Problem

Today, AI is actively used to recommend and select children’s content, but it lacks an approved reference framework for evaluating the usefulness and appropriateness of what it recommends.

As a result, AI systems rely on proxy metrics — popularity, engagement, and ratings — that do not answer the core question: does this content help solve a specific developmental task for a specific child in a specific situation?

Why this problem persists

AI cannot fix this failure on its own because it can only optimize what is formally defined.

Training on user behavior further reinforces the problem: popular and engaging choices are learned as “correct,” regardless of whether they support children’s development.

Why the problem gets worse over time

In the absence of external correctness criteria, this defect scales together with AI systems and gradually becomes normalized.

What the system is missing

To resolve this failure, AI systems need an external accountability layer they can reference when forming recommendations.

Such a layer provides clear rules for determining whether recommendations are developmentally appropriate and beneficial.

It allows systems to distinguish development from attention retention, child benefit from ratings, and makes AI decisions predictable and comparable.

Expected impact

The adoption of FSCBAC would allow millions of children to receive recommendations that genuinely support their development rather than reflect popularity or ratings.

Platforms would be able to rely on a shared standard, making recommendations more predictable, safer, and scalable across the entire children’s content market.

Why FSCBAC

The role of this external reference can be fulfilled by the FSCBAC standard; this project focuses on bringing it to an infrastructure-level of application.

What already exists

FSCBAC already exists as an open, versioned, machine-readable standard published on GitHub and Zenodo and registered in Wikidata.

The standard:

• covers children aged 1–10;

• formalizes developmental coordinates (age, tasks, constraints);

• explicitly excludes marketing metrics (popularity, engagement, ratings);

• ensures reproducible results across systems;

• is released under an open public license.

About me

I am a systems architect with experience leading organizations and projects (800+ people) and hold a Ph.D. in economics.

My expertise lies in infrastructure design, strategy, and operational execution.

Why now

Child-facing AI systems are beginning to scale.

If FSCBAC is not integrated now, standardization will become significantly harder within 1–2 years, and millions of children will continue receiving irrelevant recommendations.

The present moment represents a rare window to introduce a deterministic accountability layer before flawed practices become entrenched.

Execution plan

Adapt the existing FSCBAC standard to an infrastructure-ready form suitable for large-scale integration into AI platforms.

Develop APIs and test JSON schemas to evaluate recommendation correctness on children’s content datasets.

Validate rules through control scenarios and automated compatibility metrics (FSCBAC scoring).

Publish all artifacts openly (GitHub, Zenodo, Wikidata) to enable third-party adoption.

Prepare and document effectiveness reports covering age appropriateness, safety, and developmental relevance.

Why this is low-risk for funders

FSCBAC already exists as a fixed and published standard.

The requested support is aimed not at creating a new concept from scratch, but at elevating an existing solution to infrastructure-level applicability.

The project is not a hypothesis and does not depend on one-time funding to continue existing.