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Use Human Loop Activation Conditions JSON Schema with Amazon Rekognition

When used with Amazon A2I, the Amazon Rekognition DetectModerationLabels operation supports the following inputs in the ConditionType parameters:

When creating a flow definition, if you use the default worker task template that is provided in the Human review workflows section of the Amazon SageMaker console, inferences sent for human review by these activation conditions are included in the worker UI when a worker opens your task. If you use a custom worker task template, you need to include the <task.input.selectedAiServiceResponse.blocks> custom HTML element to access these inferences. For an example of a custom template that uses this HTML element, see Custom Template Example for Amazon Rekognition.

ModerationLabelConfidenceCheck Inputs

For the ModerationLabelConfidenceCheck ConditionType, the following ConditionParameters are supported:

When you use the ModerationLabelConfidenceCheck ConditionType, Amazon A2I sends label inferences for the labels that you specified in ModerationLabelName for human review.

Sampling Inputs

The Sampling ConditionType supports the RandomSamplingPercentage ConditionParameters. The input for the RandomSamplingPercentage prameter should be real number between 0.01 and 100. This number represents the percentage of inferences that qualifies for a human review that are sent to humans for review. If you use the Sampling condition without any other conditions, this number represents the percentage of all inferences that result from a single DetectModerationLabel request that are sent to humans for review.

Examples

Example 1: Use ModerationLabelConfidenceCheck with the And operator

The following example of a HumanLoopActivationConditions condition initiates a human loop when one or more of the following conditions are met:

Note the use of the Or and And logical operators to model this logic.

Although only one of the two conditions under the Or operator needs to evaluate to true for a human loop to be created, Amazon Augmented AI evaluates all conditions. Human reviewers are asked to review the moderation labels for all the conditions that evaluated to true.

{
     "Conditions": [{
         "Or": [{
                 "And": [{
                         "ConditionType": "ModerationLabelConfidenceCheck",
                         "ConditionParameters": {
                             "ModerationLabelName": "Graphic Male Nudity",
                             "ConfidenceLessThanEquals": 99
                         }
                     },
                     {
                         "ConditionType": "ModerationLabelConfidenceCheck",
                         "ConditionParameters": {
                             "ModerationLabelName": "Graphic Male Nudity",
                             "ConfidenceGreaterThanEquals": 90
                         }
                     }
                 ]
             },
             {
                 "And": [{
                         "ConditionType": "ModerationLabelConfidenceCheck",
                         "ConditionParameters": {
                             "ModerationLabelName": "Graphic Female Nudity",
                             "ConfidenceLessThanEquals": 99
                         }
                     },
                     {
                         "ConditionType": "ModerationLabelConfidenceCheck",
                         "ConditionParameters": {
                             "ModerationLabelName": "Graphic Female Nudity",
                             "ConfidenceGreaterThanEquals": 80
                         }
                     }
                 ]
             }
         ]
     }]
}

Example 2: Use ModerationLabelConfidenceCheck with the catch-all value (*)

In the following example, if any moderation label with a confidence greater than or equal to 75 is detected, a human loop is initiated. Human reviewers are asked to review all moderation labels with confidence scores greater than or equal to 75.

{
    "Conditions": [
        {
            "ConditionType": "ModerationLabelConfidenceCheck",
            "ConditionParameters": {
                "ModerationLabelName": "*",
                "ConfidenceGreaterThanEquals": 75
            }
        }
    ]
}

Example 3: Use Sampling

In the following example, 5% of Amazon Rekognition inferences from a DetectModerationLabels request are sent to human workers. When using the default worker task template provided in the SageMaker console, all moderation labels returned by Amazon Rekognition are sent to workers for review.

{
  "Conditions": [
    {
      "ConditionType": "Sampling",
      "ConditionParameters": {
        "RandomSamplingPercentage": 5
      }
    }
  ]
}

Example 4: Use Sampling and ModerationLabelConfidenceCheck with the And operator

In this example, 5% of Amazon Rekognition inferences of the Graphic Male Nudity moderation label with a confidence greater than 50 are sent workers for review. When using the default worker task template provided in the SageMaker console, only the inferences of the Graphic Male Nudity label are sent to workers for review.

{
  "Conditions": [
    {
      "And": [
        {
          "ConditionType": "Sampling",
          "ConditionParameters": {
            "RandomSamplingPercentage": 5
          }
        },
        {
          "ConditionType": "ModerationLabelConfidenceCheck",
          "ConditionParameters": {
            "ModerationLabelName": "Graphic Male Nudity",
            "ConfidenceGreaterThan": 50
          }
        }
      ]
    }
  ]
}

Example 5: Use Sampling and ModerationLabelConfidenceCheck with the And operator

Use this example to configure your human review workflow to always send low-confidence inferences of a specified label for human review and sample high-confidence inferences of a label at a specified rate.

In the following example, a human review is initiated in one of the following ways:

{
  "Conditions": [
    {
      "Or": [
        {
          "ConditionType": "ModerationLabelConfidenceCheck",
          "ConditionParameters": {
            "ModerationLabelName": "Graphic Male Nudity",
            "ConfidenceLessThan": 60
          }
        },
        {
            "And": [
                {
                    "ConditionType": "Sampling",
                    "ConditionParameters": {
                        "RandomSamplingPercentage": 5
                    }
                },
                {
                    "ConditionType": "ModerationLabelConfidenceCheck",
                    "ConditionParameters": {
                        "ModerationLabelName": "Graphic Male Nudity",
                        "ConfidenceGreaterThan": 90
                    }
                }
            ]
        }
      ]
    }
  ]
}

Example 6: Use Sampling and ModerationLabelConfidenceCheck with the Or operator

In the following example, a human loop is created if the Amazon Rekognition inference response contains the 'Graphic Male Nudity' label with inference confidence greater than 50. Additionally, 5% of all other inferences initiate a human loop.

{
  "Conditions": [
    {
      "Or": [
        {
          "ConditionType": "Sampling",
          "ConditionParameters": {
            "RandomSamplingPercentage": 5
          }
        },
        {
          "ConditionType": "ModerationLabelConfidenceCheck",
          "ConditionParameters": {
            "ModerationLabelName": "Graphic Male Nudity",
            "ConfidenceGreaterThan": 50
          }
        }
      ]
    }
  ]
}