In the absence of any meaningful government action, though, it's up to us to take it seriously. But every time we talk about the pandemic in the past tense, we make ourselves feel a little safer. A little more complacent. A little less likely to take care.
Measurable or Meaningful: Pick One
Assessment is all down to this simple number. Objectivity guaranteed. But if there is a correlation between socioeconomic status… if girls are driven out of particular subjects by the perception that they are not suited to them… if rural kids don’t have access to the same range of subjects… if some schools don’t have great teachers or support structures… then what we have is the pretence of objectivity and fairness, rather than actual objectivity and fairness.
The power of definitions
It doesn’t matter what technology you teach, when you’re teaching Data Science. I don’t care whether you use Python, R, spreadsheets, or stacking blocks to make graphs and analyse your data. What matters, above all else, is that you teach your students to ask critical questions about the data. How was it collected? What are the definitions you used? How do we know the definitions are valid? What other definitions could we use, and how would that change the data?
Why doesn’t technology bring us great joy?
Whether hardware or software, smartphone or car, laptop or microwave? Have you ever looked at a piece of technology and thought "This is a gloriously beautiful thing that does exactly what I need it to do, and brings me great joy?"
Consider the impact
Real world problems don't have perfect, textbook answers. Real world solutions have downsides as well as upsides. And complex, multifaceted problems such as those we are trying to tackle with AI and Data Science are going to need complex, multifaceted, and carefully tested solutions.