AI Ethics · ARTICLEMEMBERS
How a small team trials AI, compares it with a non-AI baseline, and actually stops after a poor result
AI Ethics · ARTICLEMEMBERS
Keeping empirical findings, values, organisational objectives, and risk tolerance distinct—and connecting them with reasons
AI Project Management · ARTICLEMEMBERS
Hallucinations, subtle errors and quality control
AI Project Management · ARTICLEMEMBERS
Why language models do not replace the edge of the map.
AI Project Management · ARTICLEMEMBERS
A project is not resilient because everything in the plan works. It is resilient when work can continue even when a service goes down, a limit is reached, an interface changes, or a supposedly simple AI task suddenly becomes blocked.
AI Project Management · ARTICLEMEMBERS
Good project planning does not try to predict every surprise. It creates a structure in which the unexpected can be detected, interpreted and processed without making the project lose direction whenever reality deviates from the plan.
AI Data Engineering · ARTICLEMEMBERS
Data can remain inside your own building and still belong to a logic that nobody there controls.
AI Data Engineering · ARTICLEMEMBERS
A system that may read everything and change everything at the same time is convenient. That is precisely why it is difficult to govern.
AI Data Engineering · ARTICLEMEMBERS
An isolated workspace prevents collisions. A checkpoint makes a state addressable. A report explains the path. A backup survives failure. Recoverability needs all four.
AI Data Engineering · ARTICLEMEMBERS
Automation removes work steps, not accountability. The faster a system can act, the farther it can reach and the more often it can repeat decisions, the more precisely its boundaries, signals and intervention points must be designed.
AI Data Engineering · ARTICLEMEMBERS
A persona controls how a system communicates. A skill controls how it performs a capability. Policy controls what it may do. Project data defines what it works on. Mixing these layers creates opaque bundles of power.
AI Data Engineering · ARTICLEMEMBERS
Documented knowledge explains what to do. Executable knowledge also defines when, with which tools, inside which limits and with what quality evidence it may be done.
AI Data Engineering · ARTICLEMEMBERS
An answer can be wrong. A file change can also alter the next process run, other people's work and the authoritative knowledge state. Write access therefore begins a new risk class.
AI Data Engineering · ARTICLEMEMBERS
The CE marking says that conformity is declared for a specific system against the applicable requirements. It does not say that the system is the best, error-free or endorsed by an authority.
AI Data Engineering · ARTICLEMEMBERS
A production AI system is not a completed project. It is a continuing claim that purpose, performance and controls still hold under changing conditions.
AI Data Engineering · ARTICLEMEMBERS
Once real people, decisions or consequences are involved, the word "pilot" is no longer a safety architecture.
AI Data Engineering · ARTICLEMEMBERS
A sandbox is a supervised learning environment. Treating it as a certification machine confuses the process of discovery with the outcome of conformity assessment.
AI Data Engineering · ARTICLEMEMBERS
Consultants who present solutions too early often advise an imagined organisation. A defensible assessment first reveals how work, data, systems, decisions and responsibility actually connect.
AI Data Engineering · ARTICLEMEMBERS
The same model can draft an internal text, rank job applicants or control a safety-relevant process. Technical similarity does not make these uses equally risky in law or operations.