There’s no universally agreed definition for artificial intelligence so we have compiled some useful information to reflect on.

What is an AI system?

An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.

AI Bias or Algorithm Bias

Like humans have biases there will be biases in AI models. These biases typically arises from:

The design of models themselves and the training data they use.
The assumptions of the developers coding them, which causes them to favour certain outcomes.
The data used to train the AI.
The data and content being used is accurate, reliable, and free from bias.

AI Life Cycle/Change Management

As in the real world, life cycle and change management are methods of managing and reducing uncertainity in systems. An AI system lifecycle typically involves several phases that include:

To plan and design
Collect and process data
Build model(s) and/or adapt existing model(s) to specific tasks
Test, evaluate, verify, and validate
Make available for use/deploy
Operate and monitor
Retire/decommission.

These phases often take place in an iterative manner and are not necessarily sequential. The decision to retire an AI system from operation may occur at any point during the operation and monitoring phase.

AI and Data Ethics

The AI Model or Agent is not aware of the ethics of the data collected for use by the AI. Data Ethics include:

Privacy: Ensuring that personal information is collected, stored, and used in ways that protect individuals’ privacy and comply with legal requirements.

Transparency: Being open about how the data is collected, used, and shared.

Consent: Obtaining informed consent from individuals before collecting their data.

Security: Protecting data from unauthorised access, breaches, or cyberattacks to maintain its confidentiality, integrity, and availability.

Soverenity: Data stored outside the country in which it was collected.

Fairness: Ensuring data practices do not result in discrimination or bias and that data will be used in ways that are both equitable and just.

Accountability: Holding organisations and individuals accountable for their data practices and ensuring there are mechanism in place to address any issues if they arise.

Data Transparency in AI Models and Agents

Data transparency is providing clear and accessible information about the data used in AI systems. This includes understanding where the data comes from, how it has been collected, processed, and used, and making processes more open and understandable to stakeholders.

Hallucination

The term hallucination is when AI create incorrect yet convincing outputs.

Misinformation

Misinformation refers to false or inaccurate information that’s spread regardless of an intent to deceive. Unlike disinformation, which is deliberately misleading, misinformation is often shared without malicious intent.

Predictive AI

Predictive AI (or predictive analytics) Involves using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

AI Transparency

Making the operation and decision-making processes of AI systems clear and understandable to users and stakeholders. Key components of transparency are:

Openness: Clearly communicating the purpose and capabilities of an AI system. This includes explaining what the system is designed to do and any limitations it may have.

Explainability: Providing understandable explanations of how the AI system reaches it decisions.

Accountability: Ensuring that there’s a mechanism for tracking and verifying decisions made by the AI. This can include maintaining logs, version control and audit trails

Data Transparency: Disclosing what data is used to train and operate the AI system, including its sources and how its processed.