Disruptive Technologies in the Digital Economy, Week 5 – Bias? In my AI?
So colour me surprised. We have an entire week on AI, and it's mostly about biases in AI.
Of course, it was still too damn hot to think, but at least I got to be pleasantly surprised by this.
Weekly Learning Objectives
- Differentiate between user / reality digitalisation interferences based on different media of exchanges.
- Categorise the different biases of digital technologies.
- Reflect on how individuals change the sensing of the world around them due to digitalisation (eg digital validation processes, digital self perceptions).
- Examine the underlying mechanism of the role reversal in the context between human and computer decision making.
- Explore the broader role of artificial intelligence in business but also the role of artificial intelligence in potentially enhancing the subtlety of some biases.
I guess?
A more civilised time
Again, the fact that technology changes at a rate that academia can't keep up with causes problems. The latest reading is from June 2020, and most of it was from 2017-18.
Which means it's all mostly going "Well, AI will do some great work in crunching big data, but that's about it." It's all before the agentic boom, it's all before the generative boom, it's just...out of date.
As an example, "Artificial intelligence for the real world" was published by Thomas Davenport and Rajeev Ronanki in Harvard Business Review in 2018. It focuses on what types of AI are out there, breaking them into three categories, and what they mean for businesses.
And...well...
Our research suggests that cognitive engagement apps are not currently threatening customer service or sales rep jobs.
Ooof.
Most cognitive tasks currently being performed augment human activity, perform a narrow task within a much broader job, or do work that wasn’t done by humans in the first place, such as big-data analytics.
Most managers with whom we discuss the issue of job loss are committed to an augmentation strategy—that is, integrating human and machine work, rather than replacing humans entirely.
Oh, a more innocent time. Bless their little cotton socks.
Another article, "The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms" by Spyros Makridakis, published in 2017, was even more...well...yeah, I'm just going to quote this bit about AI "pragmatists".
At present the vast majority of views about the future implications of AI are negative, concerned with its potential dystopian consequences (Elon Musk, the CEO of Tesla, says it is like “summoning the demon” and calls the consequences worse than what nuclear weapons can do). There are fewer optimists and only a couple of pragmatists like Sam Altman and Michio Kaku (Peckham, 2016) who believe that AI technologies can be controlled through “OpenAI” and effective regulation.
ELON MUSK AND SAM ALTMAN BEING AI PRAGMATISTS.
YES, THAT IS WHAT IT SAYS.
It also talks a lot about Ray Kurzweil and The Singularity and, man, I've been hearing about that bullshit since my very first job in 1999, when I was working at an agency that did online PR for books, and he was shilling The Age of Spiritual Machines: When Computers Exceed Human Intelligence and even then when we were all optimistic about the future and thought anything could happen, it was ridiculous.
I sure would like it if I got to read more recent papers. But, at the same time, not that much has changed.
For one of the activities we had to do this week, we watched Joy Buolamwini's TED Talk on How I'm fighting bias in algorithms, which was released 10 years ago.
And we're still dealing with the exact same things she talks about in the video. Facial recognition is still shit, it's just being used more and more. The Home Office has vans with live facial recognition software going around to spot "criminals", even though live facial recognition software gets so much wrong.
So while I'd love to read more recent papers, I also know that NOTHING HAS CHANGED AND WE'RE ALL DEALING WITH BULLSHIT.
It's amazing what a framework can do.
The main reading was "Artifical intelligence: Building blocks and an innovation typology", which is focused on how managers can evaluate AI tech coming in and how it might help (or massively hinder) their company, or, as it says in the abstract (Paschen, Pitt, & Kietzmann, 2020):
This framework lets managers evaluate their markets, the opportunities within them, and the threats arising from them, providing valuable background and structure to important strategic decisions.
Yes. This is what I need. This is why I'm doing this course. To get the frameworks so I can go "Based on my analysis, this is a terrible idea and we should not do it."
Rather than right now, where I go "this is a terrible idea and we should not do it" and no one listens to me. I mean, I get to say "I told you so" later on, but as good as that makes me feel, I'd kinda like to...not have to clean up after.
The framework is pretty lovely too. Basically, all of these products can go into a grid where it gets evaluated by two sliding scales:
- Is it going to help people do their work, or is it going to replace them?
- Does it affect the product that is being created, or does it affect the process involved in creation?
What I love is that the first scale is called in the paper "Competence-Enhancing" or "Competence-Destroying". Oh yes yes yes yes, competence destroying yes
So, like, if you're answering support tickets, and you run your response through a tone checker to make sure you're not being too angry, that's competence-enhancing, because you still have all your knowledge and you're still solving the problem yourself, but you're just making it a bit more readable. But if you're using an agent to write everything in your ticket, that's competence-destroying, because why am I paying you when I can buy something off of the shelf and just throw all the current knowledgebase into it.
(I would fire you, because I want real people to actually answer tickets, but you get the idea, right?)
Week 5 — Results
- I need to read more recent articles on things.
- Everything is biased, computers even more so.
- Competence-destroying is such a great phrase.
- Good frameworks are a thing of beauty.
Next week, we start to move away from individual technologies and start digging in crime and cybersecurity, which is what the rest of the course is about, because that's the professor's second favourite topic.
(His favourite topic is money, as proven by last week's blockchain lecture. But cybersecurity pays the bills.)
Today's Sticker
This is from RollPlayAromas, because this kitty is me every. damn. day.
They also have an utterly delightful Stardew Valley-themed fragance set. I have the Winter one, which is this gorgeous blend of pine and rose. So good.
(They also previously did a run of Eevee evolution-themed perfumes. I got an Espeon scent that is positively perfect.)
References
Davenport, T. & Ronanki, R. (2018) Artificial intelligence for the real world. Harvard Business Review. 96(1). January-February 2018. 108-116.
Makridakis, S. (2017) The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures 90. 46-60. https://doi.org/10.1016/j.futures.2017.03.006
Holl-Allen, G. (2025) Facial recognition vans to hunt criminals across Britain; Home Office to launch consultation on what safeguards are needed for police using the technology. Telegraph Online. 13 August. https://link-gale-com.hull.idm.oclc.org/apps/doc/A851509495/STND?u=unihull&sid=summon&xid=ae2958ed [Accessed 24 Jun 2026]
Paschen, U., Pitt, C., & Kietzmann, J. (2020) Artificial intelligence: Building blocks and an innovation typology. Business Horizons 63(2). 147-155. https://doi.org/10.1016/j.bushor.2019.10.004