Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.
Applications and devices equipped with AI can:
AI governance refers to the framework of policies, principles, and practices that guide the ethical development, deployment, and use of artificial intelligence technologies. Proper governance is the backbone of responsible AI, ensuring that these technologies responsibly advance decision-making processes.
An AI governance framework provides organizations with a structured approach to navigating the ethical considerations of AI, ensuring transparency, accountability, and explainability of AI systems. This framework is not just about compliance; but about building trust and confidence in AI technologies among users and stakeholders, ensuring that the benefits of AI are realized responsibly and equitably.
Explainability: Designing AI systems so people can understand why they make certain decisions. This involves ensuring that the internal workings of an AI system and how it uses input data to reach conclusions are clear and easy to follow.
Accountability: Clear attribution of responsibility for the actions taken by AI systems. If something goes wrong, there should be a process in place to address issues, mitigate biases, or unintended consequences.
Safety: AI systems must be designed and deployed in a manner that ensures the safety and well-being of all users, with respect for human rights.
Security: Protecting AI systems from breaches and unauthorized access, safeguarding the confidentiality, integrity, and availability of both the data and the system.
Transparency: Making the workings of an AI system open and accessible, clearly sharing insights into how an AI model is developed, deployed, and used.
Fairness and Inclusiveness: Designing and operating AI systems to avoid bias and provide impartial, just, and equitable decisions, promoting inclusiveness and equality.
Reproducibility: The ability to recreate the results produced by AI systems under the same conditions, essential for validating reliability and accuracy.
Robustness: Developing AI systems to withstand tampering and manipulation, ensuring reliable operation even under unexpected conditions.
Data Governance: Protecting personal data and upholding data privacy, ensuring sensitive information is protected while maintaining system integrity.
Trust in AI systems is built upon the assurance that they operate ethically and responsibly. An AI governance framework establishes clear guidelines and standards to ensure that AI technologies adhere to ethical boundaries and societal norms.
AI systems rely heavily on data, and it’s imperative that this data is handled correctly and stays protected. An effective AI governance framework ensures that data collection, processing, and usage adhere to regulatory requirements and ethical standards.
By establishing clear processes for data governance and aligning AI initiatives with ethical boundaries and societal norms, the framework ensures that data-driven decisions are effective and socially responsible.
Machine learning involves creating models by training an algorithm to make predictions or decisions based on data. It encompasses techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks.
Types of machine learning techniques include:
Neural networks are modeled after the human brain’s structure and function, consisting of interconnected layers of nodes that work together to process and analyze complex data. They are well suited to tasks that involve identifying complex patterns and relationships in large amounts of data.
Deep learning is a subset of machine learning that uses multilayered neural networks (deep neural networks) that more closely simulate the complex decision-making power of the human brain. Deep neural networks include an input layer, multiple hidden layers, and an output layer.
Deep learning enables:
Generative AI refers to deep learning models that can create complex original content—such as long-form text, high-quality images, realistic video or audio—in response to a user’s prompt or request.