Artificial Intelligence, Machine Learning & Robotics (Edexcel GCSE Computer Science)
Revision Note
Written by: Robert Hampton
Reviewed by: James Woodhouse
What is artificial intelligence?
Artificial intelligence (AI) is a machine that can display intelligent behaviours similar to that of a human
AI is a system that can:
Learn - acquire new information
Decide - analyse and make choices
Act autonomously - take actions without human input
What is machine learning?
Machine learning is one method that can help to achieve an artificial intelligence (AI)
By giving a machine data so that it can 'learn over time' it helps towards training a machine or software to perform a task and improve its accuracy and efficiency
What is robotics?
Robotics is the principle of a robot carrying out a task by following a precise set of programmed instructions
Robots can be categorised into two groups:
Dumb robots | Smart robots |
|---|---|
Repeat the same programmed instructions over and over again (no AI) | Carries out more complex tasks and can adapt and learn (AI) |
E.g. Car assembly line | E.g. Assisting surgeons in delicate procedures |
The development of artificial intelligence, including the increased use of machine learning and robotics raises ethical and legal issues such as:
Accountability
Safety
Algorithmic bias
Legal liability
Accountability & Safety
Why is accountability & safety an issue?
Accountability can be an ethical issue when the use of AI leads to a negative outcome
Safety can be an ethical issue when you try to ensure safety in an algorithm that is designed to make it's own choices, learn and adapt
The choices made by AI will have consequences, who is held accountable when things go wrong?
Driverless car accident
Scenario | Ethical issues |
|---|---|
As a passenger in a driverless car, the car suddenly swerves to miss a child in the road and kills a pedestrian walking on the pavement |
|
Algorithmic Bias & Legal Liability
Why is algorithmic bias an issue?
Algorithmic bias can be an ethical issue when AI has to make a decision that favours one group over another
If data used in the design of AI is based on real-world biases then the AI will reinforce those biases
If the programmer of the AI has personal biases they could make design decisions that reinforce their personal biases
Loan approvals
Scenario | Ethical issues |
|---|---|
A bank introduces the use of AI to streamline loan approvals. Historical loan data is used and a client is denied based on historical loan approval rates in certain races or post codes |
|
Why is legal liability an issue?
Legal liability is an issue in all aspects of AI, but particularly when the use of AI leads to the loss of human life or criminal activity
In the eyes of the law, who is responsible?
The programmer?
The manufacturer?
The consumer?
Smart toy
Scenario | Legal issues |
|---|---|
A person buys a smart toy designed to interact with a child and personalise the play experience, learning their preferences etc. A hacker gains access to the smart toy stealing personal data |
|
Worked Example
A hospital uses an algorithm to help decide how many nurses are needed on each day
Discuss how algorithmic bias can affect the decision the hospital makes [6]
Your answer should consider:
the cause of algorithmic bias
the impact on induvial and communities of algorithmic bias
the methods available to reduce the risk of algorithmic bias
Answer
Causes of algorithmic bias
Algorithms being trained used historical data - past scheduling practices not fair. the algorithm would continue the bias
Algorithm design focussed on efficiency over fairness - filling shifts without considering experience
Lack of transparency - hard to check and fix any potential bias
Impacts of algorithmic bias on individuals and communities
Nurse safety - unfair scheduling could lead to nurse burnout, leading to medical errors
Unequal scheduling - bias could lead to groups of nurses being assigned more shifts than others or regularly assigned undesirable hours
Patient care - short staffing compromising patient care
Methods to reduce algorithmic bias
Human oversight - algorithmic recommendations should be reviewed and adjusted by human schedulers first
Transparency - nurses and all employees should understand how the algorithm is making decisions so that concerns can be raised if needed
Auditing - regular audits to identify and address any emerging bias in the algorithms output
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