The Dark Side of Data Science | Data Mobs — The Analytical Trends.

What if, in the future, data is not everything? What if we have an opportunity for a paradigm shift so that everyone has access to an alternative measurement? If this happened, would it change our lives or just be another fad of technology? Would it make us more informed about our decisions and how they affect the world around us; will human rights be protected by this new system? Will wealth still matter in these new systems or will everyone’s worth be equalized like the old days when money was paper instead of quantum information on cells?

Sadly, we are not there yet.

In our modern society, data is everything. We rely on it to make important decisions every day, from what we should have for dinner to how we vote. Now what about when it goes wrong? What about when someone who doesn’t understand the complexities of Data Science gets involved? Take, for example, when Facebook manipulated user data without their consent or knowledge. This was not only unethical, but also illegal — they were fined $5 billion by the FTC as punishment. The lesson here is that while Data Scientists can make a ton of money if everything works out well, they are also responsible for making sure everything works out without bias.

In this blog post, I will discuss how data has been used to predict, manipulate people, and make decisions for you without you knowing, and some dangers associated with using data science in various ways as well as what you can do about it.

Originally, the idea of data science came from a subdivision of metrology, which is the measurement of science aka Data Science Metrology. Both commercially and privately, we no longer use social platforms, devices, or information as for what they were intended to do. Sadly, few know this to be true, but the fact is, Data Science has not advanced that far at all.

In the age of data, we have been collecting more and more information. This has led to a lot of useful discoveries that help us understand how our world works.

The most popular uses of data science is in predictive modelling. Predictive modelling is a technique where past data is used to “direct” future choices.

Before I explain how most of the choices have already been carefully planned, let me first explain the basics of Data Science. Data science is the scientific study of data. In order to make better decisions it uses a variety of techniques from statistics and machine learning. Again, it is a subdivision of metrology, which is the measurement of science. In metrology, we have these two different types of measurements: the measurement of physical objects and the measurement of data. The history of metrology can be traced back to the 18th century when the French Academy of Sciences decided that there should be a standard way to measure things.

Back in the day, we only had local data and it was used mainly for measurement within city limits. This was accurate enough because we could take into account all of the variables that were important to the decision at hand. However, with the advent of the world wide web and globalization, we have created a situation where we can no longer rely on local data. Instead, we need centralized systems that can take into account all of the global variables that are important to our decisions.

The problem is, these centralized systems can not always project multiple outcomes at the same time. This is because these systems do not take into account every variable that affects any given decision, which means they cannot predict all of the possible outcomes accurately, which is ironic and hilarious.

Now the most common use of Data Science happens in business, and government activities. Businesses use data to understand their customers, what they want, and how to best appeal to them. Government uses data to understand how the public is reacting to their policies, as well as what course of action for a particular situation might be. In both cases, this often comes with a cost.

In order to get this valuable data, businesses and governments have started using more and more invasive decision trees. These trees often use your data to determine how you are most likely to act, and offer services or products based on this — and in some cases to control your next decision.

Often, this is done without your knowledge or consent. Furthermore, these trees are usually proprietary, meaning the makers of them would rather keep the algorithms used secret. This means that you can’t really tell if what they are doing is fair or not.

These algorithms are built behind ID3 and CART, which are two of the most common types of decision trees. But as you can imagine, these algorithms don’t always choose right and sometimes they even discriminate against certain races or genders because, for example, it might have been shown that some people have a higher chance to default on loans than others based on their race. While this is a very simple example, try and use your imagination for other ways this is happening.

This is where the second problem comes in: data bias. Data bias happens when we only use a certain type of data to train our trees on and not the whole population. This can lead to some serious problems because it often results in models that perform well on average but fail spectacularly when applied to new data.

An example of this is if we only use the last year of data. We may find that on average there are a certain number of crimes committed in our city every week, but if you were to look at more years, it would be clear that this was not always true and usually crime levels fluctuate from month to month depending on what kind of events happen during that time. Decision trees and other forms of data science should focus our efforts on creating new methods to measure data to ensure we counter attack data bias.

Now, it was only after I read about the dark side of data did I realize that not much has changed. Sure, web-scale systems are important and required for certain aspects of the work, but at its core, data science is still measuring the same things in more advanced ways.

This is where we get back to my original point about data being used to manipulate people. As I said earlier, businesses, and governments are using data to model and direct our choices via social platforms and areas whether offline or online.

This is done by taking the input of all devices, channels, locations, hair color, friendship groups, speeding tickets, credit, date of birth, etc and turning it into a single stream of information that can be used to better control local citizens.

There have been cases where entire populations have been swayed by carefully crafted messages delivered through data science methods, without revealing how certain metrics and keywords can and will lead to human bias.

The Russian government is a great example of this, they used data science to determine which messages would be most effective at influencing people during the 2016 US Presidential Election. This is not new though, businesses have been doing this for years. Coca Cola for example has a well-known history of using people without them even knowing it.

In short, we aren’t making new methods for measurement, but running more controlled scenarios that only help those who create the decision trees, which are often wealthy companies and or governments. This is the dark side of data science, and we need to focus on changing this.

How can data science be used to defend free-market economics, prevent crimes, & More?

Let’s say we have a certain number of crimes committed, and we noticed at the same time we had an increase in unemployment. This would be a good indicator that crime is on the rise because it’s more likely for unemployed people to commit crimes than employed. Although this is a very simple example, solving this problem could be that simple.

An exemplary way to solve this would be to create a new measurement group with different elements such as age, car type, which jobs are the most unemployed in a city, and the causal effects that ignite crime.

One could cross compare that group to a new and alternative measurement that has the same factors but doesn’t include crimes. Then compare them and use that to find a correlation between unemployment causing crime or if it was something else.

This would help us find the causal effect, which is what we need in order to better represent our population making it more diverse. This is how we can use data science with free market economics by making better decisions based on accurate information instead of projecting forced measurements across entire cities.

Speed, projections, and forced decisions with quantum computing.

While the importance of alternative measurement is critical, we also need to focus our education around quantum computing. Quantum information systems are very powerful because at any given time, they can store a lot of data, and in different states. This means that the information can represent a range of things like numbers, letters and symbols, age, color, height, career choice, votes, car color, house projected and predicted at the same time, while also making it translatable, and very fast to access.

Unfortunately, these subtle but powerful programming tactics have enabled the world’s most powerful to find hidden ways to control most of our decisions at alarming rates.

Now Quantum computing is the key to understanding data science metrology, which in turn could help us defend free market economics from information warfare waged by greedy groups and nations.

Again, if we want to stop groups and nations from controlling our data, we need to focus our education around detecting quantum information systems so that we can understand how they are being used to control us. We also need to create better methods for detecting and measuring data so that we can make better decisions.

This blog provides context for a good start but we need to do more research and look at the data to see what is really going on. We can’t rely on old methods of measurement that don’t work anymore. We need to use data science to defend free market economics and find the real solutions to our problems.

So what can you do to help or protect yourself?

• Educate yourself about data science and quantum computing
• Be aware of what is going on around you and the decisions that are being made
• Write about these issues, share them with your friends and family, and raise awareness
• Support organizations that are fighting for freedom and democracy
• Don’t trust data that you do not create yourself
• If you want to buy something online, use a different browser than the one you normally use for your email or social media accounts.
• Use a VPN when you are using public Wi-Fi networks. You can snag one from dashlane
• Use a password manager to protect your passwords and other personal information
• Educate yourself and your friends and family about data science, quantum computing, and information warfare. These
• Delete and recreate profiles every few years.
• Rotate devices, and use external drives.