AI & Accountability
When a machine makes the decision, who is responsible when it goes wrong? Go deeper into the accountability, safety, algorithmic bias and legal liability of AI, machine learning and robotics.
Revise this, the fun way
Play it interactively, earn XP and build a streak, free.
Start revising freeWhat you'll cover
When a machine decides 🤖
AI, machine learning and robotics increasingly make decisions that people used to make: approving a loan, sorting job applications, even driving a car. When one of those decisions goes wrong, **who is responsible**? This module digs into the four ideas the exam cares about: **accountability**, **safety**, **algorithmic bias** and **legal liability**.
Four words that matter 🔑
These four ideas run through every AI ethics question:
Where does the bias come from? 🎯
A recruitment AI was trained on a company's past hiring, which had mostly hired men. It now rejects strong female applicants. What is the root cause?
- The training data was biased, so the model learned that bias
- The computer dislikes certain applicants on purpose
- The applicants filled in the form incorrectly
- AI is always random, so its results mean nothing
Match each issue to an example 🔗
- Accountability
- Safety
- Algorithmic bias
- Legal liability
- A hospital must be able to say who signed off an AI diagnosis
- A delivery robot is tested so it cannot injure pedestrians
- A loan model rejects one postcode far more often, unfairly
- A court decides who must pay after a self-driving car crash
Who could be to blame? ⚖️
When an autonomous system causes harm, the law has to decide who is **liable**. It is rarely just one party, and the answer depends on the exact scenario.
Which raise a bias issue? 🔍
Select the TWO situations that mainly raise an algorithmic BIAS issue.
- A facial recognition system misidentifies people with darker skin far more often
- A CV-screening AI scores women lower than men with the same experience
- A surgical robot arm moves unexpectedly and could injure a patient
- A firm copies a rival's logo onto its own product
Be specific, or lose marks ⚠️
In the exam, never just write "AI can be unfair". For the marks, **name a specific issue tied to the scenario**, say **who** is affected or accountable, and suggest a **safeguard**. For example: "A self-driving car brakes late for pedestrians it was under-trained to recognise. That is a **safety and bias** issue; the **developer** is accountable; the fix is **wider testing data plus human oversight**."
Complete the summary 🧱
Machine learning learns its patterns from _____, so unrepresentative examples lead to algorithmic _____. Because the system then decides on its own, the law must decide who holds legal _____ for any harm. Good practice adds human _____ so a person can step in, and keeps the system _____ enough for its decisions to be explained.
Build it responsibly 🪜
An interactive activity.
The crash decision 🚗
An interactive activity.
No excuse 🙅
An online shop's AI wrongly cancels thousands of valid orders. A manager says "the algorithm did it, so no one is responsible". Why is that wrong?
- People chose to build, train and deploy the system, so responsibility stays with them
- The algorithm itself should be sent to prison
- Customers agreed to the mistake by shopping there
- AI decisions are never actually wrong
Spot the exam-ready answers 🖍️
An interactive activity.
Make the case ✍️
An interactive activity.