Machine Learning Bias: When algorithms produce biased results due to flawed data inputs or inherent biases within the system.
Automation Paradox: The idea that while automation can increase productivity, it can also lead to job displacement and economic inequality.
Algorithmic Transparency: The concept of making the decision-making processes of algorithms more understandable and accountable.