Artificial intelligence (AI) is becoming part of our everyday lives. It shapes what we watch, what we buy, how we travel, and even the decisions companies make about hiring or approving loans. However, as AI becomes more powerful, so does its bias.
AI bias is the systematic preference or prejudice within an algorithm's outcomes, leading to unfair or inaccurate results that favor or disadvantage certain individuals or groups.
AI bias occurs because algorithms learn from data that may already contain human prejudices, reflect societal inequalities, or be incomplete and unrepresentative.
Biased AI can skew job applicant rankings, inflate or deny loan rates, limit content you see online, misinterpret speech accents, and route transportation services away from certain neighborhoods.
Completely eradicating bias is extremely difficult because data always reflects human society, but transparency, diverse teams, and rigorous evaluation can minimize harmful effects.
Start with diverse, high-quality datasets, audit models regularly, apply fairness metrics, involve multidisciplinary experts, encourage transparency, and create feedback channels so affected users can report and correct discriminatory outcomes.
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