thinking on how hyper-precision l, huge levels of data capture and hard mining it is dangerous in the wrong hands
eg segmenting a group to target, discriminating against a group by associated characteristics indirectly
correlation != causation
hyper-precise data capture with no specific goal (eg with machine learning) could surface correlations before mainstream science has this data
seeded by considering not everything should be IoT capable on seeing IoT toothbrushes and hypothesising a danger, what if the statistical likelihood of low dental hygiene in certain medical conditions could cause unwarranted and nonconsensual segmenting of this population of toothbrush users