A conversation with Microsoft’s AI for Good Lab Director: Juan Lavista Ferres

AI is emerging as a powerful tool to aid in addressing some of the world’s most complex challenges. Our new specialization, AI for Good, aims to expand the community of people who are equipped to combine human and machine intelligence to achieve positive outcomes for the planet and its people. 

To design this specialization, we partnered with the Microsoft AI for Good Lab and worked closely with its lab director and chief data scientist, Juan Lavista Ferres

Juan’s work focuses on catalyzing partnerships aimed at addressing the world’s most pressing challenges using AI. His contributions have been published in prestigious academic journals like Pediatrics, and have garnered coverage by esteemed media outlets such as The New York Times, CNN, and over 100 other global news outlets.

Juan recently spoke to us about his role, the innovative applications of AI in addressing social and environmental challenges, and reasons to join the growing AI for Good movement.

How did you first get interested in AI for Good and how did you wind up as lab director at Microsoft’s AI for Good lab?

I have been interested in AI for Good-type work for the majority of my career. Almost 20 years ago, a good friend of mine lost a child to SIDS (sudden infant death syndrome). As a result, I started volunteering with Seattle Children’s Hospital, helping to understand the causes and preventative steps that could be taken to reduce the risk of SIDS. This volunteer engagement led to the opportunity to create the Microsoft AI for Good Lab. I could have never imagined the scope and scale of the work we’re fortunate to do today. From improving health outcomes to biodiversity conservation and humanitarian response, the relevance and need for AI in every sector has never been more pronounced.

What are some of your core responsibilities?

As the Director of the AI for Good Lab, one of my core responsibilities is to ensure, through every project we do, that we’re advancing our mission: “To leverage the power of data, cloud technology, data science talent, and the Microsoft brand to catalyze and inspire others to partner in solving the world’s greatest challenges.” That’s our North Star. Another core responsibility of mine is to help the team, which is dispersed across 3 continents, operate as a cohesive unit. We have such a diversity of experience, background, and skill sets on the team. When we leverage all these individual strengths, we do our best work and have the greatest positive impact possible.

What advice can you give to people who are already working in AI and would like to orient their career towards positive impacts?

Jeff Hammerbacher, Facebooks’ first data scientist, once said, “the best minds of my generation are thinking how to get people to click on ads.”  What I don’t know if Jeff considered is that, even though predicting which children will have higher chances of infant mortality and which people will click on your ads cannot be more different from a societal point of view, from a pure data science perspective, the two problems are very similar. Essentially, you can use the skills you have learned in ways that can help the world. To other data scientists like me, I suggest partnering with organizations that have subject matter expertise to guide data science solutions to important, societal problems.

What advice would you give to people who are working in humanitarian, health, accessibility, and climate fields and would like to incorporate AI into their work?

I’d suggest you do 4 important things:

Collaborate with experts: AI is an interdisciplinary field, and successful integration of AI into your work often requires collaboration with AI experts, data scientists, and technologists. Engage in partnerships with professionals who have expertise in AI to leverage their knowledge and skills effectively. There are a lot of data scientists in industry and academia who would love the opportunity to partner with organizations like yours.

Identify relevant data sources: Data is the foundation of AI. Explore and identify available data sources that are relevant to your field. This can include existing datasets, open data initiatives, or partnerships with organizations that collect relevant data. Ensure that the data you use is diverse, representative, and ethically sourced.

Start small and iterate: Begin with small AI projects or pilots and use them to test and validate the feasibility and potential impact of AI in your work. This approach allows you to learn from initial implementations, make improvements, and build upon successful results. Starting small also helps manage resources effectively and mitigate risks.

Address ethical considerations: AI applications in humanitarian, health, accessibility, and climate fields come with unique ethical considerations. Be mindful of potential biases, fairness, privacy, and security concerns that may arise when working with AI technologies. Ensure that ethical frameworks and guidelines are in place to govern the development and deployment of AI solutions.

What are the most important technical skills for people looking for jobs in AI for good?

There are several key areas of expertise that are highly valuable:

Proficiency in coding and data management: As a data scientist, strong programming skills are essential. Fluency in languages such as Python or R allows for efficient data manipulation, analysis, and model development. Additionally, expertise in data management, including data cleaning, preprocessing, and integration, is crucial for working with diverse and often messy real-world data.

Strong statistical knowledge: A solid understanding of statistics is vital for drawing meaningful insights from data and making informed decisions. Data scientists working in philanthropic capacities should be proficient in statistical techniques, hypothesis testing, regression analysis, and experimental design. This knowledge enables them to identify patterns, detect biases, and evaluate the impact and effectiveness of AI solutions.

Furthermore, beyond technical skills, curiosity plays a pivotal role. The ability to ask the right questions and explore the data inquisitively is paramount. This involves understanding the societal issues at hand, being able to identify relevant data sources, and formulating insightful research questions. Curiosity drives the exploration and discovery process, leading to innovative solutions and a deeper understanding of the societal impact of AI technologies.

What are the most important soft skills for people looking for jobs in AI for good?

Communication: Strong communication skills are crucial for effectively conveying complex technical concepts to diverse audiences. AI for good often involves storytelling and collaborating with interdisciplinary teams, policymakers, and community stakeholders. The ability to articulate ideas clearly, listen actively, and adapt communication styles is vital.

Ethical Reasoning: AI for good requires ethical decision-making. Professionals in this field should possess a strong ethical compass and be able to navigate the potential social, cultural, and moral implications of AI technologies. Critical thinking, empathy, and the ability to consider multiple perspectives are valuable for addressing ethical challenges.

Collaboration: AI for good initiatives typically involve working in teams with professionals from various backgrounds. Collaboration skills like teamwork, compromise, and conflict resolution are essential for fostering positive working relationships and achieving shared goals.

What are some of the projects you are most proud of working on, or have the fondest memories of?

Every one of our projects is important, so it is difficult for me to choose among them 😊

How do you keep learning?

I like reading a lot of technical books and papers; they keep me informed, but the best way to learn is by doing. So even while I manage the team, I keep working part-time as an individual contributor, staying hands-on and doing a small number of projects myself. This can be challenging for me because I don’t have a lot of time, but I love spending time doing some of the IC work myself.

Join our new specialization: AI for Good