Unfair Bias in AI

Understanding Unfair Bias

Bias in AI often arises from training data that reflects historical inequalities or social stereotypes. If left unchecked, these biases can be amplified by AI systems, leading to discrimination in areas such as hiring, lending, or policing.

Examples of AI Bias

• Facial recognition errors impacting marginalized communities
• Job recommendation systems reinforcing gender roles
• Credit scoring systems misrepresenting minority applicants

Viswanext’s Approach

We employ fairness assessments, debiasing techniques, and diverse data auditing to ensure AI models reflect inclusive and just practices.

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