Essay on Effects of Technology

Part1: Technology influence in Society

Technology has significant impact and role in society today. It has both desirable and undesirable effects depending on the situation. For example, overuse may deteriorate users’ health and even impede one’s social and relationship prowess. In education, technology has been demonstrated to enhance learning outcomes, especially by availing instructional methods and encouraging group works. Even if demerits exist, technology is beneficial because it has revolutionized social life in society and even influences communication. However, biases and prejudice exist in search engines and reading materials. Humans program computer functionalities, and therefore, biases and discrimination are expected (Tatman). Machine language, for instance, is essential artificial intelligence (AI) tool that studies statistical models and algorithms to enable computers to use so as to do specific functions with relying on explicit instructions by using inferences and patterns. Predictive analytics is one example of this strategy that predicts the future based on big data. In the analysis of algorithm bias and algorithm oppression, technology has significant influences on how people think, talk, and learn in society.

Thinking is one significant impact of technology. Even if someone is not around, a person can stare into a laptop or phone by searching for news or other interesting stories. There is a psychological part of effect inseparable from technology, a perspective reinforced by programmers. It can impede one’s focus. Algorithm biases cam promote hatred and racism. For example, Microsoft created Tay, a chatbot and designed to emulate millennials (Crockett). Surprisingly, Twitter trolls taught Tay genocidal ideas and racial slurs. It means younger generations are explicitly more sexist and racist than their parents. Such thinking mode is detrimental if promoted in society. People used to read books linearly. Now, people scan for specific terms and links to go direct to gain information. With biased and oppressed algorithms, gaining a broad perspective is minimized.

The communication between people is enhanced, thanks to technology. Many methods are currently in use to promote interaction between individuals, such as video conferencing, social media, emails, and chat messengers, among others. In business, predictive analytics are widely used to analyze big data to forecast on the future, which then is used in decision making. This way, it is useful in the business communication processes. Videos can also be used to spread racism and hatred. For example, a video of washing dispenser machine portraying only Whiteman’s hand and neglecting the blacks was tweeted to show racialism and its impacts in the society (Hale). On the whole, technology and communication are essential, and so, the right messages should be used when communicating.

In learning, technology fosters collaboration and interaction. As a result, engagement is improved between learners and teachers as well as course materials. Instructors can give assignments remotely and evaluate them. Students as well now can retrieve information easily using their phones and laptops. However, detriments to these advancements exist. Adverse social impacts are typically linked to the use of social media and google as learners can access information that encourages racism, sexism, profiling, and wrong information (Noble). So, designing these algorithms should occur such that to eliminate and discourage biases and oppressions.

In conclusion, algorithm bias and algorithm oppression determine the contents the people access online, and as such, technology affects the way people learn, talk, and think in society. In communication, different methods are used. Biases and prejudice present in online sources influence users adversely. The way people think is also affected by the information they get online. Lastly, technology enhances learning outcomes. However, biases available online daunts knowledge the students can gain.

Part 2: Question 1

The computer algorithm has biases because they are programmed by humans who are prone to errors. Algorithm systems can then contain prejudices because, at the time of coding, there are pre-existing cultural influences, social aspects, and organizational expectations. Sometimes unanticipated audiences and contexts emerge which were not considered during the initial design. For examples, a soap dispenser machine ad it was deemed to be racist because it depicted only a white person’s hand and neglected that of blacks (Hale). Another instance is when Microsoft created a Chabot called Tay (Crocket). The millennials on Twitter used this robot to spur hatred and racism, which was contrary to the intended purpose. In both instances, programmers neglected significant social features, cultural aspects, and users’ expectations.

Part 2: Question 2

I would choose option 1, that is “how human beings design search engine algorithms in terms of the social good or social harm done,” to address algorithmic oppression. Online search engines are mostly driven by profit models when curating information. As a result, misinformation and biases are common. For example, the 2015 killings at a church in Charleston which involved nine worshippers was a result of online propagated malicious hate crime (Noble). The soap dispenser washing machine is another example of an online spread racialism (Hale). I would change “how profit models (and what kinds of profit models) drive search engine algorithms,” to change algorithmic bias.

Part 2: Question 3a

I would consider three areas for college admissions, such as intellectual curiosity, test scores, and counsellor recommendation. The tests scores would be given 40%. It will be measured by considering the test essay and difficulty scores in high courses. It would be good because it measures one’s academic ability. I would give intellectual curiosity 40%. In this strategy, different extracurricular activities are used. So, a criterion is put in place to consider each person based on various features, including gender, and disability, among others. Lastly, I would vouch for counsellor recommendation and give it 20%. This information helps gather data about the applicant, including their social skills. This port is good because a counsellor is most likely to provide accurate data about a student based on his past experiences in high school. Moreover, this information will be helpful to understand the social ability characteristics of the student joining the college.

Part 2: Question 3b

As much as I balanced between academic prowess and extracurricular ability of the applicants, some flaws exist in the counsellor recommendation part. Well, those anticipated errors are the reasons why I suggested a lower percentage value. A school counsellor may not have full data about a student, and therefore, gaining exhaustive information on applicants can be challenging. Students spend the most time with their parents. However, guardians may provide false information because they would want their children to join better schools of their choice. Also, the high school counsellors may offer inaccurate data if compromised by the learner. Equitability is one feature I intend to achieve by modeling the criterion to involve the disabled.

Part 2: Question 4a

For this part, I choose option 1, that is, “an academic administrator who wants to ensure that students actively participate in class discussion.” This person considers social skills much more than any other option. A student who asks questions and responds to queries in class is considered interactive, and that is the exact type preferred by this administrator. So, he or she would want the social skills to be awarded a high percentage. As is in my criterion, counsellor recommendation entails that expectation; however, some flaws exist, especially when the counsellor does not give accurate data about the applicant. That way, a bias is expected in the algorithm. So, this person would require a robust approach to be instituted so as to evaluate a student’s social skills. This way, his or her perspectives can be entirely addressed.

Part 2: Question 4b

College admissions algorithms demonstrate biases and express values in many forms. Most colleges use the Scholastic Assessment Test (SAT), which is a standardized test score that is used by all USA colleges in the USA for admitting students into colleges. Well, it is a value embraced by most institutions, yet it is biased to students who are gifted in extracurricular activities. I believe that it should not be the main application determinant. My belief is based on the point that people have born with intrinsic abilities, and most of them require a favourable culture to unveil their capabilities. My college currently holds this method as the primary admission strategy, a method I believe is not fair. Concerning one reading materials involving a review of Cathy O’Neil’s book, the algorithm is believed to perpetuate inequality (Lamb). Therefore, algorithmic systems cannot be entirely trusted because they are subject to biases.

Works Cited

Crockett, Emily. “How Twitter Taught a Robot to Hate.” VOX, 24 March 2016, www.vox.com/2016/3/24/11299034/twitter-microsoft-tay-robot-hate-racist-sexist. Accessed 4 March 2020.

Hale, Tom. “This Viral Video of a Racist Soap Dispenser Reveals a Much, Much Bigger Problem.” IFLScience, 18 Aug. 2018, www.iflscience.com/technology/this-racist-soap-dispenser-reveals-why-diversity-in-tech-is-muchneeded/. Accessed 4 March 2020.

Lamb, Evelyn. “Review: Weapons of Math Destruction.” Scientific American, 31 Aug. 2016, https://blogs.scientificamerican.com/roots-of-unity/review-weapons-of-math-destruction/. Accessed 4 March 2020.

Noble, Safiya. “Google and the Misinformed Public.” Sofiyaunoble.com, 2 June 2017, https://safiyaunoble.com/google-misinformed-public/. Accessed 4 March 2020.

Tatman, Rachael. “Google’s Speech Recognition Has a Gender Bias.” Making Noise and Hearing Things, 12 July 2016, https://makingnoiseandhearingthings.com/2016/07/12/googles-speech-recognition-has-a-gender-bias/. Accessed 4 March 2020.