Framework (in Trustworthy AI)
2024 | AI Dictionary
A structured approach or set of principles for designing, developing, and deploying trustworthy AI systems.
What is a Framework (in Trustworthy AI)?
Trustworthy AI Framework refers to a structured approach or set of principles that guide the design, development, and deployment of AI systems in a manner that ensures they are reliable, ethical, and transparent. It focuses on ensuring that AI systems operate with fairness , accountability, transparency , and alignment with societal values, helping mitigate risks like bias, discrimination, and unfair outcomes.
Types of AI Frameworks
- Ethical AI Frameworks: Focus on the ethical implications of AI systems, ensuring they adhere to moral principles like fairness, transparency, and privacy.
- Regulatory Compliance Frameworks: Ensure AI systems comply with legal and regulatory standards, such as the EU AI Act .
- Risk Management Frameworks: Help identify, assess, and mitigate risks associated with AI systems, including operational, security , and societal risks.
Applications of Frameworks
- AI Model Development: Ensures that AI models are designed with fairness, transparency, and privacy in mind.
- Regulatory Compliance: Helps organizations align their AI systems with legal requirements and standards.
- Impact Assessment : Evaluates the potential social, ethical, and economic impacts of AI systems before deployment.
Example of a Framework
An Ethical AI Framework might include guidelines for ensuring that a machine learning model does not inadvertently introduce biases, such as designing the training dataset to be more representative or implementing auditing systems to monitor fairness throughout the model’s lifecycle.
Did you liked the Framework (in Trustworthy AI) gist?
Learn about 250+ need-to-know artificial intelligence terms in the AI Dictionary.