Tag : Explainable AI

Sustainable AI means developing and using artificial intelligence in a way that minimizes its negative environmental, social and ethical impacts and maximizes its positive contributions to a sustainable future.

AI literacy is the ability to understand, use, and critically evaluate artificial intelligence systems, including their benefits, risks, and ethical implications.

The AI ​​governance framework ensures responsible AI development by establishing policies for fairness, transparency, privacy, accountability, and safety. This includes ethical guidelines, risk management, human oversight, and regulatory compliance to prevent bias, misuse, and harm while maximizing the benefits of AI.

Bias and fairness in AI are important concerns as AI systems influence decisions in areas such as employment, healthcare, and justice. Bias arises from problems with data (e.g., historical or sampling bias), algorithmic design, and implementation context. This can lead to discrimination, inequality, and loss of trust. Fairness in AI seeks to eliminate bias by promoting equal opportunity, demographic parity, and transparency. Addressing these issues requires using diverse datasets, fairness-respecting algorithms, human oversight, ethical frameworks, and ongoing monitoring. Despite challenges such as fair trade-offs in terms of accuracy and cultural differences, ensuring fairness is key to building responsible, inclusive, and trustworthy AI systems.