is increasingly becoming more Black and brown because of falling birth rates in European populations across the globe? This should prompt them to ask questions about algorithmic bias during the due diligence process, as this will increasingly become an issue for consumers.

There is so much work to be done on reskilling our workforce for a time when AI systems do low-stakes labor-saving tasks. How can we make sure that people living at the margins of our society are included in these programs? What information can they give us about how AI systems work and do not work from them, and how can we use these insights to make sure AI truly is for the people?

Source link