IB Digital Society 3.2E Algorithmic Dilemmas Notes

3.2E Algorithmic Dilemmas

The Double Edge of Algorithms

1. Algorithms are prized for being fast, consistent, and able to run at huge scale. An algorithmic dilemma arises when those strengths turn into problems. The same efficiency that makes an algorithm useful also magnifies any mistake it carries.
2. Because an algorithm applies the same rule to everyone, a single flaw repeats identically across millions of cases. A biased hiring algorithm can shape the chances of every applicant a company ever sees.
3. These dilemmas are about responsible use, not a reason to abandon algorithms. Four are sharpest: bias, the meaning of fairness, transparency, and the erosion of human judgment.

Algorithmic Bias

1. Algorithmic bias is a systematic error in an algorithm that produces unfair outcomes, often favouring one group. Systematic means a consistent tilt, not random noise, disadvantaging the same people repeatedly.
2. Bias rarely means deliberate discrimination; it creeps in through the data. A model trained on historical records absorbs past unfairness as its target. It can also enter through feature choices and through teams too similar to spot problems affecting others.
3. The COMPAS US risk-scoring tool flagged Black defendants as future reoffenders at nearly twice the false-positive rate of white defendants, though race was never written into the rules. The bias was inherited from a justice system's history, not invented.
4. The dangerous part is that the output looks neutral and mathematical, giving old prejudice a new air of objectivity. Because the bias is spread evenly and dressed in numbers, it can run for years before anyone proves it, all the while shaping who gets a fair chance (power).