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Year 11 Philosophy

Ethics and Technology

Explore the ethical dimensions of artificial intelligence, digital privacy, algorithmic bias, and digital rights in an increasingly technology-driven world.

AI Ethics

Artificial Intelligence (AI) raises profound ethical questions about accountability, transparency, and fairness. As AI systems make decisions about loan approvals, medical diagnoses, hiring, and even criminal sentencing, we must ask: who is responsible when an AI makes a harmful decision? How do we ensure these systems are fair?

Key ethical concerns include the "black box" problem (AI decisions that cannot be easily explained), job displacement through automation, the use of AI in weapons systems, and the potential for AI to reinforce existing social inequalities. Ethical AI requires transparency, accountability, and human oversight.

Ethical AI Principles

Transparency

AI decisions should be explainable and understandable to those affected.

Accountability

Clear responsibility must exist for AI-driven outcomes and errors.

Fairness

AI systems must not discriminate against individuals or groups unfairly.

Human Oversight

Humans should remain in control of high-stakes AI decisions.

Privacy & Digital Rights

In the digital age, our personal data is constantly being collected, stored, and analysed by technology companies, governments, and advertisers. Digital privacy is the right to control what personal information is collected and how it is used. This includes your browsing history, location data, messages, photos, and biometric data (fingerprints, facial recognition).

Digital rights extend beyond privacy to include freedom of expression online, the right to access information, and protection from surveillance. In Australia, the Privacy Act 1988 and the Australian Privacy Principles (APPs) regulate how organisations handle personal information, but many argue these protections need updating for the modern digital landscape.

Key Digital Rights Questions

  • Should companies be allowed to sell your personal data to advertisers?
  • Is mass government surveillance ever justified for national security?
  • Do you have a "right to be forgotten" online?
  • Should facial recognition technology be used in public spaces?

Algorithmic Bias

Algorithmic bias occurs when an AI system produces systematically unfair results, often reflecting and amplifying existing human biases present in the training data. For example, a hiring AI trained on historical data might discriminate against women if past hiring decisions were biased toward men.

Bias can enter AI systems through biased training data, flawed assumptions in the algorithm design, or lack of diversity among the people building these systems. Addressing algorithmic bias requires diverse development teams, regular auditing of AI systems, and inclusive datasets that represent all segments of society.

Real-World Examples of Algorithmic Bias

  • Facial recognition: Higher error rates for people with darker skin tones.
  • Hiring algorithms: Penalising applicants from certain postcodes or schools.
  • Credit scoring: Systematically disadvantaging minority communities.
  • Healthcare AI: Under-prioritising certain patient groups based on historical spending data.

Key Vocabulary

Algorithmic Bias

Systematic unfairness in AI system outputs, often caused by biased training data or flawed design assumptions.

Digital Privacy

The right to control what personal information is collected about you and how it is stored, shared, and used.

Transparency

The principle that AI decision-making processes should be open, explainable, and understandable to those affected.

Surveillance Capitalism

An economic system where profit is derived from the mass collection and commodification of personal data.

Worked Examples

1

Analysing an AI ethics dilemma

A hospital uses an AI system to prioritise patients in the emergency department. The AI consistently ranks elderly patients lower. Analyse the ethical issues.

Step 1 -- Identify the bias: The AI may have been trained on data that correlates age with treatment outcomes, leading it to deprioritise elderly patients.

Step 2 -- Apply ethical principles: Fairness: all patients deserve equitable care. Transparency: the AI's decision criteria should be explainable. Accountability: who is responsible if an elderly patient is harmed?

Answer: This scenario highlights the need for human oversight in high-stakes AI decisions, regular bias auditing, and the importance of ensuring AI systems align with medical ethics principles of patient dignity and equitable care.

2

Evaluating a privacy trade-off

A free social media platform collects user data to fund itself through targeted advertising. Is this an ethical trade-off?

For: Users get a free service; data collection enables personalised content; the business model sustains platform development.

Against: Users may not fully understand what data is collected; consent is often buried in long terms of service; data breaches can expose personal information; users become the "product."

Answer: The ethics depend on informed consent, transparency about data use, and whether users have genuine alternatives. Many ethicists argue that "free" services with data collection are not truly free.

3

Identifying algorithmic bias

A bank's AI loan approval system rejects applications from a particular suburb at a higher rate, even when applicants have good credit. What might be happening?

Step 1: The AI may use postcode as a factor, correlating location with historical loan defaults.

Step 2: If that suburb has a higher proportion of a particular ethnic group, the AI is effectively discriminating based on race -- even without explicitly using race as a factor.

Answer: This is an example of proxy discrimination -- where neutral-seeming data points (postcode) act as proxies for protected characteristics (race). The solution involves auditing the AI for disparate impact and removing or adjusting biased features.

Knowledge Check

Select the correct answer for each question. Click "Check Answer" to see if you are right.

Question 1

What is the "black box" problem in AI ethics?

Question 2

Algorithmic bias most commonly enters AI systems through:

Question 3

Which Australian legislation regulates how organisations handle personal information?

Question 4

The principle that AI decisions should be explainable and understandable is known as:

Question 5

What is "proxy discrimination" in AI systems?

Key Concepts Summary

Year 10: Knowledge