SOLUTIONING IMPLICATIONS OF HEGEMONY

Why, And the Big Picture

I have spoken before about the role solutioning can play in good training, as well as how an instructional designer can help in even content-based value creation.

But all this is possible only when we consider the personal and political biases we work with, because those affect and shape our designs. We know, ‘sexism (or racism) bad, inclusivity good’. (Or if we don’t, it’s not likely to be from a lack of awareness as much a deliberately chosen stance.) And spotting that kind of -ism is mostly a no-brainer.

There are some biases, though, that are harder to spot than others because they almost characterize the thinking of the field itself. Design is conceived in the real world; it is practiced by people living in the real world. These minds are also educated in design and conditioned daily by the real world. That’s why I tackled hegemony, using ageism as an entry point. Plus, it also intersects nicely with our superiority and cultural biases about people with limited access to technology – the product of internalizing value judgments from the same hegemonic culture.  

And as a final reason, in its Global Report on Ageism (2021), the WHO mentions:

“Ageism also tends to be more accepted and challenged less often than other “-isms”, and it has been shown to be more pervasive than sexism and racism across 28 countries in Europe.”

So. Take a deep breath. You ready? Here we go…

Through the Looking Glass: InfoSec / Cyber Security Training

As you know, this is the second part of a 2-part post (click HERE to read the first part). In this one, I’m going to get specific using information security / cybersecurity trainings as an example.

I want to explore four ways that generational stereotyping limits solutioning capability and even value creation:

1. The stereotypes and biases that cloud our design judgments
2. The potential failure to complement an SME’s perspective
3. The important questions we may fail to raise
4. The missed opportunity to contribute and create value for larger organizational goals

Stereotypes and Biases Clouding Design Judgments

Using generational categorization evokes some quick assumptions about the generations. Especially the following aspects are frequently portrayed in cartoonish oversimplification:

• The assumed level of prior knowledge and exposure to the subject matter
• Whether or not training is provided (including how much and what depth of training), competencies taught, the basic domain ‘savvy’ and learner ability assumed
• Performance and real-life work conditions
• (Unbelievably and as though we’ll never learn from history) Learning styles and preferences!

In information security, as quite a few studies confirm, age is not the sole or defining factor for predicting risky behavior. Unsolicited disclosure online is often because of the desire for convenience and a desire to maintain relationships (both of which are not dictated by age). We make the automatic and biased assumption that as age increases, so does risk; but actually, the aspects hardest to control and predict for cybersecurity are sociotechnical.

Also, when it comes to the hullabaloo over ‘new age’ teaching because of younger age learners being included in the audience, the claims made about multitasking and learning preferences of digital natives vs. digital immigrants are simply mythical. There is no foundation for these claims, no efficiency studies proving that one way yields better outcomes than another for a particular audience.

Further, these sloppy stereotypes ignore the issues of difference in access to technology, and the class privileges that influence the digital divide. (We saw during the Covid-19 lockdowns exactly how relevant those factors are!). They also fail to recognize that there may be cognitive differences among young people of different ages, and variations within age groups. A group’s learning characteristics cannot be lumped together for the reason of the individuals being born in the same year.

So, we may draw several wrong conclusions about the interventions and teaching needed, assuming a level of ignorance or ability that reality actually contradicts.

Complementing and Supporting Subject Matter Experts (SMEs)

The information security industry, like so much of the tech industry, is predominantly made up of American, white, middle-aged and male personnel.

See, there was a reason for my making ‘Dude’ our comic strip’s SME! I’ve already raised the likelihood of insufficient briefing and Dude’s own challenges when suddenly thrown into the deep end of being an SME for a training program. Now, into those already deep waters, throw in added requirements of Dude’s sensitivity to inclusivity, hegemonic and/or privileged views and attitudes… I think we can safely say that it ain’t gonna happen!

But, I had also made the case for why and how we, as instructional designers, can step up and do more to support and complement what an SME brings to the project. Working by sloppy generational stereotypes means we will fail to recognize the issues the SME doesn’t address. We will not be in a position to raise important questions because our own conditioning and prejudices will probably blind us to possibilities.

It’s not about just slotting the SME into a demographic pigeonhole. We need to be able to recognize the inherent skews in the domain that the SME may not notice or think to mention. For example, most studies on digital practices do not ensure that their samples include older people or happen to include inaccurate open-ended categories that lump together people at different life stages.

Also, Dude may overlook the difference in compliance behaviors among genders within a generation because he believes a stereotype about ‘that whole generation’. We should still be alert enough to explore that because studies have found a statistically significant gender-wise difference in terms of computer skills, prior experience, cues-to-action, security self-efficacy and self-reported cybersecurity behavior.

Important Questions We May Fail to Raise

We just saw that gender may be a more pronounced factor than age for predicting cybersecurity behavior, so if we were to ask for organizational metrics, that may be a better parameter for targeted interventions. But beyond that, it also behooves us to recognize subject-bias in the design of the very systems and security measures themselves – which hegemony can blind us to.

For instance, CAPTCHAs are commonly and widely used – and yet there are basic usability issues. CAPTCHAs are more difficult for people with learning disabilities – and for those dealing with the physical decline often associated with ageing. (Not to mention, the CAPTCHA texts, audios, and images also all tend to be a reflection of the Western world alone.) Great solutioning would include asking those realistic questions; for e.g., “alright, you’re teaching the audience to log into your application by entering credentials and answering the CAPTCHA. What’s the system’s provision if they can’t handle the latter?”

There is the aspect, too, of biometrics. Fingerprint quality may degrade with ageing (or due to manual labor, perhaps some medical treatments). If there is a fingerprint scanner for control of physical access to a restricted space, we need to be asking what the workaround is in the system if a person can’t provide such an authentication.

These questions, through the discussions and actions they prompt, create value for the organization that goes far beyond training. The design mindset of human-centered problem- solving is one that we can draw on for the benefit of several other domains and purposes, not just designing curriculum or media elements to go on a screen.

If you think these are value-adding aspects to raise in a discussion, why, there’s a heap more…

Missing Opportunities for Larger Organizational Goals

There is the simple reality that having caricatures of each other doesn’t help us relate better to one another. In fact, in an organization, it’s something of a disaster if employees of various ages aren’t able to get along properly! And if training were to mirror and feed into such a disharmonious, disastrous culture, it would be a real tragedy and disservice.

We should stop encouraging people to caricature themselves or their learning habits and abilities on the basis of these utterly problematic generational categorizations. – It’s not enough for us to just make courses on unconscious bias! We need to clean up our own thinking.

Also, speaking of larger organizational culture, there is such an opportunity for training to contribute towards safer spaces. (Don’t worry, I’m keeping to my example of cybersecurity – this will not become a pep-talk!) Consider the struggle to achieve meaningful inclusion, and consider the increasing role of AI. The two are linked, and good solutioning will be sensitive to this.

Forrester predicts that by 2025, the spend on AI software will double. We keep seeing organizations trying to bring in more AI into their operations, often starting with that popular form – the chatbot. What does a chatbot have to do with inclusion and culture? When Microsoft released Tay, it was a complete fiasco and Tay was taken down a day later because it had very quickly learnt hateful messages.  When we consider even a simple ‘code of conduct’ training, our thinking has to evolve to eventually also encompass interactions with AI. Can you spew bigotry to a chatbot? There are interesting questions to be asked, processes and systems to be implemented and translated to build a rounded-out curriculum. And that’s just one example to get us started on further explorations!

Where Do We Go from Here?

We’ve already seen that generational profiling causes several problems, that there are intersections and overlaps with race, class, gender, etc. We need to remind ourselves as solutioning consultants that we’re not immune to the constant barrage of conditioning in the world. How we view even seemingly unrelated disconnected aspects of learner characteristics, like literacy, competency or efficacy are very much influenced by the inherent political skew of our own field.

As a final example, would we normally concede that an illiterate person in a third world country, with very limited access to tech could perpetrate a large-scale phishing scam? (And don’t forget – it’s one of the biggest threat forms!) And that they would do so with such success that it would become a cottage industry for several people to participate in?! And yet it happens. Again and again.

Norms aren’t neutral: who gets to define them and in what context, matters. It’s up to us to really look at the world around us and notice the instances that expose our de facto assumptions and biases, and how these influence our design choices.

Related:

• https://learnnovators.com/blog/confirmation-bias-and-bad-design-reasoning/ 
• https://learnnovators.com/blog/solutioning-a-tale-of-three-companies/ 
• https://learnnovators.com/blog/solutioning-and-content-based-value-creation/ 
• https://learnnovators.com/blog/hegemony-generational-profiling-and-ageism/

References:

• https://m.facebook.com/CTYJohnsHopkins/photos/names-and-dates-of-generations-1433-present/10150947257424982/
• https://www.theatlantic.com/family/archive/2020/02/generation-after-gen-z-named-alpha/606862/ 
• https://www.zippia.com/instructional-designer-jobs/demographics/
• https://www.who.int/teams/social-determinants-of-health/demographic-change-and-healthy-ageing/combatting-ageism/global-report-on-ageism 
• https://www.zippia.com/information-security-analyst-jobs/demographics/ 
• https://www.forrester.com/report/global-ai-software-forecast-2022/RES178146
• https://www.wired.com/story/effective-altruism-artificial-intelligence-sam-bankman-fried/ 
• https://cointelegraph.com/news/this-ai-chatbot-is-either-an-exploiter-s-dream-or-their-nightmare  
• https://blogs.microsoft.com/blog/2016/03/25/learning-tays-introduction/  
• https://www.indiatoday.in/india/story/the-jamtara-files-cyber-frauds-on-the-prowl-from-your-smartphones-to-your-wallet-with-audacity-2284516-2022-10-12 
• https://theprint.in/the-fineprint/mewat-is-indias-latest-jamtara-and-sextortion-is-the-new-kill/1232746/ 
• Blaut, J. M. (1993). The colonizer’s model of the world: Geographical diffusionism and Eurocentric history (Vol. 1). guilford Press.
• Srinivasan, V. (2012). Multi generations in the workforce: Building collaboration. IIMB Management Review, 24(1), 48-66.
• Lyons, S., Urick, M., Kuron, L., & Schweitzer, L. (2015). Generational differences in the workplace: There is complexity beyond the stereotypes. Industrial and Organizational Psychology, 8(3), 346-356.
• Costanza, D. P., & Finkelstein, L. M. (2015). Generationally based differences in the workplace: Is there a there there?. Industrial and Organizational Psychology, 8(3), 308-323.
• Prensky, M. (2001). Digital natives, digital immigrants. From On the Horizon. MCB University Press, 9(5), 1-6.
• Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British journal of educational technology, 39(5), 775-786.
• Debb, S. M., Schaffer, D. R., & Colson, D. G. (2020). A reverse digital divide: comparing information security behaviors of generation Y and generation Z adults. International Journal of Cybersecurity Intelligence & Cybercrime, 3(1), 42-55.

Written by Mridula R., Principal Learning Consultant @ Learnnovators