Established five years ago by the Rockefeller Foundation and the Mastercard Center for Inclusive Growth, Data.org is a nonprofit dedicated to advancing the use of data and AI for social good.
The rapid evolution of AI and data science presents both an unprecedented opportunity and a growing challenge for the social impact sector.
While AI-powered tools have the potential to enhance decision-making, streamline operations, and increase efficiency, the gap between the private sector’s adoption of AI and the ability of nonprofits to leverage these technologies remains significant.
One of the most immediate impacts of AI on data work is its ability to automate many traditionally labor-intensive tasks, from data cleaning and visualisation to sophisticated data analysis. For social impact organisations, this represents a powerful efficiency boost, particularly for those with limited resources. Yet, while AI can enhance accessibility to data and streamline its use, it cannot replace human judgment, particularly in contexts involving vulnerable communities. The ethical deployment of AI remains paramount, and organisations must ensure that human oversight is preserved in critical decision-making processes.
Beyond efficiency gains, AI is also reshaping how nonprofits and global grant-making organisations assess impact. Many NGOs possess vast repositories of historical data that remain largely untapped due to resource constraints. AI-driven document analysis and natural language processing are now unlocking these archives, enabling organisations to extract meaningful insights and make data-driven decisions.
The conversation also delves into the broader ethical considerations of AI, particularly the risks associated with overcorrection in training data. AI models are designed to reflect the information they are fed, and any attempt to engineer ethical biases — whether to correct for historical exclusions or to impose specific viewpoints — must be handled with caution. The balance between mitigating bias and preserving accuracy remains a complex challenge, as evidenced by recent controversies over AI-generated historical imagery that distorted reality in the name of diversity.
The takeaway is that ethical AI cannot be an afterthought. It must be integrated into the design and development process from the outset, ensuring that social scientists, ethicists, and technologists collaborate in real-time rather than operating in silos.
About Danil Mikhailov
Danil Mikhailov, Ph.D. is a computer scientist and social scientist and a world-leading expert in the application of technology and data innovation for social impact. Danil’s main research interests are in Science and Technology Studies, including social media, AI, and misinformation; technology and AI ethics; and design of healthy and inclusive data ecosystems. A second, unrelated area of research interest is the history, philosophy, and practice of traditional Chinese martial arts.
In parallel to his research Danil serves as the Executive Director of data.org, where Danil launched global data for social impact programs in climate, health, and financial inclusion to build digital public goods, train a new generation of purpose-driven data practitioners, and design sustainable data ecosystems in Latin America, Asia, Africa, Europe, and the US. Prior to data.org, Danil was Head of Data & Innovation at Wellcome where he founded and directed the Wellcome Data Labs, an interdisciplinary team of data scientists, software developers, and social scientists, creating open-source data tools supporting Wellcome’s mission.
Danil has served on executive and advisory boards of multiple other high-profile academic, nonprofit, and public sector initiatives, including founding and chairing the Digital Strategy Forum for Science, Art, and Culture, in the United Kingdom, co-founding the Research on Research Institute that studies the processes and structures of scientific research, and co-founding and chairing the Global Pandemic Data Alliance, formed under the auspices of the G7 to improve the effectiveness of world’s pandemic & epidemic data infrastructure.
Danil holds a Ph.D. in Sociology and Communications from the University of Brunel, an MA in Philosophy, from Birkbeck, University of London, an MA in Chinese Studies, from SOAS, University of London, and a BSc in IT & Business Management, University of York. He is a visiting research fellow at Harvard University, in the US, a research associate at SOAS, in the UK, and the co-lead of the newly launched Institute of Community AI research (I-CAIR).