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⚖️ AI Ethics & Accountability — Decision Network Simulation

Interactive AI ethics simulation: watch decisions flow through a neural network and see how autonomy, decision impact, bias and explainability shape real-time accountability risk.

AI & Machine Learning3DEasy60 FPS

This simulation turns the abstract ethics of AI systems into something you can watch happen: a layered decision network where every "decision" is a pulse of light travelling from raw input, through the model's hidden internals, to an output the system acts on.

🔬 What It Demonstrates

Each pulse rolls a chance of being biased based on the Algorithmic bias slider, then lands on an output node — fair decisions glow teal, biased ones glow red. Autonomy level sets how fast and how often the system decides on its own, Decision impact scales how much every decision visually "weighs", and Explainability (XAI) controls the fog obscuring the hidden layer: the lower it is, the more the model's internals become an unreadable black box.

🎮 How to Use

Drag Algorithmic bias to see the ratio of red vs teal decisions shift, raise Autonomy to watch decisions fire faster with less pacing, and lower Explainability to thicken the fog around the hidden layer. Watch the ring around the whole network — it is the visual "human oversight" and shrinks as the Accountability gap grows. Press Reset to clear the decision log and start clean.

💡 Did You Know?

The panel's Complexity score is literally Autonomy × Impact — the same relationship researchers use informally to argue that highly autonomous, high-stakes systems (like autonomous vehicles or medical AI) need proportionally stronger explainability and audit trails to stay accountable.

⚙ Under the hood

Watch decisions flow through a layered AI decision network and see how autonomy, decision impact, algorithmic bias and explainability shape real-time accountability risk.

Three.jsAI EthicsNeural NetworkInstancedMesh

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install