Practical AI
A Public AI Dataset Still Needs a Usage Check
Check an AI dataset’s licence, provenance and intended use before assuming a public download can be used freely in a business workflow.
AI · VISION · EVERYDAY SYSTEMS
Journal
Practical AI
A Benchmark Average Can Hide the Language Your Users Need
A model’s overall benchmark result may hide weak performance in a language your users rely on. Evaluate the actual task and language mix separately.
AI reliability
OCR Can Read Every Number and Still Put It in the Wrong Row
Check extracted tables by row, column, unit and heading rather than assuming accurate character recognition means the structure is correct.
AI reliability
Ask Which Error Matters Before Comparing AI Accuracy
Use a simple confusion-matrix example to see why a high accuracy score can hide the AI errors that matter most for the task.
AI reliability
An AI Confidence Score Needs a Reality Check
Distinguish a model’s confidence from demonstrated reliability, using calibration checks on representative data before treating a score as a probability.
Applied AI
An AI Test Can Look Excellent Because It Has Seen the Answers
Spot machine-learning data leakage by checking when information became available and keeping test data out of model preparation.
AI reliability
A Confident AI Answer Needs an Abstain Option
Design an AI workflow that can leave a question unresolved, request evidence or escalate instead of rewarding a fluent answer to every input.
How AI systems work
A Long AI Conversation Is Not the Same as Permanent Memory
Understand the difference between an AI model’s context window and an application’s memory features before assuming that a long conversation remains fully available.
Working with AI
A Useful AI Summary Needs a Checking Routine, Not Just a Better Prompt
Check AI-written summaries by tracing claims to the original, reviewing omissions and separating a useful reading aid from a verified record.