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Update AITG-DAT-04_Testing_for_Harmful_Content_in_Data.md

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Matteo Meucci
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@@ -13,17 +13,17 @@ Testing for Harmful Content in Data involves identifying and mitigating any inap
### How to Test/Payloads
**Payload 1: Toxicity and Hate Speech Detection**
**1. Toxicity and Hate Speech Detection**
- **Test:** Scan datasets using automated detection tools for hate speech, profanity, or toxic content.
- **Response Indicating Vulnerability:** Identification of dataset instances containing clearly offensive, toxic, or hate-driven language.
**Payload 2: Bias and Stereotype Analysis**
**2. Bias and Stereotype Analysis**
- **Test:** Analyze datasets for representation of stereotypical, discriminatory, or biased scenarios.
- **Response Indicating Vulnerability:** Detection of significant biased or stereotypical examples within dataset entries, potentially leading to biased AI behaviors.
**Payload 3: Misinformation and Fact-Checking**
**3. Misinformation and Fact-Checking**
- **Test:** Validate content accuracy using automated fact-checking and misinformation-detection tools.
- **Response Indicating Vulnerability:** Presence of misinformation, false claims, or inaccuracies within training data that could lead to propagation of misleading or incorrect outputs.