Monthly Archives: November 2021

The potential relationship between media, education, text, and technology shifts my thoughts towards those displaced people that Canada provides refuge. Of those refugees, the single Sub-Saharan African mothers with their children live at the margins of Canada’s marginalized. These women function in an almost state of “primary orality” (Ong, 2002) arising from their meagre education. A lifetime of conflict, displacement, years of the encampment, and femaleness has exacted a heavy toll. “Secondary orality” (Ong, 2002) develops with their coming to Canada mainly through television and their cell phone that is permanently close at hand. Hopefully, most of this group will end up in Language Instruction for Newcomers to Canada (LINC) offered by the Government of Canada.

The federal program provides English instruction for those who need it. Classes are streamed for English level, but not for tertiary-educated nor refugee-camp-educated. This mixture potentially poses instructional challenges—as many still employ teacher-centred mass instruction. Furthermore, as a group, the refugee-camp-educated students’ progression pales compared to those who are well-versed in formal education and equipped with modern literacies.

I can read, write, and do arithmetic. And for most people, reading and writing is pretty straightforward. Arithmetic is pretty straightforward… As so even in today’s world, what we mean by literacy is highly conditioned on [your] life. _{-Doug Fisher}

Unfortunately, a quieted griping could be heard amongst instructors ill-equipped to handle the discrepancy, let alone the despondency, forgetfulness, or exhaustion found within the classroom space. The tipping point for some was the incessant phone calls with animated unintelligible voices interrupting the sanctity of the occasion.

Literacy pedagogy … has been a carefully restricted to formalized, monolingual, monocultural, and rule-governed forms of language. –The New London Group

As a result, the Learners with Interrupted Formal Education (LIFE) classroom was created to work with students with low literacy, numeracy, technology, and learning in a formal environment. However, it was capped at ten students. Furthermore, admittance to the class was only through recommendations from regular stream instructors. Thus, it was not surprising that LIFE’s wait-list became long.

Our heroine is Hawa, a reserved woman from the Democratic Republic of the Congo (DRC) who joined the LIFE classroom. Her previous instructor felt that her slow laboured responses were indicative of cognitive issues. Moreover, her neediness in the computer lab and constant phone interruptions were even more reasons for leaving the regular-stream. Little effort was given to knowing about her lived experience; it would cause discomfort, so canonicity and textuality (Scholes, 1992) were hidden behind.

Textuality refers to a collaboration between language and the human consciousness that always distances us from reality without ever replacing the reality. – _{Robert Scholes}

who is this student in front of us

Like most of her classmates from other unsettled Sub-Saharan African countries, she had found safety from civil strife, ethnic cleansing, and genocides in Kakuma. Nevertheless, as a woman-headed household, she suffered from severe poverty. Moreover, as a refugee, she could not supplement the meagre UNHCR rations sustaining her family.

Kakuma was established for the Lost Boys of Sudan in 1992. By 2021, it has since surpassed its capacity of 60,000 by almost threefold necessitating sub-division. This influx has strained the camp’s infrastructure and resources, so clean water, food, and medicine are scarce (UNHCR, 2021). In addition, donor countries’ donations dwindle even though displaced people keep arriving.

She was four months pregnant when she left her village afoot with the seven children carrying parts of their life. They entered Kakuma emptied-handed after the exhausting trek across borders – it had been chosen because of rumours that settlement abroad was accessible through Kenya. Years passed before Canada accepted them for resettlement; the eldest three had reached the majority by then and had become ineligible. Only the eldest daughter had been able to find a third country, the Netherlands. The two sons remaining behind were still part of her monthly expenditures. In Canada, she heads a household with two pre-teen children and two youngsters abandoned to her care by their parents.

Canadians pat themselves on the back for their country’s generosity. And why not, after all, previous students, women-heads of households, had requested a counsellor at the school to help negotiate the paperwork needed for the three levels of government. Immigration Canada Refugee Citizenship (IRCC) came up with the funds. That counsellor had also secured Provincial Training Allowances (PTA) to help stabilize their lives while studying to improve their skill sets to become employable in Canada. Furthermore, the federal government had fulfilled the request to equip the LIFE classroom with technology such as Smart Screens and tablets to increase their digital skills. As a result, learning was swift, and some of the students even started to dream of a future.

A novel examines not reality, but existence is not what has occurred, existence is the realm of human possibilities, everything that [wo]man can become, everything [s]he’s capable of. _{– Milan Kundera}

That was then and this is now

We Support Refugees! was a campaign for a Saskatoon mayor candidate a few years back. The premier echoed this slogan. Nevertheless, that was then, and this is now. At the same time as that campaign, the Saskatchewan provincial government stopped the PTA for refugees. As a result, some women found night employment as cleaners, and while they tried to continue studying during the day, they were often found asleep at their desks. Working, running a household, and studying was more than their bodies could handle. At the same time, others went on social assistance to keep afloat; it allowed them to continue their full-time studies. But there is a difference between PTA and Social Assistance governmental support; PTA requires the student to show up, like a job, to receive funding.

And then COVID-19 hit. A few of these over-aged female learners managed to finish the term: April, May, and June, but when the new period started in August, all the women had vanished. The reasons are numerous—for example, the cost of the internet is out of their range; they were relying on the free internet provided by the school. To study, they now needed to find access to free wifi: the local mall? Some had difficulty connecting to Zoom and the LMS with their phones.

In contrast, others had trouble doing the homework designed for desktops, laptops, and tablets but which did not function so efficiently on the phone. The tablets that they were using in class were squirred away for safekeeping. The dystopia for women like Hawa is already here. As educators and society, we must ask ourselves how their fate impacts their futures and what about their children’s future?

The mission of education … its fundamental purpose is to ensure that all students benefit from learning in ways that allow them to participate fully in public, community, and economic life. _{– New London Group}

Today, we don’t really have a person. We only have a person with a phone in hand. That’s the extended self… but by 2025 … our students will be extended selves. And of course, that has huge implicaitons for how we teach and even what we teach. _{– Jaco Hamman}

dawn of a new age

As for our heroine, Hawa was hit by the loss of PTA and found employment as a cleaner. However, she was one of the lucky ones as she had moved through the system before COVID-19. When she needed to be transferred from the face-to-face format, her computer skills were strong enough for a blended-class design. At first, she was overwhelmed with the change but providing her with targetted help increased her confidence to work solo. She still was using only the phone, but the programs used were more phone friendly which decreased the troubleshooting required, thus reducing the frustration she experienced. As her English skills improved, she moved from the LINC program onto the Basic Education program and completed a primary caregiver and first aid certification. She is now employed in that field.

Pedagogy is a teaching and learning relationship that creates the potential for building learning conditions leading to full and equitable social participation. _{– The New London Group.}

With no regulatory oversight, the exponential advancements in technology are fueled by the profit of private tech owners. Technology holds promise but also worries about its dark and disturbing potential. As Elon Musk stated, we need to have a future where we get up and want to live.

References

Cazden, C., Cope, B., Fairclough, N., & Gee, Jim. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review, 66(1), 1–60.

Downey Jr., R. (2021, December 18). How far is Too Far | The age of A.I. https://youtu.be/UwsrzCVZAb8

Dunne, A., & Raby, F. (2013). Chapter 5: A methodological playground: Fictional worlds and thought experiments. In Speculative Everything: Design, Fiction, and Social Dreaming. Cambridge: The MIT Press.

Futurology. (2020, May 22). The world in 2050. https://youtu.be/RNVh_HMX2IY

Insane Cuiosity. (2020, February 27). The world in 2050: Future technology. https://youtu.be/Oa9aWdcCC4o

Mars, R. (n.d.). The Age of the Algorithm (No. 274). https://soundcloud.com/roman-mars/274-the-age-of-the-algorithm

Musk, E. (2017, May 3). The future we’re building—And boring. https://youtu.be/zIwLWfaAg-8

Ong, W. J. (2002). Chapter one: The Orality of language. In Orality and literacy: The technologizing of the word (pp. 1–11). Routledge.

Price, L. (2019). Books won’t die. https://www.theparisreview.org/blog/2019/09/17/books-wont-die/

Saskatoon Mayor Charlie Clark offers support to people. (2017, January 29). https://globalnews.ca/news/3212524/saskatoon-mayor-offers-support-to-assist-people-affected-by-u-s-refugee-ban/

Scholes, Robert. (1992). Canonicity and textuality. In Introduction to scholarship in Modern Languages (2nd ed., pp. 139–158). Modern Languages Association of America.

Tech Vision. (2020, June 26). The world in 2050: A peek into the future. https://youtu.be/nho3r9SjL7Y

UNHCR. (2021, April 1). Inside the world’s largest refugee camps. USA for UNHCR.

Canada and United States are founded on the same constitutional structure of the Rule of Law where laws apply to everyone equally – justice is supposed to be blind. The laws should not unfairly target or be advantageous to one person or group over another. Therefore, when I undertook the simulation, “Detain/Release,” my full intent was to make the high stake judgements in an impartial, consistent way. I was equally aware of the need to prevent overcrowding for those remanded to custody while awaiting the trial. I was also mindful that the American judicial system balances accountability to popular and political pressures with its laws. Moreover, I intended to defer to the Canadian viewpoint where Canadian and American values differed, such as drug use and guns.

Recalled

Unfortunately, I did not meet the needs of the American system. I was recalled after judging 21 defendants out of 24 due to the increase in public fear. Five of the high-risk-of-flight defendants that I had released did not return to court. Most importantly, one defendant was charged with fraud, with low risk for fleeing and risk of violence, and whom the prosecutor recommended releasing and thus I released her. Later she had a warrant out for manslaughter. This defendant caused a significant spike in the public’s confidence in my judgment.

I was consistent when reflecting on my decisions: I considered the prosecutors’ recommendations, but downplayed the defendants’ impact statement. I found myself thinking when dismissing their impact statements that perhaps the defendants should have considered their reasons before committing the crime. I had already judged them guilty, contrary to the presumptions of innocence and the guarantee of a fair process by the independent and impartial trier of facts as laid out in the Canadian Charter of Rights and Freedoms.

When I looked at the three algorithmic derived scores, I automatically released those who scored low for all three categories and found that I was not too concerned by the high-flight risk or drug use. As consistently, I detained those with a high risk for violence and took more time over the consideration about the unlawful use of weapons.

I had several questions that I pondered about: What was meant by low, medium, and high-risk? What did the system mean by “a new violent incident” when the defendant was a no-show. Would I run out of space in the detainment centre? How could you possibly predict a fraudster would kill someone?

Pretrial Risk Assessment

Many American judicial structures introduced algorithmic pretrial risk assessment tools to reform the wealth-based bail system. It was thought that algorithms would eliminate variability, bias, and subjectivism that pervaded the courts. In addition, it was believed that the predictive algorithm would provide consistent neutral scores based on evidence to determine the defendant’s future behaviour (LCO, 2020). For example, the algorithmic pretrial risk assessment tool scores a defendant from low to high on their risk of flight, the likelihood of committing another crime, and their propensity to violence. As a result, public safety was factored into consideration.

“What qualifies as low or high depends on the thresholds set by tool designers and merely denotes the risk a group presents relative to other risk bins”

(Buskey & Woods)

The mathematical equations are trained with vast inputs of curated data from the past. Their strength is their dexterity in crunching, sorting, and evaluating the data to recognize past patterns and then propagating them to predict future behaviour (Mars, 2017; McRaney, 2018; O’Neil, 2016; 2017). Their output feedback into the system to inform future operations. To optimize the algorithm, the designer imposes a definition of success. Without mindfulness of what success is being sought, the algorithm will strongly reinforce the status quo and perpetuate any patterns of unfairness or discrimination. Mars (2017) points out that algorithms impact our society profoundly and imperceptibly.

These are not clear mathematical expressions of the way of the world… we are translating human languange, human perspective into machine language and machine perspective. As we do that we need to be careful on a meta level about how we train the system. _{–Allistor Croll}

O’Neil (2016) argues that a good assessment tool needs to be checked and verified. She adds that the initial action is to build a fair model and then use the learning algorithm for auditing for equity, thereby increasing trust in the equations. The problem is fair for whom – this is a subjective decision. O’Neil advocates that problems in algorithms can be corrected by measurement and transparency, but when measuring the general implications, it is vital to measure for whom the algorithm injures and look at what injuries are being caused (Mars, 2017; O’Neil, 2016).

Vallor (2018) points out that artificial intelligence amplifies and extends human cognition; it augments our performance in ways that would be impossible solo. However, it does not replace human decisions for complex, unpredictable real-world problems but for simple repetitive and routine tasks that can be automated.

Perhaps there lies the problem – humans use algorithms for complex real-world situations around today’s social issues that split society. The algorithms must make a judgment call with vast amounts of bias data from the past, yet culture and values have evolved. With little or no human oversight, the system creates a feedback loop the reinforces the status quo without any deprejudicing.

“When are machines are wrong, … they are not wrong statistically. The data that we feed them is statistically more likely, But if you are trying to change that, but if you are trying to progress away from that, if you are trying to move away from the past, then this sort of bias and prejudice is a real problem It means our machines are morally wrong; they are sociall wrong, and that kind of wrongness is difficult to program out of the algorithms that we have created.” _{–Allistor Croll}

How does this apply to Canada?

Algorithmic tools are shrouded in secrecy from how they code to what is coded. (Mars, 2017; McRaney, 2018; ) However, a fundamental to transparency and accountability of these tools would be an informed citizenry with access to open, understandable software that their government employs in the judicial, services, and policing structures (LCO, 2020; Mars, 2017, McRaney, 2018, O’Neil, 2016, 2017).

Nevertheless, there are no central lists or academic studies on how widespread predictive algorithms are in Canada, even though predictive algorithmic are already employed in numerous Canadian cities by the police service (Robertson, 2020).

“These systems are often disclosed as a result of litigation, freedom of information requests, press reports or review of government procurement websites” _{– Law Commission of Ontario}

The increased use of algorithms in the criminal justice system for “pre-trial, sentencing, and post-sentencing phases” raises many legal issues in Canada. According to Christian (2020), there are three main issues: algorithmic racism, the legality of using AI risk assessments when sentencing, and proprietary vs individual’s Charter of Rights.

Despite Canadians pride in being a country of peace, order, and good government with a multicultural population, Canada has a long history of strained racial relations with several minority communities. For example, many police jurisdictions have been scrutinized for systematic racism and discrimination throughout the years, especially from women, black, and Indigenous groups. Thus, the Stonechild inquiry provides a poignant benchmark to gauge progress. However, unfortunately, Stonechild died during his “starlight tour’ when the city police dumped him on the city’s outskirts in the throes of winter. A common enough practice directed towards Indigenous men by some prairie city police officers. So it would be expected that if the Canadian society wished to move away from our difficult path towards a future of improved racial relations, algorithms based on the past would be less than helpful. Christian (2020) argues that the Canadian criminal justice system had an obligation to ensure that the information they use does not directly or indirectly stereotype and discriminate.

Algorithmic racism … arises from the use of historial data in training AI risk assessment tools. This has the tendency to perpetuate historical bias which are replicated in the risk assessment by these AI tools.

–_{Gideon Christian}

Moreover, Christian (2020) quotes Justice Nakatsuru of the Ontario Superior Court, who noted that “sentencing is an individual process” and not a construct of a group membership. However, algorithmic predictive tools’ risk scores are based on statistics from analyzing big data, anything but individual. Christian (2020) supports his argument with the Ewert v Canada 2018 case in which an Indigenous defendant challenged his risk assessment because he was trained principally on non-Indigenous specifics. The Canadian Supreme Court ruled that algorithmic tools trained on one predominant cultural group are unrepresentative.

In Canada, an individual has the right to due process, an open court where they face their accuser to mount a defence against the charges; that becomes impossible to defend against the reasoning of the “blackbox” that has morphed several times since its creation or if proprietary rights trump my rights.

In Canada, an individual has the right to due process, an open court where they face their accuser to mount a defence against the charges; that becomes impossible to defend against the reasoning of the “blackbox” that has morphed several times since its creation or if proprietary rights trump my rights.
We have the freedoms to disagree with the government, the majority and work towards change. Even though Canadian society is a work in progress, and there is still far to be inclusive, what exists comes from individuals fighting for their values. However, there is no guarantee for longevity: laws, rights, and freedoms are human constructs. What would a machine construct look like?

References

Buskey, B., & Woods, A. (2018). Making sense of pretrial risk assessments. The Champion, June. https://www.nacdl.org/Article/June2018-MakingSenseofPretrialRiskAsses

Christain, G. (2020). Artificial intelligence, algorithmic racism and the Canadian criminal justice system. SLWA: Canada’s Online Legal Magazine. http://www.slaw.ca/2020/10/26/artificial-intelligence-algorithmic-racism-and-the-canadian-criminal-justice-system/

Law Commission of Ontario. (2020). The rise and fall of AI and algorithms in American criminal justice: Lessons for Canada (pp. 1–55). https://www.lco-cdo.org/wp-content/uploads/2020/10/Criminal-AI-Paper-Final-Oct-28-2020.pdf

Mars, R. (2017, September 5). The Age of the Algorithm (No. 274). https://soundcloud.com/roman-mars/274-the-age-of-the-algorithm

McRaney, D. (2018, November 21). Machine bias (No. 140). https://youarenotsosmart.com/2018/11/21/yanss-140-how-we-uploaded-our-biases-into-our-machines-and-what-we-can-do-about-it/

O’Neil, C. (2016, September 1). How algorithms rule our working lives. The Guardian. https://www.theguardian.com/science/2016/sep/01/how-algorithms-rule-our-working-lives

O’Neil, C. (2016, November 2). Weapons of math destruction. https://youtu.be/TQHs8SA1qpk

Robertson, K., Khoo, C., & Song, Y. (2020). To surveil and predict: A human rights analysis of algorithmic policing in Canada (Transparency and Accountability, pp. 1–192) [Research]. The University of Toronto. https://citizenlab.ca/wp-content/uploads/2020/09/To-Surveil-and-Predict.pdf

Vallor, S. (2018, November 6). Lesson from the AI mirror. https://youtu.be/40UbpSoYN4k

the diss: techie vs Baggar

‘UI user inyerface’ taunts the target audience to a head-to-head competition against Baggar, who guarantees to frustrate those who dare to attempt completing an undemanding task. Moreover, the masculine overtone of the challenge reflects that the tech economy is male-dominated (Brookfield Institute, 2016; OCED, 2018). Thus, there is a high likelihood that this contemptuous, confrontational jeer directed at 20-25-year-old males (Harris, 2017) who are competent in web programming will trigger a response. A challenge motivates one to compete as the individual seeks to overcome and taste success’s glory. After all, competition is a fundamental human motive in a hierarchical society (Anderson & Hildreth, 2016; Yildirim, 2015).

“All gets brought back into building a more and more accurate model … once you have it, you can predict the kinds of things that the person does, predict what kind of emotions tend to trigger.” – Tristan Harris

n00b vs boss

Baggar employs other default stylings for the inline elements of links. For example, most users know and expect that underlining ‘click’ indicates the connection and similarly, colouring the ‘next page‘ suggests that the link has not yet been visited. However, the highlighted ‘click’ and attention-getting ‘next page‘ is meant to mislead and delay.

Even though the all caps are shouting ‘HERE,’ it is virtually invisible. On top of the other misdirections, using block letters makes it difficult to scan and increases the time needed to read and interpret.

A UI programmer’s attention would have been drawn quickly to the uninformative’ click Here,’ since programmers typically avoid its use when designing navigation on a web page. Moreover, navigation links generally are placed at the top, mid-page, or bottom of a page.

TLDR

Once in, Baggar has a red box slip in at the top of the screen to draw attention. Red heightens emotions and creates a feeling that this must be dealt with to advance. Their A/B test found within the red box is puzzling. The question is unusual; the “Yes” choice is evident and apparent like ‘no’; however,’ no’ is paired with ‘Not really,’ suggesting uncertainty. In reality, it does not matter whether one clicks yes or no after many tries later; it became apparent that neither one was required to advance. The box is a red herring meant to confuse and distract. Nevertheless, a competent programmer would be aware of the trick as A/B testing pioneered by Chamath has become standard practice as growth tactics by tech companies (Orlowski, 2020).

“Dark patterns tend to perform very well in A/B and multivariate testing simply because a design that tricks users into doing something is likely to achieve more conversions.” –Harry Brignull

After the red box diversion, the designed loop on the progress indicator solely draws attention to the timer. A timed element acts as an intrinsic motivator in gaming to improve flow, engagement, and enjoyment; however, for others, a timer can negatively impact decision-making competence and performance (Yildirim, 2015). They add variability to the timer: the time it takes to lock the system fluctuates from just over one minute to nearly five minutes. As a result, the emotional state quickly heightens as one races against an unknown cutoff time.

Other distractions and miscues employed on subsequent pages:

GTG

FTW

Verifying humanness

This page appears complete, yet the anchor points cut off the top layer of the required boxes to be checked. It simply requires scrolling up on the page, easy to execute, but it took time to imagine this visual illusion.

concluding thoughts

As Harris (2017) stated, “the best way to get people’s attention is to know how someone’s mind works. Baggar is not an inadept design company but a commercial enterprise that seeks clients’, skilled tech programmers and engineers, and perhaps tech writers’ attention. With this game, the company picked up free promotion through posts like Slashdot, technewstube.com, fossbytes.com and chatter of the players/programmers. However, by digging deeper into Baggar’s website, it is easy to recognize a sophisticated Belgium tech company focused on guiding corporate clients towards digital transformation with creative design. UI User Inyerface was not an interface with bad design nor an interface with no clues, rules, and convention, but a reasonably sophisticated design that engaged people to stay on their screens through allusion, tapping into intrinsic human behaviour, and predicting what people would do. It also demonstrated to potential clients the frustration that customers have with lousy interface design.

“A magician understands … some part of your mind that we’re not aware of. That’s what makes the illusion work. People do not know how their mind is vulnerable… from this perspective you can have a very different understanding of what technology is doing… Persuasive technolgy is just sort of design intentionally applied … to modify someone’s behaviour … to take this action we want them to keep doing.” –Triston Harris

References

Brignull, H. (2016, December 23). How do dark patterns work?

Cameron Anderson and John Angus D. Hildreth. (2016). “Striving for superiority: The human desire for status.” IRLE Working Paper No. 115-16. http://irle.berkeley.edu/workingpapers/115-16.pdf

OCED. (2018). Bridging the Digital Gender Divide: Include, upskill, innovate (p. 151). https://www.oecd.org/digital/bridging-the-digital-gender-divide.pdf

Harris, T. (2017, April). How a handful of tech companies control billions of minds every day. https://www.ted.com/talks/tristan_harris_how_a_handful_of_tech_companies_control_billions_of_minds_every_day?utm_campaign=tedspread&utm_medium=referral&utm_source=tedcomshare

O’Neil, C. (2016, September 1). How algorithms rule our working lives [The Guardian]. https://www.theguardian.com/science/2016/sep/01/how-algorithms-rule-our-working-lives

Orlowski, J. (2020, January 26). The social dilemma.

Rainie, L., & Anderson, J. (2017). Code-Dependent: Pros and cons of the algorithm age (pp. 1–87) [Number, Facts and Trends Shaping Your World]. Pew Research Centre. https://www.pewresearch.org/internet/2017/02/08/code-dependent-pros-and-cons-of-the-algorithm-age/

Vu, V., Lamb, C., & Zafar, A. (2019). Who are Canada’s Tech Workers? (p. 56). Brookfield Institute for Innovation and Entrepreneurship. https://brookfieldinstitute.ca/wp-content/uploads/FINAL-Tech-Workers-ONLINE.pdf

Yildirim, ïrem G. (2015). Time pressure as video game design elements and basic need satisfaction [Master of Science in Modeling and Simulation Department, Middle East Technical University]. https://www.researchgate.net/publication/281637918_Time_Pressure_as_Video_Game_Design_Element_and_Basic_Need_Satisfaction/citation/download