IB Digital Society 3.1G Data Dilemmas Notes

3.1G Data Dilemmas

What Makes a Data Dilemma

1. A data dilemma is a genuine conflict of values. One use of data can help some people while harming others, and no option keeps everyone satisfied. That conflict is what makes it a dilemma, with no clean right answer.
2. Often the benefit and the harm come from the same act. Sharing health records speeds up research while exposing private lives. A camera network makes a street safer while turning it into a place of constant watching.
3. The syllabus groups tensions into three families: bias, reliability and integrity (is the data fair and trustworthy?); control, ownership and access (who holds it and who may use it?); and privacy, anonymity and surveillance (what does it reveal and who watches?).

Bias: When Data Encodes Unfairness

1. Data is collected by people, from some situations and not others, so it can carry the imbalances of the world it came from. When that skew is systematic it is called data bias.
2. Bias enters via biased collection (over-representing one group) or from the historical record itself. When a system then produces skewed outcomes, that is algorithmic bias.
3. Example: a large online retailer built a CV-screening tool trained on a decade of mostly male hires. It taught itself to mark down CVs mentioning women's activities and was scrapped. Nothing said to prefer men; it faithfully copied a skewed past.
4. The dilemma: data can look complete and neutral while working against the people it under-counts. Naming who is missing from the data is the first move in spotting the bias.
5. Stakeholders feel harm unequally: the organisation sees efficiency and apparent objectivity, while the under-counted group is quietly filtered out with no visible cause, making bias as much a question of power as of accuracy.