Diving into the Wave of the AI Job-Revolution

The AI revolution is here. What has been talked about for the last two decades is finally being realised. It’s here, with all its hype, and it is here to stay. Every company wants the proverbial slice of pie and wants to ingrain the use of Artificial Intelligence (AI) in its products. There has been a massive amount of funding in labs and companies across the world for developing new AI research. With this, fears have arisen about AI taking over the world as well as our jobs. There are statements like “Millions of jobs will be replaced by AI”. Or will they? To answer this question, we have to go back a few decades.

There is no reason anyone would want a computer in their home.

Key Olsen, founder of Digital Equipment Corporation, 1977.

Somehow, the microcomputer industry has assumed that everyone would love to have a keyboard grafted on as an extension of their fingers. It just is not so.

Erik Sandberg-Diment, New York Times columnist, 1985.

Both of these statements have not aged well at all. This is not to throw shade at them, but to show how predicting the impact of any new technology is extremely hard.

Every new technology, especially in the last two and a half decades, has brought about a revolution in the job market. Yes, many jobs were permanently lost. However, each new technological revolution also brought about completely new jobs which were hitherto unknown. For all the jobs lost because of the internet, like media distribution stores, encyclopedia salesman, librarians, phone-book companies, there have emerged new jobs like social media managers, web developers and bloggers. It gave so many people a platform to sell their products and services, as well the ability to reach out to a huge number of people faster. In fact, it also transformed some of the pre-internet jobs and boosted them just because of social media’s easy reach. Similarly, the smartphone revolution killed off the need to use so many devices like a separate radio, mp3 player, point-and-shoot cameras, physical maps, and even wristwatches; smartphones have fast become a behemoth contributor towards the economy, with mobile tech generating $3.3 trillion in revenues in 2014 [1]. Jobs like social media influencer and online tutors on Youtube and various educational websites are available at fingertips thanks to smartphones.

Warehouses in Guanajuato, Mexico

AI development is extremely fast; however, the background frameworks it needs has not grown at the same pace. It also has applications in the real world, and thus, paints a target on its own back for criticism. There is a huge clamour about how it will replace humans in jobs in many journals and articles. While some jobs will be lost due to AI, it will also produce new opportunities. According to the Autonomous Research Report in 2018, 70% of the front office jobs like tellers, customer service representatives, loan interviewers and clerks will be replaced by AI technologies like chatbots, voice assistants and automated authentication; however, according to Accenture, there will be a net gain in jobs among companies using AI, as well as a net gain in revenues. They claim that AI will create new roles like explaining the deployment of AI technologies, which would still be done by humans. The reasoning they give for the latter is that AI will help people with advice at investments and banking, an improvement on the human agents. In the same article from American Banker [2], the senior VP of First National Bank of Wynne in Arkansas argues that people are replaceable, AI is not. That is, if a person makes mistakes repeatedly, they can be replaced by another human more capable of doing the job; a malfunctioning AI, however, cannot be fired, it needs to be shut down, and replaced with a human, which makes companies wary of using AI. Research by the Royal Bank of Canada states that humane skills like active listening and social perception will help prospective job applicants complement AI technologies, rather than compete with them.

These discussions come from management employees, but that doesn’t mean that we have to agree to them necessarily. They state that there will be more jobs available. However, we also need to look at what kind of jobs AI will create. If we look at job markets as a whole, the trend would be that indeed, new jobs will be created. If we decide to look closely, however, we can see that the kind of jobs AI will take away are the main source of livelihoods for many working-class people. A taxi driver would not be too amused to see an autonomous truck taking away their passengers and wages, especially if they’re struggling to pay their rent each month with a job. I would also respectfully disagree with the aforementioned VP of the Bank; the sentence ‘humans are replaceable’ is not a good look with the current job market prospects. We should be protecting our workforce with the help of technology, not replace them with more technology. We also need to consider that people need to adapt to the new technologies; some might not have the resources to do so. Do they then try to find different jobs, or risk being left behind with the new revolution? 

There are discussions in companies like Accenture, that AI will maximise profits for companies, which will lead to maximization of growth and in turn more employees as companies join more global markets. With even more widespread use of AI, it will be necessary to regulate the ethics behind the usage. This will necessitate employing humans to monitor the decisions taken by AI tools. An example of AI being limited to a single purpose job is given in this New York Times article [3], which quotes an important MIT report [4], “A finely tuned gripping robot can pluck a glazed doughnut and place it in a box with its shiny glaze undisturbed, but that gripper only works on doughnuts, it can’t pick up a clump of asparagus or a car tyre.” A general-purpose AI is still a few years away. AI is normally designed as a tool to help humans do better at their jobs. In fact, ASAPP, a New York-based developer of AI-powered customer service software trains human call centre representatives with the help of AI to be more productive, rather than replacing them with AI [5]. Combining human jobs with AI seems to be the best way to go forward, delicately achieving a balance between productivity and human ingenuity.

The effects of AI will be seen in almost all industries. Since AI is a tool which can be applied in so many industries, there is a huge push to apply AI in various domains like medical imaging, neuroscience, business analysis to even sport science. The very essence of AI is such that it pervades all kinds of job markets without discrimination. It is going to change the landscapes of many job markets. Whether you are a taxi-driver, or a doctor, or an assembly-line worker, it is going to affect your job. The hardest hit will be people who are in no position to learn the new skills needed to complement AI entering our lives. This is because as of now, AI is great at doing single purpose repetitive tasks efficiently. AI is not a messiah which will take us to a proverbial promised land, nor is it a weapon of mass destruction wiping out the planet, it is somewhere in the middle, and like with every new technology, we need to adapt. In fact, the balance is more towards the negative. Have we thought about the sheer displacement of people with more and more jobs utilizing AI? Is AI, and especially complex deep learning models, even necessary in tasks? We need to ensure that we’re not just falling victims to following trendy buzzwords and trying to incorporate the latest technology in our services. We have lots of experience in dealing with revolutions, we need to find a way to deal with this latest revolution and ensure it becomes close to the messiah. Thankfully, we still are some years away from AI pervading all job markets, so we can make concrete plans to handle smoother takeovers. 

To tackle the wave of AI revolution and ensure that we’re not left behind on the kerb while AI takes our jobs away, we need to keep reinventing ourselves. Yuval Harrari, in his article the “Reboot for the AI revolution”, sums it up beautifully. He says, “…humankind had to develop completely new models [to adapt to the Industrial Revolution] — liberal democracies, communist dictatorships and fascist regimes. It took more than a century of terrible wars and revolutions to experiment with these, separate the wheat from the chaff and implement the best solutions.” [6] He then adds that we need a working model to overcome the challenges introduced by the new technology. These words may prove to be seminal in the coming years as AI starts disseminating in all job markets. We may need to turn the wheel- or reinvent it completely, as we adapt to the new revolution.

This article delved into how AI is affecting the job industries. The next follow-up article, “Riding the Wave of AI Job Revolution”, of the same series will cover how we can improvise and adapt to this challenging situation. 

References

[1] Dean Takahashi, “Mobile technology has created 11 million jobs and $3.3 trillion in revenues”, (2015), https://venturebeat.com/2015/01/15/mobile-technology-has-created-11-million-jobs-and-a-3-3-trillion-in-revenues/.

[2] Penny  Crosman,  “How artificial intelligence is reshaping jobs in banking”, (2018), http://files.parsintl.com/eprints/S060220.pdf.

[3] Steve Lohr, “Don’t Fear the Robots, and Other Lessons From a Study of the Dig-ital Economy”, (2020), https://www.nytimes.com/2020/11/17/technology/digital-economy-technology-work-labor.html?searchResultPosition=1.

[4] Elisabeth Reynolds, David Autor, David Mindell,  “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines”, (2020),  http://workofthefuture.mit.edu/wp-content/uploads/2020/11/2020-Final-Report2.pdf

[5] Kenrick Cai Alan Ohnsman, “Meet The AI Designed To Help Humans, Not Replace Them”, (2020), https://www.forbes.com/sites/alanohnsman/2020/07/14/meet-the-ai-designed-to-help-humans-not-replace-them/?sh=42754419572e.

[6] Yuval Noah Harari, “Reboot for the AI revolution”, Nature News550.7676(2017), p. 324.

The face recognition complication

Artificial Intelligence is a subject which is often, inaccurately, talked about in terms of ending the world.  Several films have a theme of a rogue AI destroying the whole world and threatening the existence of humanity. They show a very physical manifestation of AI; however, the problem is much more personal and all the more mordacious. Our faces are collected and are being used to build increasingly powerful, yet error-ridden and human bias induced AI models. A recent survey found that on average, a Londoner is captured on 300 security cameras a day [1]. With this ominous beginning, its time to talk about a specific form of dangers posed by AI- facial recognition based discrimination especially among minority groups.

There has been centuries of oppression of minority groups by humans, it now seems like AI is also adding to it. Amazon created a bot to help with resume sorting, keyword searches based on skills and job profiles, and then recommend candidates. The caveat? The AI showed a bias against women, because it had been fed male-dominated resumes [2]. This kind of bias is an unwanted result, a danger posed by relying too much on AI to help with tasks.

Speaking of blatant discrimination, Michael Kosinski, an AI researcher, published a paper in 2017, where an AI model guessed whether a person was straight or otherwise, just by looking at their facial features [3]. The political, ethical and moral implications of this are dangerous [4], [5]. Especially in the countries where homosexuality is still a criminal offence, and AI which outs you just by looking at your facial expressions- this could set a terrible precedent to any hopes of getting homosexuality decriminalised, and could strengthen the already existing institutional bias against LGBT folks. What was even more surprising about the model, is that it was 81% accurate in correctly identifying men, and 74% for women, using just a single facial feature. With five facial images of a person, this rate increased to 91%. All of these rates are far ahead of identification done by humans.

Another major misfire by AI in face recognition is the discrimination against people of colour [6], which has countless examples of not recognising people of colour as accurately as white males. Detroit Police wrongfully arrested a man of colour using facial recognition software [7] with an inherent bias. There are countless others who are being subjugated to police searches after using AI as a facial recognition software. Another facial recognition software misidentified a person of colour behind bombings in Sri Lanka. The college student received severe abuse when his name was leaked to the public, all over a faulty output.

Joy Buolamwini, the founder of Algorithmic Justice League, an organization trying to raise awareness about the social implications of AI, used AI facial recognition models from a few leading companies like Amazon, Microsoft, etc [8]. All the models performed poorly for minority groups and women. In fact, some of the results were just shocking. Figure 1 shows how two famous black women, Oprah, and Michelle Obama, were labelled by the leading facial recognition software as “appears to be male”, and “young man wearing a black shirt”. The reasons for the underwhelming performance are explained later in the article.

a)Amazon Face Recognition                                                                                        b) Microsoft Face Recognition

Figure 1: Discrimination by Facial Recognition Software

These episodes lead to a necessary discussion about the use of facial recognition software, especially with AI development still in the nascent stage, and above all, such software being unregulated. There haven’t been laws against the use of such software yet, and there has been no proper consolidation of rules regarding the ethics of using image recognition. Yes, there are many benefits to using face recognition software, but currently, the problems outweigh them. The accuracy of any data-based model depends on the data it is fed; facial recognition software depends on the faces provided to it. We as AI researchers dive into every new technology and application we feel AI can be applied to, however, there is a need to stop and have a look around at the legal, moral and philosophical aspects of the technology we are building, before we actually start with our models.

We arrive at the same question again, Do we really need facial recognition? Is AI for detecting faces is at the right threshold to be used effectively in mainstream applications? Who is responsible if a person is discriminated against by a facial recognition algorithm? Who is to blame, when AI accidentally outs the sexual identity of a person, when they are not ready to come out yet? Humans can be taught to be less racist, less sexist and less homophobic, by education. Who is going to teach the AI? On the other hand, is the AI really at fault, being fed predominantly white faces as input to recognise who enters a building, in a cosmopolitan city? Maybe, instead of trying to get that validation accuracy of 99%, instead of trying to  focus on pushing publications, we should have a look at ourselves and ask, are we doing it right? Are we working with the right data? Maybe the fact that AI discriminates based on race, gender, colour, is that because we are letting our human biases into the dataset [9]. AI is fast becoming a proverbial looking glass, reflecting the society’s values. However, with the social media revolution, the extremities of the society are amplified and reach more people than ever. This opens a box of questions. Have we thought about the implications of what we are creating? Have we thought if the law frameworks exist to regulate AI? Have we thought of the ethical implications of the dataset we are trying to collect? So many tech companies are shutting down their facial recognition software. Amazon has decided to shutdown Recognition, IBM has shut down its facial recognition research as well, both because of human rights concerns. Instead of shutting down the research altogether, we could build more robust data to fix historical societal problems. We should focus more on building the legal and ethical foundations and understand more on how we use our data.

References

[1]Luke Dormehl. “Surveillance on steroids: How A.I. is making Big Brother biggerand brainier”. In: (2019).url:https://www.digitaltrends.com/cool-tech/ai-taking-facial-recognition-next-level/.

[2]Julien Lauret. “Amazon’s sexist AI recruiting tool: how did it go so wrong?” In:(2019).url:https://becominghuman.ai/amazons-sexist-ai-recruiting-tool-how-did-it-go-so-wrong-e3d14816d98e.

[3]Yilun Wang and Michal Kosinski. “Deep neural networks are more accurate thanhumans at detecting sexual orientation from facial images.” In:Journal of person-ality and social psychology114.2 (2018), p. 246.

[4]Sam Levin. “New AI can guess whether you’re gay or straight from a photograph”.In: (2017).url:https://www.theguardian.com/technology/2017/sep/07/new-artificial-intelligence-can-tell-whether-youre-gay-or-straight-from-a-photograph.

[5]Paul Lewis. “I was shocked it was so easy”. In: (2018).url:https : / / www .theguardian.com/technology/2018/jul/07/artificial-intelligence-can-tell-your-sexuality-politics-surveillance-paul-lewis.

[6]Fabio Bacchini and Ludovica Lorusso. “Race, again: how face recognition technology reinforces racial discrimination”. In:Journal of Information, Communication and Ethics in Society(2019).

[7]V Steeves J Bailey J Burkell. “AI technologies — like police facial recognition —discriminate against people of colour”. In: (2020).url:https://theconversation.com/ai-technologies-like-police-facial-recognition-discriminate-against-people-of-colour-143227.

[8]Joy Buolamwini. “Arti cial Intelligence Has a Problem With Gender and RacialBias. Here’s How to Solve It”. In: (2019).url:https://time.com/5520558/artificial-intelligence-racial-gender-bias/.

[9]Brendan F Klare et al. “Face recognition performance: Role of demographic information”. In:IEEE Transactions on Information Forensics and Security7.6 (2012),pp. 1789–1801

The first edition is near! Read more about 2 upcoming and very interesting articles

To prove that science and arts do go together, Ngoc Đoàn, Andrius Penkauskas and Ecaterina Grigoriev paired up with a theater group. The AI-students from Tilburg University want to model group dynamics using data about theater performances.

Using ARtInfo in the Valkhof museum Nijmegen. ©Rein Wieringa

The apps RecolourAR and ARtinfo are guaranteed to make any museum trip more fun and interactive. Loes van Bemmel, Master student Artificial Intelligence at Radboud University, will tell you all about them.

 

Get excited with a sneak preview on 2 amazing articles!

The first edition of Turning Magazine publishes on September 1st! You want to know how AI can be utilized in the field of fashion? Then the following 2 articles will catch your interest:

A mirror that can give you fashion advice? Find out more about it in the interview with Alexey Chaplygin, data scientist at PVH corp. 

Read about technology inspired fashion in the article ‘Cyber Couture’. Anneke Smelik, researcher at Radboud University and author of “Ik cyborg. De mens-machine in populaire cultuur.”, will tell all about the futuristic clothing-designs of Iris van Herpen.

The first edition will be released on September 1st – what you can expect

In the upcoming weeks we will post short overviews about what contents you will be able to read about in the first edition of Turning Magazine.

Daphne Lenders, Master student Artificial Intelligence at Radboud University, will tell about Shakespeare-inspired poems, written by an AI. See yourself how the sonnets compare to ‘Hamlet’ or ‘Romeo & Juliet’. 

How Neural Networks can be used to generate new pictures, stories or even new knowledge, will be the topic of Nynke Zwart’s article. Nynke is a Master student Interaction Technology at the University of Twente.