“Mythology” Series:
Format: Each week we present a concise mythological story and draw direct parallels to contemporary AI concepts.
Goal: Highlight how modern technological dilemmas mirror ancient Greek tales, sparking interest about both subjects.
1. Mythological reference
In Greek myth, Iris is the swift messenger of the gods. She travels between Olympus and the human world and leaves a rainbow in her path. Her role is to make divine intent visible and understandable. Iris stands for clarity, bridge building, and beauty in communication.
2. Parallel with AI and lesson from ancient mythology
Modern AI generates complex signals models, weights, probabilities, feature interactions. Without interpretation they stay on Olympus. Data visualization, explainable AI dashboards and reporting layers are our rainbow, turning hidden logic into forms that users, executives and regulators can read.
Visual bridges
Feature importance bars, SHAP plots, timeline anomaly charts, map overlays for satellite AI. Each makes invisible reasoning visible.Audience aware
Designers should tailor views for engineers, for business owners and for the public. Iris spoke the language of each realm.Honest color
Visuals must include uncertainty, limits, and data provenance so they do not oversell confidence.
Lesson
Insight is not complete until it is communicated. Build AI that speaks in color.
3. Reflections and questions to consider
Do our models ship with human readable explanations by default
Are visualizations aligned with the data literacy of the audience
Do we display uncertainty, data freshness, and source so trust is justified
Can non technical stakeholders trace a decision from input to visualization
4. References
Iliad
Scenes of divine messages shaping human action.Odyssey
Examples of clear guidance in moments of confusion.Adrienne Mayor, Gods and Robots: Myths, Machines, and Ancient Dreams of Technology
Mythic context for mediating between power and people.Doshi Velez and Kim, work on interpretability
Frameworks for making models understandable.Model Cards and Datasheets for Datasets
Structured ways to present AI decisions in clear form.
“Without interpretation they stay on Olympus. Data visualization, explainable AI dashboards and reporting layers are our rainbow, turning hidden logic into forms that users, executives and regulators can read.” Loved it. Thank you nice post.
The parallell between Iris and AI explainability is really insightful. In my experince, the biggest challenge with data visualizaton isn't the technical side but making sure stakeholders actually understand what they're looking at. I've seen too many fancy dashboards that just confuse people more. The point about including uncertainty and data provenance is crucial, we need those honest colors to build real trust.