3.2 Algorithms and Code

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Unambiguous: Each step is clear and precise, leaving no room for interpretation.
Finite: The algorithm must eventually terminate after a finite number of steps.
Well-defined inputs and outputs: The algorithm requires specific inputs and produces specific outputs.
Feasible: The algorithm must be practical and efficient, able to be carried out with available resources.

Instructions: Step-by-step commands that make up the algorithm.
Variables: Named storage locations for data values that can change during the algorithm's execution.
Conditionals: Statements that control the flow of the algorithm based on specific conditions (e.g., if-else statements).
Loops: Structures that allow the algorithm to repeat a set of instructions until a specific condition is met.

Natural language: Describing the algorithm using everyday language and terminology.
Flow chart: A graphical representation of the algorithm using shapes and arrows to depict the flow of steps.
Code: Implementing the algorithm using a programming language.
Programming languages: Formal languages used to write algorithms, such as Python, Java, or C++.

Sorting: Arranging data in a specific order (e.g., alphabetical, numerical).
Searching: Finding specific data within a larger dataset.
Filtering: Selecting data that meets specific criteria while excluding the rest.
Prioritising: Arranging data based on importance or urgency.
Classifying: Grouping data into categories based on shared characteristics.
Associating: Linking related data items together.
Counting: Determining the number of items in a dataset.

Software development: Creating software applications using algorithms as building blocks.
Software implementation: Translating algorithms into functional software code.

Machine learning: Enabling computers to learn and improve from experience without being explicitly programmed.
Neural networks: A set of algorithms modeled after the human brain, designed to recognize patterns and learn from data.
Creation of other algorithms: Using AI techniques to generate new algorithms for specific tasks.

Algorithmic bias: The perpetuation of societal biases through algorithms, leading to unfair or discriminatory outcomes.
Fairness: Ensuring that algorithms treat all individuals or groups equitably.
Algorithmic accountability: Holding the creators and users of algorithms responsible for their impacts and decisions.
Transparency: Making the inner workings and decision-making processes of algorithms clear and understandable.
Black box algorithms: Algorithms whose internal workings are hidden or too complex to understand, making it difficult to identify and address issues.
Human judgment erosion and loss: The potential for over-reliance on algorithms to lead to a decrease in human critical thinking and decision-making skills.