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

Summary

Dr. Tiia Williams emphasizes the importance of statistical and data literacy in the 21st century, highlighting how data is ubiquitous in our lives, from targeted ads to health apps. She illustrates the beauty of math in nature, like the Fibonacci sequence, and stresses the need for critical analysis of data to avoid misleading narratives. Williams discusses the dangers of biased algorithms and the importance of representative samples to ensure fairness. She advocates for a data-literate society to participate in decisions made by AI and machine learning, influencing outcomes that affect our daily lives.

Takeaways

• 📚 21st-century literacy involves more than just reading and writing; it includes understanding information and data.
• 📱 Many digital platforms collect data in real-time, like discussions about green rugs leading to targeted advertisements, raising privacy concerns.
• 🧠 The rise of AI makes it crucial for society to develop statistical and data literacy to prevent misuse of data for corruption.
• 🔍 Data literacy empowers individuals to make informed, rational choices rather than relying on others' beliefs or interpretations.
• 👩‍🏫 Dr. Tiia Williams, a math professor and science communicator, emphasizes that everyone can be a statistician or data scientist in their daily lives.
• 🌿 Mathematical concepts like the Fibonacci sequence and fractals are not only theoretical but also manifest in the natural world and human body.
• 🕵️‍♀️ When analyzing data, it's critical to understand its limitations and not to mistake correlation for causation.
• 🔍 Detecting biases and confounding variables in data is essential to avoid perpetuating inaccuracies or prejudices in statistical models.
• 🏥 An example of a healthcare algorithm that was biased due to historical data, highlighting the importance of scrutinizing data sources and their implications.
• 🌐 In a data-driven society, being data literate is vital for individuals to understand and influence decisions made on their behalf.

Q & A

What does 21st-century literacy encompass according to Dr. Tiia Williams?

21st-century literacy is not just about reading and writing; it involves understanding information and data.

How does Dr. Williams explain the feeling of being listened to by technology?

Dr. Williams illustrates this by mentioning how discussing green rugs might lead to seeing ads for them on platforms like Facebook or Instagram, indicating real-time data transfer.

What potential issues does Dr. Williams raise with the rise of AI and data?

She points out the ease with which AI can be used for corruption and emphasizes the need for statistical and data literacy to avoid being misled.

Why is statistical literacy important according to the transcript?

Statistical literacy empowers individuals to make informed, rational choices instead of relying on others' beliefs or misunderstandings.

How does Dr. Williams relate everyday activities to data science?

She gives examples such as using Uber or Lyft, where one interacts with data like distance, time, and price, or tracking steps and heart rate for exercise.

What role does mathematics play in understanding the world around us, as per Dr. Williams?

Mathematics is integral to understanding patterns in nature, such as the Fibonacci sequence and fractals, which appear in various natural and human-made forms.

What does Dr. Williams suggest is critical to understand when analyzing data?

She emphasizes understanding what data can and cannot reveal, and being cautious about drawing conclusions from statistical models.

How does Dr. Williams describe the process of uncovering hidden information in data?

She likens it to detective work, focusing on the source of data, its accuracy, and the presence of confounding variables or biases.

What is the importance of representative sampling in data analysis, as mentioned by Dr. Williams?

A representative sample is crucial to avoid biases that may be present in the data set, which can lead to incorrect conclusions and decisions.

Can you provide an example of how biases in data can lead to unfair outcomes, as discussed in the transcript?

Dr. Williams cites an example of a healthcare algorithm that perpetuated racial bias in treatment assignments because it was based on historical data that included racist practices.

Why is it important for society to be data literate in the age of AI and machine learning, according to Dr. Williams?

Being data literate ensures that society is not only aware of decisions made on their behalf but can also participate in and influence those decisions, which are increasingly data-driven.

Outlines

00:00

📊 Understanding Data in the 21st Century

Dr. Tiia Williams, a math professor and science communicator, discusses the importance of data literacy in the modern world. She explains that literacy now encompasses understanding information and data, not just traditional reading and writing. The script touches on how personal data is collected and used in real-time for targeted advertising, highlighting the role of AI in this process. Dr. Williams emphasizes the need for statistical and data literacy to empower individuals to make informed decisions independently, rather than relying on others' beliefs. She also points out that everyone, in some capacity, is a statistician, data scientist, or mathematician in their daily lives, as they interact with data through various services like Uber or health apps. The paragraph concludes with Dr. Williams' passion for revealing the mathematical beauty in nature and the critical aspects of data analysis, such as understanding the limitations of data and the importance of distinguishing correlation from causation.

05:00

🔍 The Role of Data Literacy in Society

In this paragraph, the focus is on the societal implications of data literacy, particularly in the context of AI and machine learning. The script warns against the dangers of implementing models without first identifying confounding variables and understanding that correlation does not imply causation. It discusses the consequences of biased algorithms in healthcare, where an algorithm's predictions were influenced by historical racist practices, leading to biased treatment recommendations. The paragraph stresses the importance of a data-literate society to ensure that decisions made on behalf of individuals are transparent and influenced by an understanding of the underlying data. It concludes with a call for society to be statistically and data literate to actively participate in and influence the decisions that affect them.

Keywords

💡Literacy

In the context of the video, literacy is not just about reading and writing but extends to understanding information and data. This modern literacy is crucial for navigating the digital world where data is constantly collected and used to influence decisions and behaviors. The video emphasizes the importance of being literate in data to make informed choices and to understand the algorithms and advertisements we encounter daily.

💡Data

Data refers to the information collected from various sources, which can be analyzed to reveal patterns, trends, and insights. In the video, Dr. Tii Williams discusses how data is transferred in real-time and used to create personalized advertisements, illustrating the pervasive nature of data in our lives. Understanding data is key to making sense of the world around us and the decisions that are made on our behalf.

💡AI (Artificial Intelligence)

AI is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video mentions the rise of AI and its potential for both positive uses and misuse, such as corruption through biased data analysis. AI's role in society is significant as it influences decision-making processes, and thus, a data-literate society is necessary to ensure that AI serves us well.

💡Statistical Literacy

Statistical literacy is the ability to understand, evaluate, and analyze data. The video underscores the importance of statistical literacy in a society increasingly reliant on data. It empowers individuals to make informed, rational choices rather than relying on others' beliefs or interpretations of data, which they might not fully understand.

💡Data Literacy

Data literacy is the skill of reading, working with, analyzing, and arguing with data. The video argues for the necessity of data literacy to ensure that individuals can critically assess the data they encounter and make their own informed decisions. It is presented as a tool for personal empowerment in a data-driven world.

💡Correlation vs. Causation

The video explains the difference between correlation, which is a statistical relationship between two variables, and causation, which implies that one variable causes the other. Dr. Williams uses the example of tying shoes and age to illustrate that correlation does not imply causation, a critical concept in data analysis to avoid misleading conclusions.

💡Confounding Variables

Confounding variables are factors that can distort the relationship between the variables under study. The video discusses how these variables can introduce bias into data sets, leading to incorrect conclusions. Identifying and addressing confounding variables is essential for accurate data analysis and decision-making.

💡Bias

Bias refers to a systematic distortion or prejudice in data or analysis, which can lead to inaccurate or unfair results. The video gives an example of a healthcare algorithm that was biased due to underlying racist practices in the historical data it was trained on. Recognizing and mitigating bias is crucial for ethical and effective data use.

💡Representative Sample

A representative sample is a subset of a population that accurately reflects the characteristics of the whole population. The video emphasizes the importance of having a representative sample when conducting data analysis to ensure that the conclusions drawn are valid and applicable to the broader context.

💡Algorithm

An algorithm is a set of rules or steps used to solve a problem or perform a computation. In the video, algorithms are discussed in the context of AI and data analysis, where they are used to process and interpret data. The video highlights the potential for algorithms to perpetuate biases if not carefully designed and monitored.

💡Mathematics in Nature

The video mentions how mathematics appears in nature, using the Fibonacci sequence and fractals as examples. This concept illustrates the beauty and ubiquity of mathematical patterns in the world around us, which can inspire awe and curiosity about the mathematical principles that govern our universe.

Highlights

21st-century literacy involves understanding information and data.

Smartphones often use data collected from users in real-time for targeted advertising.

The rise of AI can be used for corruption if data literacy isn't widespread.

Statistical and data literacy empowers individuals to make informed decisions.

Dr. Tiia Williams is a math professor and science communicator emphasizing the importance of everyday math.

Mathematics and statistics are integral to daily life, from ride-sharing to health tracking.

Math concepts like the Fibonacci sequence and fractals are found in nature and the human body.

Data analysis requires understanding the limitations and potential of the data.

Statistical models are not infallible and can be manipulated to tell untrue narratives.

Data analysis involves detective work to uncover hidden truths and biases.

Correlation does not imply causation, a critical concept in data interpretation.

Representative sampling is crucial to avoid biases in data analysis.

Confounding variables can introduce bias into data sets.

An example of a healthcare algorithm that perpetuated racial bias due to unexamined data.

The importance of a data literate society in the era of AI and machine learning.

The necessity for society to be statistically and data literate to influence decisions made with data.

Transcripts

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 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 and 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 and and data

scientists and mathematicians

if I want to get a ride to the airport

and I do Uber or lift 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 uh 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 study and

understand have direct occurrences in

the world around us 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 Beall

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 uh 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 is my job to find it uncover it

pull it out and get rid of it there was

an a healthcare algorithm that was

developed so that if you were to show up

and present with symptoms it would use

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 patient showed up and would often

get 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 but 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

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 we're

a part of them

[Music]