17/06/2026
🤔 If AI can only learn from what is documented, what happens to the people, relationships and realities that never become data?
Read excellent new submission "The blind spots of AI in sport for development" by sportanddev contributor Anna Fedorchuk of the Premier League Foundation 👉https://zpr.io/tDdUmPvyYNzF
✨ Here's a human-checked AI summary of this article ✨
* AI offers SFD organisations opportunities to improve monitoring, reporting, accessibility and efficiency, but its effectiveness depends on the quality and scope of existing data.
* AI can only learn from documented information, leaving important but undocumented aspects of sport systems invisible.
* Much of sport’s social value—trust, belonging, safety, community relationships and informal leadership—is difficult to capture through conventional data collection.
* Evidence from healthcare shows that AI can reinforce inequalities when measurable proxies fail to reflect complex social realities.
* In SFD, proxies such as attendance, participation numbers or donor reports may overlook inclusion, impact and informal sport activities.
* AI systems often rely on Global North knowledge and formal documentation, risking the exclusion of local perspectives and under-researched communities.
* Grassroots sport activities, oral histories, community networks and local knowledge may remain invisible because they are rarely documented digitally.
* AI may create an illusion of comprehensive understanding while reflecting only a partial picture of local sport ecosystems.
* The article warns of “epistemic narrowing”, where AI repeatedly amplifies existing narratives and makes knowledge gaps harder to identify.
* The sector should use AI to support—not replace—participatory research, local knowledge production, community-led monitoring and critical reflection on what data is missing.
