Bias in machine learning stems from systematic errors in data and algorithms, leading to unfair outcomes.

Types of bias include selection, labeling, and sampling bias, influencing model predictions.

Algorithmic bias perpetuates discrimination in hiring, policing, and facial recognition technologies.

Social implications of bias include widening inequalities and hindering access to opportunities.

Ethical concerns revolve around fairness, accountability, and transparency in AI systems.

Data preprocessing and algorithmic fairness techniques mitigate bias in machine learning.

Real-world case studies highlight biased AI systems in healthcare, finance, and criminal justice.

Future trends focus on interdisciplinary approaches and regulatory guidelines to address bias.

Understanding bias helps in building more equitable and inclusive AI systems.

Advocating for diversity and participation promotes fairness and transparency in AI development.

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