AI Data Quality Evaluation
Evaluate Data Quality Batch
Asynchronously evaluates the quality of multiple survey responses
POST
Authorizations
API key for authentication
Body
application/json
The name of the survey
URL where results should be sent when processing completes
Optional unique identifier for the survey.
Aftercare will use this to build a data model to associate questions and answers with the survey.
A description of the survey purpose and background.
Types of quality issues that can be detected in survey responses.
Nonsensical
- Response lacks logical meaning or sense. Likely gibberish.Irrelevant
- Response does not address the question.Low Effort
- Respondent did not put in much effort to answer the question. Lacks detail or concrete examples.LLM Generated
- Response appears to be generated by AI.Self Duplicated
- Responses from the same respondent contain duplicated content across multiple answers. Only evaluated when multiple survey entries are provided or if a survey identifier and response identifier is provided.Shared Duplicate
- Responses contain duplicated content across different respondents for the same question. Only evaluated if survey identifiers and question identifiers are provided.
Available options:
Nonsensical
, Irrelevant
, Low Effort
, LLM Generated
, Repeated Answers
, Duplicate Answers