Key concepts: Artificial intelligence
Author: Trusted Autonomous SystemsPublished: 28/06/2022Category: Body of KnowledgeLast updated: 08/08/2022
Key concepts: Artificial Intelligence
Artificial intelligence (AI) may be defined as a collection of interrelated technologies used to solve problems autonomously and perform tasks to achieve defined objectives, in some cases without explicit guidance from a human being. Subfields of AI include machine learning, computer vision, human language technologies, robotics, knowledge representation and other scientific fields.
Australia defines artificial intelligence by its functions (sensing, learning, predicting, independent action), focus and degree of independence in the achievement of defined objectives with or without explicit guidance from a human being. The Australian definition encompasses the role of AI in digital and physical environments without discussing any particular methodology or technology that might be used.
In doing so it aligns with the OECD’s Council on Artificial Intelligence’s definition of an AI system:
AI system: An AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy.1
RAS-G is specifically focused on autonomous systems being developed for use in maritime, air, and land domains within Australia. However, these broader definitions are useful when it comes to linking into international discussions on the development of trusted and trustworthy autonomous systems.
| OECD and trustworthy AI The OECD AI Principles provide five ‘values-based’ principles for the responsible stewardship of trustworthy AI inclusive growth, sustainable development and wellbeing: stakeholders should engage in creating credible AI that contributes to inducing outcomes that are beneficial for people and planet human-centred values and fairness: the values of human rights, democracy, and rule of law should be incorporated throughout the AI system’s lifecycle, while allowing human intervention through safeguard mechanisms transparency and explainability: AI actors that develop or operate AI systems should provide information to foster an overall understanding of the systems among stakeholders, in which people affected by AI systems could comprehend the outcome and challenge the decision when needed robustness, security and safety: AI systems need to function appropriately while ensuring traceability. AI actors need to apply systematic risk management approaches to mitigate safety risks accountability: AI actors should respect the principles and should be accountable for the proper operation of AI systems. |
- OECD, “The OECD Artificial Intelligence (AI) Principles.” OECD AI Policy Observatory, 2021. https://oecd.ai/en/ai-principles. ↩