21세기 데이터 해독 능력의 중요성

### 21세기 데이터 해독 능력의 중요성
* 📚 21세기에는 문해력이 **전통적인 읽기 및 쓰기 능력을 넘어 정보와 데이터를 이해하는 데 초점**을 맞추고 있습니다.
* 📱 데이터가 실시간으로 수집되어 타겟팅 광고에 사용되는 경우가 많기 때문에 많은 사람들이 **자신의 기기가 자신의 말을 듣고 있다는 기묘한 느낌**을 경험한 적이 있을 것입니다.
* 👩 티아 윌리엄스 박사는 **차량 공유 앱 사용부터 개인 건강 지표 추적**에 이르기까지 모든 사람이 매일 데이터를 활용하고 있으며, 우리 모두는 그 자체로 데이터 과학자라고 강조합니다.
* 🌿 피보나치 수열과 프랙탈과 같은 수학적 개념은 자연 속 수학의 아름다움을 보여주며 **수학과 우리 주변 세계 사이의 연관성**에 대해 더 깊이 이해할 수 있도록 영감을 줍니다.
* 🔍 데이터를 분석하려면 통계 모델을 제대로 이해하지 못하면 오해의 소지가 있을 수 있으므로 **데이터를 통해 알 수 있는 것과 알 수 없는 것을 구분할 수 있는 비판적 사고**가 필요합니다.
* ⚖ 데이터의 편향성을 인식하는 것의 중요성은 편향되지 않은 것처럼 보이지만 데이터의 근본적인 편향성으로 인해 **치료의 인종 격차를 지속시킨 의료 알고리즘**을 통해 강조됩니다.
* 📈 사회가 점점 더 AI와 머신러닝에 의존함에 따라, 개인이 자신의 삶에 영향을 미치는 결정에 참여하고 이해하기 위해서는 **데이터 리터러시를 키우는 것**이 필수적입니다.
* 🤝 궁극적으로 데이터에 능통한 사회는 개인이 다른 사람이 내린 결정을 수동적으로 받아들이는 대신 **정보에 입각한 선택**을 할 수 있도록 지원합니다.

데이터 리터러시는 개인이 정보에 입각한 의사 결정을 내리고 일상 생활에서 통계의 의미를 이해할 수 있도록 지원합니다.

[Music] Literacy of the 21st century is more than just reading and writing. It really is understanding information and understanding data. If you've ever felt like your phone is listening to you, often it is. You may talk about green rugs, and then all of a sudden, you pull up Facebook or Instagram, and you scroll down. It's like, Whoa, here's an advertisement for a green rug. That data is being transferred in real-time and then turned into a commercial or an ad for you. With the rise of AI, it's easy to use that for corruption. And so, as a society, when we help move everyone towards statistical literacy and data literacy, it forces us to be very objective in our analysis, to take in all different viewpoints and different pieces of information, and it empowers everyone because we can all make our own informed rational choice instead of depending on the belief of someone else or trusting the belief of someone else because we don't understand the information that's right in front of us. My name is Dr. Tiia.

Williams and I am a math professor in a science communicator. So, every day we're doing math and working with numbers just to live in society. And so really, all of us are statisticians and data scientists and mathematicians. If I want to get a ride to the airport and I do Uber or Lyft, I'm looking at it and I'm interacting, and it's telling me a distance and a time and a price or exercise data, right? You know, the number of steps that you took per day or, you know, what was the range of your heart rate today, how much movement did you have. All of these things can not only help us make decisions but can help our doctor understand when things are going wrong in our system or things are working right. There are so many ways that math and statistics show up in the beauty of nature. The Fibonacci sequence is a wonderful example of how it shows up in the pattern of leaves on a plant or things like fractals. Fractals are seen in the human body with the way that

blood vessels sort of grow and move around and expand. So many of these mathematical concepts that we studied and understand have direct occurrences in the world around us in ways that are just beautiful and awe-inspiring. And so, part of my excitement is to make those connections and help people see that if they walk outside, there's mathematics right in front of them that they can be really excited about. What's really critical to understand when you analyze data is first what does it have the power to tell you and what does it not have the power to tell you. People think a statistical model is the end-all be-all, and it's not. If I wanted to paint a narrative or tell a story that was not true, I could very easily do that, and many people would not know, especially if it feeds an underlying belief, right? Then people will take that story, whether it's true or not, and latch on to it. And so, it's almost like being a detective, right? When you give me a set of data, it's my job to uncover what's

hidden in it. The first thing I'm looking for is where was the data collected. Is it a reputable source? Is it accurate? The other thing that I'm often looking for is understanding that correlation does not imply causation. For example, if you mapped the ability to tie shoes relative to age, right, you would see that you tie them much more effectively when you go from zero to 10 years old, but it's not just because your age is increasing, right. It's because you're becoming nimble with your hands, and now you can reach down and tie your shoes. And so, I have to be careful that once I uncover those relationships, I understand the true sort of root cause of them. Then I want to know, do I have a representative sample? Often, data that we collect is data that has come from society in some way, and so the same biases that we might possess ends up in the data set. We call that confounding variables. And so, as a statistician, if there's a bias in the data, it's my job to find it, uncover it.

pull it out and get rid of it. There was a healthcare algorithm that was developed so that if you were to show up and present with symptoms, it would use this historical data to predict what your treatment should be, and it was pretty accurate. It was very good. It didn't take into account patient race, which made the statisticians believe that it was a very unbiased model. Once the model was in practice, they found that it was biased because built into the data was some underlying racist practices in terms of how Black and Brown patients showed up and what often got assigned to lesser treatments. The model actually perpetuated that bias. And so, if we don't uncover those biases, those confounding variables, the correlations that don't imply causations beforehand, we end up implementing it in a model, putting it into a real-life situation that affects people or outcomes or decisions, and those decisions then have consequences. When you have a sort of a data-literate component of society moving in the direction of AI and

machine learning, and folks in society who could care less about it, who are more maybe just consumers. We end up with people making decisions for us that we don't understand, or we've had very little influence on, because so many of our daily decisions depend on numbers and math and data. It's important that as a society, we are data literate, we're statistically literate, so that we are not only aware of the decisions that are being made on our behalf, but we're a part of them. [Music]

You need data literacy now more than ever – here’s how to master it | Talithia Williams

Big Think is the leading source of expert-driven, actionable, educational content -- with thousands of videos, featuring experts ranging from Bill Clinton to Bill Nye, we help you get smarter, faster. Get actionable lessons from the world’s greatest thinkers & doers. Our experts are either disrupting or leading their respective fields. We aim to help you explore the big ideas and core skills that define knowledge in the 21st century, so you can apply them to the questions and challenges in your own life. Other Frequent contributors include Michio Kaku & Neil DeGrasse Tyson.

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데이터 리터러시는 읽고 쓰는 능력을 넘어 정보와 데이터를 이해하는 능력으로, 오늘날의 세계에서 매우 중요합니다.

소셜 미디어를 통한 실시간 데이터 전송은 종종 타겟 광고로 이어져 윤리적 문제를 제기합니다.

통계 및 데이터 리터러시를 갖춘 개인의 역량을 강화하면 정보에 입각한 의사결정을 내릴 수 있고 우리 주변 세계에 대한 더 깊은 이해를 촉진할 수 있습니다.

수학적 개념이 혈관의 자연스러운 움직임과 성장에 반영되어 일상 생활에서 수학의 아름다움을 보여줍니다.

데이터를 분석하려면 데이터의 한계를 이해하고 잘못된 결론을 도출하지 않도록 주의해야 합니다.

통계학자는 데이터에서 숨겨진 관계를 발견하고, 편향을 인식하며, 관찰된 상관관계의 진정한 근본 원인을 이해해야 합니다.

과거 데이터를 기반으로 환자 치료를 예측하는 의료 알고리즘이 개발되었는데, 처음에는 인종을 고려하지 않아 편견이 없는 것처럼 보였습니다.

그러나 이 모델은 흑인과 갈색 환자에 대한 치료 배정에서 기존의 인종적 편견을 지속시켰습니다.

이러한 모델을 구현할 때 이러한 편견과 혼란을 야기하는 변수를 파악하는 것은 현실에서 해로운 결과를 방지하는 데 매우 중요합니다.

사회에서 데이터 해독 능력의 중요성

사회의 많은 사람들이 머신러닝과 데이터에 무관심하지만, 이러한 요소는 우리의 일상적인 의사결정에 큰 영향을 미칩니다.

데이터 기반 의사 결정에 대한 이해와 영향력이 부족하면 개인과 개인을 위한 선택 사이에 단절이 발생할 수 있습니다.

사회가 데이터와 통계에 능통해져 의사 결정 과정을 인식하고 적극적으로 참여하는 것이 중요합니다.