655-Final

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)

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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.

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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

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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)?

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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

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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.

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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.

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http://www.infoplease.com/ipa/A0931238.html

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