Category: Student Projects

Introduction

The purpose of this 750-1000-Word Expanded Definition is to explore the definition of the term “machine learning” with regards to the scientific community and society. I will be analyzing the term in a study on fairness, education, and machine translation. My working definition will be provided afterwards.

Definitions

In the article “A Snapshot of the Frontiers of Fairness in Machine Learning” by Alexandra Chouldechova and Aaron Roth, the definition of machine learning is straightforward. “Machine learning is no longer just the engine behind ad placements and spam filters; it is now used to filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things.” (Chouldechova, Roth, 2020, p.82). To Chouldechova and Roth, machine learning is a process that has evolved to automate more complex data.

On the other hand, the New York Times article “The Machines Are Learning, and So Are the Students” by Craig S. Smith defines the term differently. “Machine-learning-powered systems not only track students’ progress, spot weaknesses and deliver content according to their needs, but will soon incorporate humanlike interfaces that students will be able to converse with as they would a teacher.”(Smith, 2019). According to this, it can be seen that Smith defines machine learning as a means to an end, and this end being helping students learn better.

As for the article “On the features of translationese” by Vered Volansky, Noam Ordan, and Shuly Wintner, machine learning is a simple one. “In supervised machine-learning, a classifier is trained on labeled examples the classification of which is known a priori. The current task is a binary one, namely there are only two classes: O and T.”(Volansky, Ordan, Wintner, 2015, p. 103). To Volansky et al., machine learning is an assisting tool to help create more humanlike translation and must be supervised in order to function correctly.

Context

The context of all three articles is quite simple. The quotes I have used above are the most relevant to the topic of choice, as well as definition since machine learning wasn’t clearly defined in each article. So I will bounce off of that.

In the first article, the context used is a scholarly article searching into how machine learning can be made “fair”, or better put, “objective”. “With a few exceptions, the vast majority of work to date on fairness in machine learning has focused on the task of batch classification.”(Chouldechova, Roth, 2020, p.84). For better or for worse, the quote tells us that fairness has typically through batch classification. Batch classification, in this context, is sorting data by inputting user-defined characteristics and then judged through user-defined fairness. Machine learning is just the process of automating this process and even “learning” how to do it with other types of data. But the fallacy of fairness with such a method is laughable since humans are the ones defining fairness. Since humans have inherent bias, fairness is difficult to judge.

In the second article, it is a news article speaking about technology in education, specifically, machine learning and how beneficial it is for teachers. “The system also gathers data over time that allows teachers to see where a class is having trouble or compare one class’s performance with another.”(Smith, 2019). For teachers, this system is a way to track a student’s progress or performance without having to personally analyze the sheet data.

For the last article, the context is the machine translation. What should come to mind when hearing the term machine translation should be famous web browser-based translation services such as Google Translate, Niutrans, Sougou, and DeepL. That’s about it.

Working Definition

Personally, I am majoring in Computer Systems: IT Operations track. However, I have a hobby in translation with the assistance of machine translation. So, my working definition for machine learning is “the application of gathering vast amounts of data, categorizing the data, sorting them out, and analyzing data to find out the psyche of people.” For example, if given a group of 100, the data collected must be categorized by their gender or whatever category is set. Then, the answers gathered will be sorted by correct/incorrect based on the generally accepted answer. Finally, the data is analyzed so that there are percentages of what questions were answered correctly most of the time based on the sorted category. With that, the machine has a sample of what to expect if someone of x category answers the same data collection set. Done on a macro-scale, the machine will be able to predict what a population’s answer could be.

References

Smith, C. S. (2019, Dec. 18). The Machines Are Learning, and So Are the Students. New York Times. https://www.nytimes.com/2019/12/18/education/artificial-intelligence-tutors-teachers.html

Volansky V., Ordan N., Wintner S. (2015). On the features of translationese. Digital Scholarship in the Humanities, 30(1), 98–118. https://doi.org/10.1093/llc/fqt031

Chouldechova, A., Roth, A. (2020). A Snapshot of the Frontiers of Fairness in Machine Learning: A group of industry, academic, and government experts convene in Philadelphia to explore the roots of algorithmic bias. Communications of the ACM, 63(5), 82–89. https://doi.org/10.1145/3376898

Sources:

I. Margaret Rouse
What Is Cybersecurity? Everything You Need To Know
https://searchsecurity.techtarget.com/definition/cybersecurity?amp=1
April 03, 2020

II. Dakota Murphey
A History of Information Security – IFSEC Global: Security and Fire News and Resources
https://www.ifsecglobal.com/cyber-security/a-history-of-information-security/
June, 27, 2019
III. defined, explained, and explored
IV. What is Cyber Security?
https://www.forcepoint.com/cyber-edu/cybersecurity

TO: Prof. Jason Ellis

FROM: Stephan Dominique

DATE: 10/29/20

SUBJECT: Expanded Definition of Biometrics

Introduction

The purpose of this document is to expand the definition of the term ‘Biometrics’, which is very popular in today’s advancing technological world. If you use a smartphone today that uses any fingerprint or face scanning technology to unlock your phone, biometrics is being used. I will cover this topic by first defining the term and the history behind it, followed by the context of the word as well as the working definition. 

Definitions

According to Meng-Hsuan Fu, Biometrics is defined as “Using human physical characteristics including finger vein, iris, voice, and facial features for recognition.” (Fu, M., 2020, p.2). This means, for example, if a crime were to be committed and the police found the fingerprints of the criminal to later identify the person, biometrics is being used in this instance. To understand biometrics, one must break everything down by first looking at the term “anthropometry”, which is the study of the specific measurements of the human body. Biometrics stems from this as without Anthropometry, biometrics simply does not exist. Anthropometry involves analyzing the unique properties of humans that make each person different from the next. Going even further, the founder of this study is Alphonse Bertillon, who also was the first person to identify a criminal through their fingerprints as well as being the inventor of what is known now as the mugshot, another form of biometrics. “Biometrics are physical or behavioral human characteristics that can be used to digitally identify a person to grant access to systems, devices or data.” (Korolov, M., 2020). Essentially, biometrics means that no one else can have access to what you have access to. If there is a password required for something, your body is the key to unlock it and only you can unlock it. These two definitions are similar for the fact that they both discuss that biometrics involves using the human body to identify a particular person. The difference in these two definitions, however, are that Fu speaks on biometrics in a general sense. The second author’s definition goes more into biometric security, applying the first definition into security, which is a form of security which is hard to crack.

Context

The first contextual appearance is where Fu states that “Biometrics is becoming more widely used in access-control systems for homes, offices, buildings, government facilities, and libraries. For these systems, the fingerprint is one of the most commonly used biometrics. Users place their finger on a read device, usually a touch panel. This method ensures a unique identity, is easy to use and widely accepted, boasts a high scan speed, and is difficult to falsify. However, its effectiveness is influenced by the age of the user and the presence of moisture, wounds, dust, or particles on the finger, in addition to the concern for hygiene because of the use of touch devices”  (Fu, M., 2020, p.5). In this quote, Fu describes the use of biometrics and where it is popularly used with the benefits of the term as the technology is being utilized more and more in typical workplaces because of its ease and efficiency but also the cons of it as well. The second contextual appearance is where Korolov mentions “62 percent of companies are already using biometric authentication, and another 24 percent plan to deploy it within the next two years.” (Korolov, M., 2020). She is essentially saying that because biometrics are highly effective, companies are quickly becoming on board as their information is tightly guarded.

Working Definition

Biometrics is extremely popular and as such, will require workers in the I.T. field to aid in installing, maintaining, and fixing such technologies. Biometrics is relevant to my career because I plan to start off on the technical side which includes being a field service technician. This job consists of going through workplaces and maintaining various things such as a fingerprint scanner. The knowledge of handling such equipment will probably become more mandatory as time goes on and more companies switch to biometric security. 

References

Meng-Hsuan Fu. (2020). Integrated Technologies of Blockchain and Biometrics Based on Wireless Sensor Network for Library Management. Information Technology & Libraries, 39(3), 1–13. https://doi-org.citytech.ezproxy.cuny.edu/10.6017/ital.v39i3.11883

Korolov, Maria. “What Is Biometrics? 10 Physical and Behavioral Identifiers.” CSO Online, CSO, 12 Feb. 2019, www.csoonline.com/article/3339565/what-is-biometrics-and-why-collecting-biometric-data-is-risky.html.