Interactive learning cards to explore AI topics
AI ethics focuses on the moral implications and decisions made during the creation and implementation of AI technologies.
Ethical AI involves fair and transparent algorithms, responsible data use, privacy considerations, and ensuring AI doesn't perpetuate biases or inequality.
Privacy concerns in AI revolve around the use and potential misuse of personal data collected and processed by AI systems.
Ensuring privacy involves implementing strict data handling policies, using data anonymization techniques, and giving users control over their data.
Bias in AI refers to skewed outcomes produced by algorithms, often reflecting historical or societal inequalities.
Promoting fairness involves training AI on diverse data sets, regularly auditing algorithms for bias, and developing inclusive AI models that serve all communities equitably.
Transparency and explainability in AI emphasize the importance of making AI systems understandable to users and stakeholders.
This involves creating interpretable models, providing clear documentation, and ensuring stakeholders can understand AI decisions and the processes behind them.
Environmental ethics in AI focuses on the environmental impact of developing and running AI technologies, including energy consumption and e-waste.
Mitigating environmental impact involves optimizing AI models for energy efficiency, using sustainable energy sources, and recycling hardware in responsible ways.
Accountability in AI refers to the mechanisms in place to hold developers and users responsible for the effects their AI systems have on individuals and society.
Establishing effective governance involves creating clear ethical guidelines, legal frameworks for AI use and misuse, and mechanisms for redress when AI causes harm.