IB Digital Society 4.7B Social Class Notes

4.7B Social Class

1. Organising concept is power; digital systems can widen or narrow the gap between rich and poor and decide who gets access, a fair wage, or a loan.
2. Supporting concepts: space (class maps onto geography) and systems (a small design choice ripples across whole communities).
3. Content topics: networks (who is connected), data and artificial intelligence (allocating work and money), and values and ethics throughout since livelihoods are at stake.

The Digital Divide

1. The digital divide is the gap between those with good access to digital technology and those without.
2. It falls along class and geographic lines: in 2025 roughly 94 percent of people in high-income countries are online against about 23 percent in low-income ones, with around 2.2 billion still offline; urban far outpaces rural.
3. A second-level divide concerns quality of access and skills: a fast home connection and laptop open doors a shared, capped phone does not.
   1. Impacts vs implications: measurable gaps in access, speed, and skills, which shut people out of school, work, and services and compound over time as more of life moves online.

Work in the Gig Economy

1. The gig economy uses apps to connect workers to short tasks: driving, delivery, cleaning, or online micro-work.
2. The manager is an algorithm: algorithmic management assigns jobs, tracks, and scores workers by rating, offering flexibility but little security and pay that can fall below a wage floor.
3. Many platforms class workers as self-employed, removing rights labour has long held.
   1. Contested: the EU Platform Work Directive (2024) introduces a presumption of employment and, for the first time, requires human review of algorithmic management decisions.

Algorithms and Inequality

1. Automated decision-making sorts people in ways that track class: who gets credit, welfare, insurance, or extra police attention.
2. Learning from a society's past, systems inherit its inequality: a credit model trained on records where poorer people were denied keeps denying them; predictive policing over-patrols already over-policed areas, a feedback loop.
3. Automation tends to replace routine, lower-wage jobs first, pushing the cost of change onto those least able to absorb it.
   1. Values and ethics: a decision that looks neutral because a computer made it can still be deeply unfair (entrenched disadvantage, hidden discrimination).

Digital Systems That Lift People Up

1. Financial inclusion: mobile money lets people without a bank account save, send, and borrow using a basic phone.
2. In Kenya, M-Pesa helped raise access to formal financial services from around three-quarters of adults in 2016 to over four-fifths by 2021, and studies estimate it lifted many households out of extreme poverty.
   1. Cheap smartphones open online learning, wider markets for small sellers, and remote work once out of reach.
   2. Impacts vs implications: real gains in inclusion, income, and opportunity, against dependence on private platforms, new debt risks, and anyone without a device left further behind.

Weighing a Contested Balance

1. Two-sided: systems can widen access, open opportunity, and include people the old economy shut out. The same systems can deepen the divide, entrench inequality inside opaque models, and make work precarious.
2. The result depends on who is connected, whether algorithms can be seen and challenged, how workers are classified, and whom the tools serve; left unexamined they tend to harden existing inequalities.