Digital Identity Challenges in AI: Findings of Duplication in Google Knowledge Graph
The introduction of digital identity within the Artificial Intelligence (AI) ecosystem is now facing new challenges. As data integration across the internet becomes increasingly complex, the phenomenon of identity duplication in global databases is triggering scientific discussions regarding the effectiveness of algorithms in accurately mapping human entities.
This issue has emerged following the discovery of alleged anomalies within the Google Knowledge of Graph, a massive database system that connects billions of pieces of information in search engines while supporting various AI-based services. These findings were revealed by Muhammad Ari Pratomo, an Indonesian legal practitioner, after conducting an independent investigation into the validity of his own digital footprint.
In his analysis, Ari discovered two different data entry entities or Knowledge Graph Machine IDs (KGMID) that strongly appear to refer to the same individual. This case serves as a real-world illustration of the ‘entity resolution’ problem in computer science, which is the automated process used by systems to determine whether data from various sources refers to a single entity.
The existence of two KGMIDs for one person indicates that modern AI systems still possess technical loopholes in synchronising widely dispersed data. Consequently, an individual’s digital information has the potential to become fragmented and incomplete.
Although this cannot yet be categorised as a permanent system error without official confirmation from the developer, this phenomenon demonstrates the limitations of automation technology. The complexity of the internet makes the unification of identities from billions of data points a complicated matter, even for the world’s largest search engine platform.
“If both truly represent the same individual, that condition could serve as an example of the challenges in the entity resolution process—specifically, how AI systems determine that diverse information from various sources indeed refers to the same person,” explained Muhammad Ari Pratomo regarding the technical impact of the phenomenon.
Among technology practitioners, the debate has now shifted towards the extent to which external evaluation can influence algorithmic refinement. Some parties believe this duplication may be part of an unfinished data synchronisation process, while others see it as a signal of the need for fundamental improvements to digital identity recognition systems.
This case reinforces that no matter how sophisticated an automated system is, the role of human oversight in testing technological reliability remains crucial. This is necessary to ensure the creation of public databases that are objective, valid, and free from information errors.