`` Weapons Of Math Destruction `` By Cathy O ' Neil Essay

Computers dehumanize society by treating everyone as a number. Reflecting a rather positive shift in attitude, only 37 % of the adult respondents agreed with this statement and 50% disagreed. The younger respondents in the current Creative Computing survey were more pessimistic, 40 % agreed that computers dehumanize and only 31 %

Examples include, for instance, large sets of Twitter messages or data extracted from Wikipedia. In their definition, boyd and Crawford (2012) further include the capacity to ‘to search, aggregate, and cross-reference large data sets’, further noting that big data, besides from its technical aspects, entails a cultural ideology, circulating around the notion that the accumulation and analysis of large datasets offers a new form of a more truthful, accurate and objective knowledge of the world (2012: 663).

A common theme throughout this class for me was seeing mathematical/ statistical ideas and topics in a new light. Many of these aha moments came to me when reading Weapons of Math Destruction. Most often it involved seeing past all the math and statistics and seeing the people being affected by statistical models and methods. Whether it was the teachers that were affected by the value-added models or people being affected by insurance companies, these issues impact everyone in society. Mathematical and statistical literacy are greatly important to civic engagement. A great example from our readings was the article “Data for Good”. In the article D. J. Patil, the first chief data scientist of the United States, discussed his guidelines for his

Cloud computing has really shifted the whole landscape of how companies do their databases now, and with this has brought the concept of big data.

Michael A. Morell, founding & managing partner from Riviera Partners, a technology search and recruitment firm, is positive about one thing: Big Data is the future of job recruiting.

They used facts and arguments from various sources, such as studies and authors. As they are introducing the topic, they use the ideas of Lev Manovich to justify their argument that the name “Big Data” can be misleading. Manovich observed that Big Data has been used to refer to data sets large enough to require supercomputers, yet large amounts of data can now be analyzed on much simpler computers. Boyd and Crawford contend that the value of the industry does not simply come from the large data sets, but the “patterns that can be derived by making connections between pieces of data…” By relating Manovich’s idea, their argument made more sense. As computers become more advanced, bigger data sets look much simpler. But the connections Big Data makes are still valuable, no matter how advanced computers

Many contemporary scholars debate the ethics and morals behind big data algorithms and machine learning. In Weapons of Math Destruction, Cathy O’Neil, a respected data scientist, describes the notion of Weapons of Math Destruction (WMD), algorithms or models that try to qualitatively determine characteristics like individual creditworthiness, mental health, proper sentencing, and ect. While these models seem to reduce prejudice and bias from decision making, she argues that they, in fact, increase and perpetuate inequality by disguising as technology. On the other hand, in The Master Algorithm, Pedro Domingos, a professor at the University of Washington, argues that there exists one master algorithm that can derive knowledge about the past, present, and future from just information. He cites different fields of study like Neuroscience, Computer Science, and Applied Math to present proof about the algorithm’s existence.

Big data is a term to describe the 2.5 quintillion bytes of data that we create each day. There are many ways that the big data is collected and used; some types may be good, and some might not. As we read the book “Too Big to Ignore” by Phil Simon, you will see this is a controversial debate inside of the book. The book talks about whether big data collection is good, such as data mining or bulk data collection. In the story, it tended to lean towards the side of agreeing with the use of data collection.

Technology companies are using big data to analyze millions of voice samples to deliver more reliable and accurate voice interfaces. Banks are using big data techniques to im- prove fraud detection. Health care providers are leveraging more detailed data to im- prove patient treatment. Big data is being used by manufacturers to improve warranty management and equipment monitoring, as well as to optimize the logistics of getting their products to market. Retailers are harnessing a wide range of customer interactions, both online and offline, in order to provide more tailored recommendations and optimal pricing.96

There is sudden increase in the way data is currently being collected. People use smart phones and smart tablets and connected to internet throughout the day. Most of the shopping has gone online. As a result, data is getting collected in different formats and from different applications. The rapid expansion of Big Data is further fueled by exponential increase in usage of internet and people ability to interact on social media and different networking applications including YouTube, Gmail, Facebook to name a few. But just how easy is the task to leverage the vast amount of data, which is called Big Data, our computer systems are collecting on a daily basis? What significant challenges are currently faced by the organizations

Big data and its definition has changed over the years. In a 2011 research project by MGI and Mckinsey’s Business’ defined big data as

Like Luna et al., Kuo, Sahama, Kushniruk, Borycki, & Grunwell (2014) note the inconsistency of the definition of big data in research. They, however, say this inconsistency is because big data is an evolving concept. They describe big data as data that is so large, so complex, and growing so fast that traditional data management methods cannot hold or organize it. Kuo et al.’s and Luna et al.’s definitions varied among syntax, but both stressed the complex nature of big data. This variation seemed to be a trend among several of the researchers. While each researcher’s definitions somewhat differed, each seemed to agree that big data is data that is so complicated that traditional methods cannot handle it. The researchers continued to agree that the complex health data available today falls under the definition of big data.

Big Data is defined as extremely large data sets that can be used to analyze and compute to reveal patterns and trends, and associations. The US first started to collect data through the First Census and crop yields to track our harvest and how to improve on it. Today we do the same thing but on a much larger scale. In 2011 we hit 1.8 zettabytes of data generated in that year, that is 1,000,000,000,000 gigabytes of data we have to analyze and compute, and it was estimated to quadruple in 2013. The reason we continue to generate more and more data every year is through the technology we have today and the information that they record and transmit to us. Now we have finally come up with a name for this, Internet of Things. The internet of things is every device that can connect to the internet and transmit data, these thing can range from your: car, phone, fridge, thermostat, smart watch, computer, etc. Big Data today will be measured in 3 fields volume, velocity, variety. Volume how big the data actually is, how many zettabytes how much space and energy does it cost to have this volume. Velocity how fast we can analyze this data and put it to good use as well as how fast can transfer such volume of data from one location to another. Lastly variety, since the volume of data is so enormous we have and equally wide variety ranging from cat videos on the internet to private leaks to research and scientific papers written

In a fast paced, business ordinated technological world the overall welfare of a company is tied to the success or failure to make the tough decisions. On one instance a company’s CEO might be able to make the choices based on experience, advice, or simple gut instinct. However, this is not the only skill one needs. There is a great deal of information to be found in being able to see investments in data and analytics. These decisions are based off of big data. Big data is a catch-phrase, used to describe a massive volume of both structured and unstructured data that is too large to process using traditional database and software techniques. The volume of data is in most cases is too big, moves too fast or it exceed the processing capacity the company has. Despite these potential drawbacks, big data contains the potential to help companies by improving operations and making faster, more intelligent decisions. This can be broken into three key parts, knowledge, data, and information.

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past