US 2025/0148306 A1 · OPTIMIZED CONTENT MODERATION WORKFLOW

Abstract

A method, computer system, and a computer program product are provided for evaluating content moderation by training an Artificial Intelligence (AI) engine. A plurality of data is obtained to be used for training the AI engine. A plurality of current labels are generated using an associated category and topic. Any past labeled data similar to the plurality of data obtained is collected. It is then determined if the current labels or the past labelled data have one or more associated biases based on a bias criteria. A fairness weight score is calculated for each current label based on the associated number of biases and whether the current label is associated with any past label data with one or more biases. A final label is generated for the plurality of current labels based on the fairness weight score. The final label is used to train the AI engine.

Claims20

Dependent claims

A method for evaluating content moderation by training an Artificial Intelligence (AI) engine, comprising:

obtaining a plurality of data to be used for training said AI engine;
generating a plurality of current labels for said plurality of data using an associated category and a topic;
collecting any past labeled data that is similar to said plurality of current labels;
determining if said current labels or said past labeled data have one or more associated biases, wherein said one or more associated biases are based on a bias criteria;
calculating a fairness weight score for each current label based on a number of biases determined to be associated with said current label and whether said current label is associated with any past label data determined to have one or more biases;
generating a final label for each plurality of current labels based on said fairness weight score;
and
using said final label for training said AI engine.

5 more elements

A computer system for evaluating content moderation by training an Artificial Intelligence (AI) engine, comprising:

one or more processors, one or more computer-readable memories and one or more computer-readable storage media;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to obtain a plurality of data to be used for training said AI engine;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to generate a plurality of current labels for said plurality of data using an associated category and a topic;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to collect any past labeled data that is similar to said plurality of current labels;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to determine if said current labels or said past labeled data have one or more associated biases, wherein said one or more associated biases are based on a bias criteria;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to calculate a fairness weight score for each current label based on a number of biases determined to be associated with said current label and whether said current label is associated with any past label data determined to have one or more biases;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to generate a final label for each plurality of current labels based on said fairness score;
and
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to use said final label for training said AI engine.

6 more elements

A computer program product for evaluating content moderation by training an Artificial Intelligence (AI) engine comprising:

one or more computer readable storage media;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to obtain a plurality of data to be used for training said AI engine;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to generate a plurality of current labels for said plurality of data using an associated category and a topic;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to collect any past labeled data that is similar to said plurality of current labels;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to determine if said current labels or said past labeled data have one or more associated biases, wherein said one or more associated biases are based on a bias criteria;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to calculate a fairness weight score for each current label based on a number of biases determined to be associated with said current label and whether said current label is associated with any past label data determined to have one or more biases;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to generate a final label for each plurality of current labels based on said fairness score;
and
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to use said final label for training said AI engine.

6 more elements

Dependencies are inferred from the claim text.

Optimized content moderation workflow

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Optimized content moderation workflow

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Abstract

Claims20