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MASTER COMPUTER SCIENCE FOR YOUR CAREER
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LEARN BASICS OF AI BY STARTING OFF WITH THE MOST EASIEST TOPIC AI ETHICS
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Introduction to AI Ethics Definition of AI ethics: Ethics concerned with the moral implications of AI technologies.
Importance in data science: AI systems impact society, requiring ethical considerations in
their design, deployment, and use.
Ethical Principles in AI
Fairness: Ensuring AI systems do not discriminate unfairly.
Transparency: Making AI decisions understandable and traceable.
Accountability: Determining who is responsible for AI outcomes.
Privacy: Protecting individuals' data and privacy rights.
Robustness and safety: Ensuring AI systems operate reliably and safely.
Bias in AI
Definition of bias in AI: Systematic errors resulting in unfair outcomes for certain groups.
Examples of bias: Facial recognition systems biased against certain demographics, hiring
algorithms favoring certain backgrounds.
Impact of biased AI: Reinforcing societal inequalities and undermining trust in AI
technologies.
Fairness and Bias Mitigation
Data preprocessing: Techniques such as data augmentation and balancing datasets to
reduce bias.
Algorithmic fairness: Implementing fairness metrics and algorithms that mitigate bias
during model training.
Regular auditing: Continuously monitoring AI systems for bias and fairness post-
deployment.
Transparency and Explainability
Importance of transparency: Building trust with stakeholders and users by making AI
decisions understandable.
Techniques for explainable AI: Using interpretable models, providing explanations for AI
predictions.
Regulatory requirements: Emerging standards and regulations (e.g., GDPR) mandating
transparency in AI systems.
Accountability and Responsibility
Assigning accountability: Determining whether responsibility lies with developers, users,
or regulatory bodies.
Legal frameworks: Overview of legal frameworks addressing liability for AI-related
incidents.
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