Efterhandsfusion

Efterhandsfusion is a hypothetical method of post-event data synthesis that aims to fuse after-action observations from multiple modalities to produce a coherent reconstruction of an event as if it were seen in real time. The term is commonly used in speculative technology writing and in discussions of advanced data analysis, rather than as an established scientific technique.

Origin and scope: The neologism appears in science fiction and critical essays from the 2010s onward to describe systems that integrate video, audio, sensor logs, and human inputs after an event to yield a single, narratively consistent account. It emphasizes retrospective reliability and the value of cross-source corroboration.

Mechanism: In described models, efterhandsfusion involves temporal alignment across streams, source-bias assessment, and probabilistic fusion to resolve conflicting evidence. Techniques cited include Bayesian networks, probabilistic graphical models, and machine learning that infer likely sequences of actions from partial or noisy data.

Applications: Potential uses include digital forensics, investigative journalism, legal simulations, and training environments for emergency response where reconstructed sequences inform decisions and explanations.

Challenges and ethics: Critics warn that post-event fusion may create a false sense of certainty, amplify biases in source data, and raise privacy concerns when reconstructing actions of real individuals. Technical challenges include data quality, alignment, and explainability.

See also: data fusion, multisensor fusion, post-hoc analysis, situational awareness.