655-Final
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This document contains 6 questions for a final exam on the topic of gender, race, ethnicity and the use of information technology. It provides instructions for doctoral and master's students to write essays in response to the questions. Doctoral students should write a 4 page double spaced essay and master's students should write a 3 page double spaced essay using APA citation format. The deadline for submission is October 13th as a PDF file. The questions ask students to discuss differences in how genders, races and ethnicities use information technology in non-work contexts. They are asked to consider areas like online gaming, social media, shopping and discuss if and why differences exist. For doctoral students, the final question asks what these
655-Final
- 1. HCI - 655 Final Exam Question 1 Anthony Townsend Final Exam Question Just one question, you write an essay in response. Doctoral students have four (4) pages of text, whatever it takes for cites, Masters students have three pages, whatever it takes for cites. Use an APA citation format (that puts your cites at the end) and by pages I mean double-spaced, 12 point, one-inch margin type pages. Go read whatever you need to answer this articulately, and turn it in by October 13 (send it to me with the subject heading “655 Final”); I prefer PDF files. Question We have talked about gender and IT in the workplace and a little about race/ethnicity and IT in the workplace…but we did not talk about either in non-work areas of IT. Do genders, races, ethnicities differ in the way that they use IT? If so, how (and more importantly) why do they differ? Finally, at a higher level for the doctorate crowd, what do these differences reveal about information technologies? (Only the doctorate types have to answer the last part) Desarae A. Veit
- 2. HCI - 655 Final Exam Question 2 Anthony Townsend Preface We’ve already talked at length on this subject. So, I’m sure without writing you already know my personal stance on the commonly known “isms” (e.g. sexism, racism, ageism etc.). At one point or another, many people probably experience varying levels of being left out, bullying, or miscommunication and data on these subjects can be skewed based on who and when you ask the right or wrong questions. So, because I’ve already read some of your papers and discussed with you the good, the bad, and the ugly of this subject- I’d like to take the approach of reviewing prejudices through the lens of marketing, online guidelines or rules, and perceptions. In this paper, I’ll also examine research based on other areas we’ve discussed that are non-employee to employee or indirect communications where gender, race, age, and ethnicity may be veiled like shopping, chat, forums, gaming, and online content. At first, I thought about taking the viewpoint of HR/Laws - which are hopefully put in place to protect employees (optimistic viewpoint) but potentially created simply to protect the company from liability (pessimistic viewpoint), but it seemed too close to our hacktivism paper and after reading a few related research articles, I took a different approach. Desarae A. Veit
- 3. HCI - 655 Final Exam Question 3 Anthony Townsend We have talked about gender and IT in the workplace and a little about race/ethnicity and IT in the workplace…but we did not talk about either in non-work areas of IT. Non-work areas of IT can be left open to broad interpretation (e.g. online gaming, social media, online dating, chat, multi-user research like MIT’s Media Lab, artificial intelligence, and shopping). According to Infoplease (2016), on men use the internet more than women to get news, plan travel, shop on auctions, check on sports, create internet content, download music, and interact with the stock market. Women however, are more likely to look for health, spiritual, religious, or support group information. Granted, to my knowledge, Infoplease is not an accredited or peer reviewed website but the data seems skewed based on perceived gender roles. The site does not offer how data is collected, what method of analytic tools are used to validate types of users, or if a verification method is in place to confirm that couples are not using a spouses’ login to access certain types of web pages. Regardless, it would seem these gender biases are implied in their published statistics. This pessimistic viewpoint of the data could hinder or help different IT stakeholders. Imagine if a large website, like Amazon based their website content, promotions, and copywriting on unverified data (with assumptions that a computer collected the data so it is inherently correct, because a biased ‘expert’ didn’t vet the data). The social implications of this Desarae A. Veit
- 4. HCI - 655 Final Exam Question 4 Anthony Townsend type of bias would impact the end-user with the type of marketing and content the website’s algorithms gave them (Awad et al., 2008). Do genders, races, ethnicities differ in the way that they use IT? If so, how (and more importantly) why do they differ? Though, I’m sure some companies are afflicted by expert bias (Fisk, 2004), that is not how Amazon content is created, most of the product content is crowdsourced or written by brands, individual sellers, promotions, and buyers (even some who are incentivized). In comments and shopping on Amazon gender and ethnicity biases still exist in negative and positive ways. The navigation of the website allows users to narrow down shopping focus and more feminine products sometimes have different interfaces or content voices. Branding is sometimes softer, lighter, darker, or more colorful depending on age and gender. Korean clothing brands often are modeled by young, only skinny, short Korean models. Common female American and European brands seem to focus on a variety of tall slender models, except in plus sizes. This is something that is common offline though, and marketing is well known for its fair share of criticism, especially in magazines. Another forum is question and answer boards or gaming. Men and women are known to create profiles of the opposite sex, ethnicity, height, or even pretend to be someone or something they are not (Reyns, 2010; Christin, 2013; Drouin, 2014; Turner, 2014). This sometimes leads to trolls and bully (Sturgis, 2014). Other times it leads to a sense of community and self discovery in a perceived “safe space” so how is it that regardless of gender or ethnic identification do people identify one another, even through lies like catfishing (Turner, 2014)? Desarae A. Veit
- 5. HCI - 655 Final Exam Question 5 Anthony Townsend Overall, technology does seem to be used differently by different genders, ages, and ethnic groups but no as a broad reaching generalization as the Infoplease statistics seem to imply. Individual factors are always in play, geographic, experiences, and even the knowledge of social and racial prejudices may have an impact on the way society or individuals interact with television, news, shopping, and of course the internet. Knowledge of cliches seems to have the effect of sometimes cementing a norm and sometimes forming larger and larger populations of outliers (Cox, 1980; Gupta, et al. 2013). (Gupta, et al. 2013) suggest in the 2013 ASONAM tutorials that the world is socially connected and ongoing data-mining efforts, combined with an easy access to communication and familiar marketing are only bringing once distance and drastically different societies even closer and more alike. The ASONAM paper also discusses outlier detection. Optional Question: Finally, at a higher level for the doctorate crowd, what do these differences reveal about information technologies? The ASONAM outlier research and others like it (social engineering, data mining, AI predictions) could be used to identify fake accounts, automate the removal of spam or disinterested users, create better ways to engage people with various networks, streamline scarier areas of the web like identity theft and image theft/misuse which may not sound scary until you consider how many unsuspecting parents post their children’s photos online or friends/lovers use open platforms like SnapChat to communicate intimately. Differences in online usage from varying demographics may reveal lifestyle priorities, economic significances, prioritization of knowledge vs. beauty/money/self/others. Overall, I Desarae A. Veit
- 6. HCI - 655 Final Exam Question 6 Anthony Townsend think this research reveals how little is known about diversity, communication, and how much can be gained from collaboration when barriers are removed. It also seems to imply that humans like to have a persona (real or otherwise), and that data mining may have scary implications as well as opportunities to improve worldviews and problem solving. Outliers may also help identify unique skills, fake identities, and force everyone to continue improving- forever. Citations Awad, N. F., & Ragowsky, A. (2008). Establishing trust in electronic commerce through online word of mouth: An examination across genders. Journal of Management Information Systems, 24(4), 101-121. Christin, N. (2013, May). Traveling the Silk Road: A measurement analysis of a large anonymous online marketplace. In Proceedings of the 22nd international conference on World Wide Web (pp. 213-224). ACM. Cox, N. J., & Anderson, E. W. (1980). In defence of exploratory data analysis. Drouin, M., Miller, D., Wehle, S. M., & Hernandez, E. (2016). Why do people lie online?“Because everyone lies on the internet”. Computers in Human Behavior, 64, 134-142. Fiske, S. T. (2004). Intent and ordinary bias: Unintended thought and social motivation create casual prejudice. Social Justice Research, 17(2), 117-127. Gupta, M., Srivastava, J., Kang, U., Akoglu, L., Chau, P., & Reuser, A. H. (2013, August). ASONAM 2013 tutorials. In Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on (pp. xliii-xlvii). IEEE. Desarae A. Veit
- 7. HCI - 655 Final Exam Question 7 Anthony Townsend Infoplease (2016). Different People Use the Internet in Different Ways. Published by: FEN Learning. Accessed December 2016: http://www.infoplease.com/ipa/A0931238.html Reyns, B. W. (2010). A situational crime prevention approach to cyberstalking victimization: Preventive tactics for Internet users and online place managers. Crime Prevention & Community Safety, 12(2), 99-118. Sturgis, I. (2014). High-tech bullies. Diverse Issues in Higher Education, 19. Turner, A. (2014). Selfie-Representation: New Literacies and Youth Identity Production in Online Spaces. Hampshire. Desarae A. Veit