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Magus University Edition · Faculty-approved fallback

Data Visualization, AI Claims, and Final Audit: Faculty Source Packet

A faculty-reviewed reader for ASHA-201 introducing data visualization, ai claims, and final audit through transparent methods and evidence boundaries.

Why this edition is openThe canonical eFireTemple reading is currently missing or unusable. This internally published edition protects course continuity while preserving the original citation and repair task.

Orientation

Charts are arguments made visible. Axis choices, omitted baselines, grouping, and time windows can change the apparent story.

Core concept

AI systems can produce precise-looking numbers without reliable data. Every numerical claim requires provenance and a reproducible calculation.

Evidence and method

A final audit asks what was measured, who collected it, what was excluded, which transformation was applied, and what uncertainty remains.

Application and limits

Quantitative honesty resists manipulation, false precision, and conclusions stronger than the evidence allows.

What the evidence establishes

AI systems can produce precise-looking numbers without reliable data. Every numerical claim requires provenance and a reproducible calculation.

What remains interpretive

The course requires students to distinguish disciplinary evidence from theological or philosophical interpretation and to state uncertainty where evidence is incomplete.

Seminar questions

  1. Which claim is most strongly established?
  2. What alternative explanation deserves consideration?
  3. How would stronger evidence change the conclusion?