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IB
A4.4.1 Ethical Implications of Machine Learning in Real-World Scenarios

A1.1 Computer hardware and operation

A1.2 Data representation and computer logic

A1.3 Operating systems and control systems

A1.4 Translation (HL only)

A2.1 Network fundamentals

A2.2 Network architecture

A2.3 Data transmissions

A2.4 Network security

A3.1 Database fundamentals

A3.2 Database design

A3.3 Database programming

A3.4 Alternative databases and data warehouses (HL only)

A4.1 Machine learning fundamentals

A4.2 Data preprocessing (HL only)

A4.3 Machine learning approaches (HL only)

A4.4 Ethical considerations

B1.1 Approaches to computational thinking

B2.1 Programming fundamentals

B2.2 Data structures

B2.3 Programming constructs

B2.4 Programming algorithms

B2.5 File processing

B3.1 Fundamentals of OOP for a single class

B3.2 Fundamentals of OOP for multiple classes (HL only)

B4.1 Fundamentals of ADTs

IB Computer Science Topic A4.4.1: Ethical Implications of Machine Learning in Real-world Scenarios Key Definitions

A4.4.1 Ethical Implications of Machine Learning in Real-World Scenarios Key Definitions

Definition

Algorithmic fairness

The property of a machine learning system whose outcomes do not depend on group membership such as sex, ethnicity or age.

Definition

Bias

An unfair tendency for or against particular individuals or groups. In machine learning it is learned from patterns already present in the training data.

Definition

Ethical implication

A possible consequence of a decision or action for someone's well-being, rights, fairness or freedom.

Key Definitions