27 Technology and Ethics

Key concepts:

Machine learning — A type of AI where systems improve by analysing large amounts of data rather than being explicitly programmed for every task. For example, a spam filter learns to identify spam by studying millions of emails.

Automation — The use of technology to perform tasks with minimal human involvement. Factory robots, self-checkout machines and automated customer service chatbots are all examples.

Algorithm — A set of step-by-step instructions that a computer follows to solve a problem or make a decision. Social media algorithms decide which posts appear in your feed.

Generative AI — AI systems that can create new content, such as text, images, music or code, based on patterns learned from existing data.

Ethical questions around AI and automation:
- Bias: AI systems can reproduce and amplify existing biases in the data they are trained on. Facial recognition, for instance, has been shown to be less accurate for people with darker skin.
- Accountability: When an AI system causes harm — a self-driving car crash, a wrongful denial of a loan — who is responsible? The programmer? The company? The user?
- Job displacement: Automation may eliminate many jobs while creating new ones. The transition affects some communities more than others.
- Creativity and ownership: If an AI generates a painting or writes a song, who owns it? Can AI-created work be considered art?

How your data is collected:

1. Active data — Information you voluntarily provide: filling in forms, posting on social media, sending messages.

2. Passive data — Information collected without your direct input: browsing history, location tracking, cookies that follow you across websites, app usage patterns.

3. Inferred data — Information predicted about you based on patterns: your likely age, income level, political views or health status, all estimated from your online behaviour.

Key terms:

- Cookies — Small files stored on your device that track your activity across websites
- Data broker — A company that collects and sells personal data to advertisers, employers or other organisations
- End-to-end encryption — A security method where only the sender and receiver can read a message
- GDPR — The General Data Protection Regulation, an EU law that gives individuals greater control over their personal data
- Surveillance capitalism — A term coined by scholar Shoshana Zuboff describing the business model of collecting and monetising personal data. Others argue that data-driven business models provide free services to billions and can be managed through regulation like the GDPR

The privacy paradox:
Research shows that most people say they value privacy, yet they freely share personal information online and rarely read privacy policies. This gap between stated values and actual behaviour is called the privacy paradox.

The digital divide refers to the gap between people who have access to modern information and communication technology (ICT) and those who do not.

Three levels of the digital divide:

1. Access divide (first-level)
The most basic gap: some people simply do not have access to the internet or digital devices. According to the International Telecommunication Union, approximately 2.6 billion people — about one third of the world's population — were still offline in 2023. The gap is widest in sub-Saharan Africa, South Asia and among rural communities everywhere.

2. Usage divide (second-level)
Even among people with access, there are differences in how effectively they use technology. Factors include digital literacy, language (most online content is in English), age, education and confidence.

3. Outcome divide (third-level)
Even among people who use technology regularly, the benefits are not equally distributed. Some use the internet to access education, apply for jobs and participate in democracy. Others use it mainly for passive consumption.

Why the digital divide matters:
- Education: Students without reliable internet fall behind, as the COVID-19 pandemic made painfully clear.
- Employment: Many jobs now require digital skills. Those without them are excluded from a growing part of the economy.
- Democracy: Online government services, digital voting information and political discussion increasingly happen online, leaving offline citizens out.
- Health: Telemedicine and online health information are inaccessible to those without connectivity.

The digital divide is not only a technology problem. It is a justice problem that intersects with poverty, geography, gender and age.