Author: Mallory

AI Detection & Evaluation

When Detection Gets It Wrong: Understanding False Positives and Bias in AI Classification

How AI detection systems may misclassify content, why certain populations face higher false positive risks, and how to think about detection results responsibly.

Mallory

March 4, 2026

Responsible AI Practices

AI-Generated Content vs AI-Assisted Writing: The Difference That Matters

Understanding the important distinction between fully automated content and human-guided AI assistance — and why intent and workflow define quality.

Mallory

February 20, 2026

Editorial Standards & Quality

A Practical Framework for Evaluating Content Quality in the AI Era

A structured approach to assessing content quality that prioritizes transparency, accountability, and usefulness over origin — human or machine.

Mallory

February 17, 2026

AI Content Authenticity

What Is AI Content Authenticity (And Why It Matters Now)

An in-depth look at what content authenticity means in the age of generative AI, why verification matters, and how the concept of digital integrity is evolving.

Mallory

February 13, 2026

Editorial Standards & Quality

A Framework for Thinking About Content Quality Beyond the AI Question

A practical approach to evaluating content based on originality, clarity, usefulness, and accountability—independent of whether AI tools were involved in creation.

Mallory

February 6, 2026

AI Detection & Evaluation

The Risks of Blind AI Detection: False Positives, Bias, and Overreach

Examining the real-world consequences when AI detection systems are applied without nuance — from wrongly accused students to systemic bias against non-native writers.

Mallory

February 6, 2026

AI Detection & Evaluation

How AI Detection Works (Without the Marketing Hype)

A clear, honest explanation of how AI content detection systems actually work, their real capabilities, and why no detector achieves perfect accuracy.

Mallory

February 6, 2026