(upbeat music) - In data, we have to start asking how do we educate people about ramifications of their data, but also opportunities where data could beneficially help people. There's a lot of times where if people did supply more data, you could actually provide better services for them. How do we do that? How do we start talking about those things? There's a whole rich area in here of questions about, say like what data you give up when you fill out a form, when you click a button on an app, when there's data that's collected about you that you had no idea because you have a loyalty card or something that you swipe every time you pick up a new medication or buy a grocery.
We have to help people see that world. But also, a lot of that responsibility sits on the technologist to help really take care of the customers and the public. What does it start to look like when we talk about the what does a number mean, what does data look like? I've concretely put that around the question of vaccinations. We have this thing where we know vaccinations save a tremendous amount of lives.
It's well proven, yet people question it. People don't want to have their kids get vaccinated. Yet, if you don't, you're really fundamentally putting your child's life at risk. We've just gotten comfortable, we've gotten complacent. We're also ignoring the issues and the data around the potential of a pandemic. The reason I bring it up this way, it may seem sort of off topic, but it's actually tethered together because there's your individual data and there's society data.
Society's data is also critically dependent on making population health work, whether it's for just better wellness, addressing obesity, diabetes, or all the way into the extreme of a pandemic. And then there's your individual data of how do you make sure that you are protected, your family's protected? Let me give you a direct litmus test. In genomic testing, if you give up your data by a genetic test, your spouse gives up their data, enough of your family gives out data, you've effectively given up your children's data.
Mathematically, that's just the way the probabilities work. What if your child says I don't want to be in that dataset? Now, there's the other side that by combining enough of these datasets with that genomic information might save their life because they're going to have a tailored medical treatment that is derived from their genetic results. How do we balance that? That's a conversation that needs to be had today. Another version right now is around the genetic test around what are BRCA1 and 2 which are the genetic tests for breast cancer.
What do you do when you have those tests? Do you take a radical approach to having a double mastectomy or other aggressive treatment? What's the right cause? Let me put that in the lens of how much medical treatment policy has changed so radically in the last several years. We've completely changed the route of prostate exams. We've completely changed estrogen therapy and mammogram recommendations. Because we've gotten smarter through the data.
But what are the implications of all this? There's no simple solutions here. There's only an approach that's going to work, is where we collectively stay on top of this issue and constantly are asking what is right by everybody, and how do we make those decisions? This gets back to, in many ways, the only way to do this is with a fundamental, very good training in the humanities and liberal arts because these are ethical questions. In the classic Kant framework of, does the needs of an individual outweigh the needs of the total population, the all? These are questions that we have to get to and can't be solved by technology alone.
They have to be a question of how do we view society and what's this world we want to live in? (upbeat music)