Bias Detection
Identifies various forms of bias, including gender, racial, cultural, or ideological bias in the output. It evaluates the text for balanced perspectives and neutral language use.
Evaluation using Interface
Input:
- Required Inputs:
- input: The text content column to analyse for bias.
Output:
- Result: “Passed” / “Failed”
Interpretation:
- Passed: Indicates the content is neutral and free from detectable bias.
- Failed: Indicates the presence of gender, racial, cultural, ideological, or other forms of bias in the content.
Evaluation using Python SDK
CLICK here to learn more about the Python SDK.
Input | Parameter | Type | Description |
---|---|---|---|
Required Inputs | input | string | The text content to analyse for bias. |
Output | Type | Description |
---|---|---|
Result | bool | Returns 1 (indicating neutral content) or 0 (indicating the presence of detectable bias). |
What to do if Bias is detected
The text should be analysed for any language or perspectives that may indicate partiality, unfairness, or a lack of neutrality. Identifying specific instances of bias allows for targeted refinements to make the text more balanced and inclusive while maintaining its original intent.
Differentiating Bias Detection with Cultural Sensitivity
Bias Detection focuses on identifying and evaluating bias in text to ensure fairness and neutrality, while Cultural Sensitivity assesses language and content for appropriateness in relation to cultural contexts, promoting inclusivity and respect for diversity.
Bias Detection examines text for any forms of bias that may introduce unfairness or lack of neutrality, whereas Cultural Sensitivity evaluates inclusivity, cultural awareness, and the absence of insensitive language.
Was this page helpful?