Panel 1: Anansi’s Mythic Web

In West African folklore, Anansi spins webs of story and deception—structured, intentional, and rule-bound. Each strand is a choice, each knot a consequence. His power lies in narrative logic, not brute force.

This mirrors traditional algorithms:

• Rule-based systems
• Static logic trees
• Predictable outputs

These systems are structured like Anansi’s original web—deterministic, with clear cause and effect.

Panel 2: Modern Digital Web

Today, the web is no longer mythic—it’s machine-learned. Neural networks replace static rules with interconnected nodes, where data flows through layers and meaning emerges from distributed computation.

The EIOC hub (Email, Identity, Online Communication) becomes the model core—a central node in a learning system.

• Input vectors = email, identity, communication
• Hidden layers = emotional triggers, behavioral signals
• Output = prediction, classification, action

Anansi’s web evolves into a dynamic threat surface, where each click reshapes the model.

Panel 3: AI/ML Warnings

The three heads in the triptych now represent technical vulnerabilities:

• Training Bias: The spiral head reflects skewed data—models trained on distorted inputs learn distorted truths.
• Adversarial Inputs: The coin head becomes a lure—crafted inputs that deceive the model into false predictions.
• Hallucination: The lightning head signals overconfidence—outputs that sound plausible but are false.

Alternate framing:

• Overfitting: The model memorizes noise, not signal.
• Data Poisoning: Malicious data corrupts learning.
• Model Drift: The system evolves away from its intended purpose.

Each is a thread in Anansi’s new web—a trap for the curious, the careless, the unprepared.

The Takeaway

Anansi’s web, once a folkloric snare, now maps onto neural architecture and AI risk. The myth becomes a framework:

In both folklore and machine learning, the web is a place of traps as well as treasures. Recognizing the pattern is the first step toward resilience.