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1. Anti-patterns in AI Projects

Purpose

Overview of the "NOT DONE" anti-patterns that must be avoided in AI projects to prevent failure and compliance issues.

1. Objective

This list defines the "NOT DONE" criteria for AI projects: anti-patterns that must be absolutely avoided to prevent failure, unethical behaviour or compliance issues.

2. The "NOT DONE" List

No Fairness Check (Bias Audit)

  • Rule: AI systems must be regularly checked for bias.
  • Impact: Discrimination and reputational damage.

No Human Oversight

  • Rule: AI decisions (especially at high risk) must have human approval or 'in-the-loop' supervision in line with the chosen Collaboration Mode.
  • Impact: Uncontrolled errors.

No Continuous Monitoring

  • Rule: Models degrade over time (Performance Degradation). Continuous monitoring is required.
  • Impact: Performance loss and unreliable output.

No Governance Checkpoints

  • Rule: Every phase must have formal checkpoints (Gates).
  • Impact: Unmanageable risks and budget overruns.

No Stakeholder Engagement

  • Rule: Stakeholders and end users must be involved from day one.
  • Impact: Solutions that are not used.

No Explainability

  • Rule: AI decisions must be explainable to the user.
  • Impact: "Black box" distrust and non-compliance with regulations.

No Data Evaluation

  • Rule: Input data must be valid, clean and representative.
  • Impact: "Garbage in, garbage out".

No Risk Management

  • Rule: Risks must be proactively identified and mitigated.
  • Impact: Unexpected incidents.

No Traceability

  • Rule: For every model version it must be traceable on which data and with which Steering Instructions it was trained.
  • Impact: Inability to audit errors.

3. Implementation

Use this list as:

  1. Checklist during project initiation.
  2. Review criteria during Gate Reviews.
  3. Training material for teams to create awareness.
  4. Audit tool for compliance verification.