Fairness¶
Fairness is the ability of an organization or ecosystem to treat participants equitably, recognize contributions appropriately, and distribute information and value without undue bias.
In an Enterprise Knowledge Graph (EKG), fairness is not only a social or HR topic. It also has a concrete meaning for data and knowledge:
- Recognition of value-chain contributors: make it clear who created, curated, and published data and knowledge, and what value they added.
- Less biased appreciation: reduce subjective or opaque judgement by using explicit criteria (e.g. quality, provenance, fitness for use, reuse) when selecting or ranking information.
- Unbiased distribution of information: make information available to the right people and systems, at the right time, based on entitlements and policy rather than favoritism.
Fairness is tightly linked to transparency and accountability: if you cannot see what happened, or who did what, you cannot fairly evaluate outcomes or assign credit and responsibility.
Fairness also aligns with the FAIR principles (Findable, Accessible, Interoperable, Reusable), which are widely used in knowledge graph practice.
See also the theme page.