An API that provides bias score and refined statement
Classify text into categories
Track, rank and evaluate open Arabic LLMs and chatbots
Generate Shark Tank India Analysis
Generate answers by querying text in uploaded documents
Humanize AI-generated text to sound like it was written by a human
Semantically Search Analytics Vidhya free Courses
Generate relation triplets from text
Check text for moderation flags
Generate vector representations from text
Analyze Ancient Greek text for syntax and named entities
A benchmark for open-source multi-dialect Arabic ASR models
Determine emotion from text
Bias Detection and Mitigation using WEAT score is a tool designed to identify and reduce bias in text. It leverages the Word Embedding Association Test (WEAT), a statistical method to measure biases in word embeddings by comparing associations between target words and attribute words. The tool provides a bias score and offers a refined statement to mitigate detected biases, ensuring fairer and more neutral text outputs.
• Bias Detection: Analyzes text for inherent biases using the WEAT score.
• Bias Mitigation: Suggests refined, neutral versions of biased text.
• Quantifiable Scoring: Provides a numerical bias score for objective assessment.
• Language Support: Works with various languages for global applicability.
• Fairness Assessment: Evaluates the fairness of statements in real-time.
What is the WEAT score?
The WEAT score is a statistical measure that assesses the bias in word embeddings by comparing the relative associations of target and attribute words.
How is the bias score calculated?
The bias score is derived from the WEAT test, which calculates the difference in word association strengths and normalizes it to produce a standardized score.
Does the tool support multiple languages?
Yes, the tool is designed to support various languages, making it a versatile solution for global text analysis.