Bias in AI - Artificial Cognition and Machine Technology Today

Bias in AI refers to the presence of unfair or prejudiced outcomes in artificial intelligence systems, often arising from biased data or flawed algorithms. This topic explores how AI can unintentionally perpetuate social inequalities, such as racial, gender, or cultural biases, by learning from historical data that may reflect societal prejudices. It highlights the importance of identifying and mitigating these biases to ensure AI decisions are fair, ethical, and inclusive. Addressing AI bias requires diverse data sets, rigorous testing, transparency in AI development, and ongoing monitoring to prevent discriminatory outcomes and promote fairness in AI applications across industries.