Sensitivity assessment for media production using artificial intelligence

A computer system for determining a sensitive portion of a dataset for media production, the computer system comprising:

• a memory having processor-readable instructions stored therein;
• and
• one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions, including functions for:
• receiving, by one or more processors, a digital dataset corresponding to a video source file;
• processing, by the one or more processors, the digital dataset into a plurality of frames;
• annotating, by the one or more processors via a machine-learning model, at least one of the plurality of frames with censorship metadata, wherein the censorship metadata includes a sensitive event;
• mapping, by the one or more processors, utilizing a multidimensional tensor, the annotated plurality of frames to a sensitivity score based on the censorship metadata, wherein the mapping includes mapping the sensitive event to the sensitivity score;
• assigning, by the one or more processors, a sensitivity tag to the sensitivity score, wherein the sensitivity tag indicates a censorship likelihood for the sensitive event;
• updating, by the one or more processors, the multidimensional tensor to include the sensitivity tag, the multidimensional tensor having a plurality of dimensions including a sensitivity tag category, a localization region, and a sensitivity level;
• and
• outputting, by the one or more processors, a visual representation of the sensitive event and the sensitivity tag to a display.

A non-transitory computer-readable medium containing instructions for determining a sensitive portion of a dataset for media production, the instructions comprising:

• receiving a digital dataset corresponding to a video source file;
• processing the digital dataset into a plurality of frames;
• annotating, via a machine-learning model, at least one of the plurality of frames with censorship metadata, wherein the censorship metadata includes a sensitive event;
• mapping, utilizing a multidimensional tensor, the annotated plurality of frames to a sensitivity score based on the censorship metadata, wherein the mapping includes mapping the sensitive event to the sensitivity score;
• assigning a sensitivity tag to the sensitivity score, wherein the sensitivity tag indicates a censorship likelihood for the sensitive event;
• updating the multidimensional tensor to include the sensitivity tag, the multidimensional tensor having a plurality of dimensions including a sensitivity tag category, a localization region, and a sensitivity level;
• and
• outputting a visual representation of the sensitive event and the sensitivity tag to a display.