Algorithmic Transparency
Making algorithmic decisions understandable and auditable to build trust and satisfy oversight.
Goals it supports
Landscape feels like
Signals to watch for
- Procurement or regulators require audit trails, model cards, or explainability artefacts.
- Users appeal or contest automated outcomes at rising rates.
- Media or stakeholder scrutiny focuses on opaque decision-making.
First momentum moves
- Map the highest-impact decisions and define the minimum explainability needed for each.
- Publish a transparent decision dossier (model cards, data lineage, evaluation metrics).
- Establish a cross-functional review board for algorithmic changes.
Watch outs
- Sharing transparency artefacts without controls, leading to gaming or security risks.
- Overloading users with technical detail instead of actionable explanations.
- Treating compliance checklists as a substitute for genuine accountability.