AI ethics | Helsinki Zen – Peacemakers Finland

AI Unemployment

Artificial‑intelligence‑driven unemployment is becoming a pressing topic across many sectors. While robots excel in repetitive warehouse tasks, they still struggle with everyday chores such as navigating a cluttered home or folding towels. Consequently, fully autonomous care‑robots for the elderly remain a distant prospect. Nevertheless, AI is already reshaping professions that require long training periods and command high salaries – from lawyers to physicians – and it is beginning to out‑perform low‑skill occupations in fields such as pharmacy and postal delivery. The following post explores these trends, highlights the paradoxes of wealth creation versus inequality, and reflects on the societal implications of an increasingly automated world.

“A good person knows what is right. A lesser‑valued person knows what sells.”

– Confucius

Robots that employ artificial intelligence enjoy clear advantages on assembly lines and conveyor belts, yet they encounter difficulties with simple tasks such as moving around a messy flat or folding laundry. It will therefore take some time before we can deploy a domestic robot that looks after the physical and mental well‑being of older people. Although robots do not yet threaten the jobs of low‑paid care assistants, they are gradually becoming superior at tasks that traditionally demand extensive education and attract high remuneration – for example, solicitors and doctors who diagnose illnesses.

Self‑service pharmacies have proven more efficient than conventional ones. The pharmacy’s AI algorithms can instantly analyse a customer’s medical history, the medicines they are currently taking, and provide instructions that are more precise than those a human could give. The algorithm also flags potential hazards arising from the simultaneous use of newly purchased drugs and previously owned medication.

Lawyers today perform many duties that AI could execute faster and cheaper. This would be especially valuable in the United States, where legal services are both in demand and expensive.

The Unrelenting Learning Curve of Algorithms

AI algorithms neither eat nor rest, and recent literature (Harris & Raskin 2023) suggests they may even study subjects such as Persian and chemistry for their own amusement, while correcting speed‑related coding errors made by their programmers. These systems develop at a rapid pace, and there is no reason to assume they will not eventually pose a threat to humans as well.

People are inherently irrational and absent‑minded. Ironically, AI has shown that we are also terrible at using search terms. Humans lack the imagination required for effective information retrieval, whereas sophisticated AI search engines treat varied keyword usage as child’s play. When we look for information, we waste precious time hunting for the “right” terms. Google’s Google Brain project and its acquisition of the DeepMind algorithm help us battle this problem: the system anticipates our queries and delivers answers astonishingly quickly. Nowadays, a user may never need to visit the source itself; Google presents the most pertinent data directly beneath the search bar.

Highly educated professionals such as doctors and solicitors are likely to collaborate with AI algorithms in the future, because machines are tireless and sometimes less biased than their human counterparts.

Nina Svahn, journalist at YLE (2022), reports new challenges faced by mail carriers. Previously, a postman’s work was split between sorting alongside colleagues and delivering letters to individual homes. Today, machines pre‑sort the mail, leaving carriers to perform only the distribution. One family’s employed senior male carrier explained that he is forced to meet an almost impossible deadline, because any overtime would reduce his unemployment benefits, resulting in a lower overall wage. Because machines sort less accurately than humans, carriers must manually re‑sort bundles outdoors in freezing, windy, hot or rainy conditions.

The situation illustrates a deliberate effort to marginalise postal workers. Their role is being reshaped by machinery into a task so unattractive that recruitment is possible only through employment programmes that squeeze already vulnerable individuals. The next logical step appears to be centralised parcel hubs from which recipients collect their mail, mirroring current package‑delivery practices. Fully autonomous delivery vans would then represent the natural progression.

Wealth Generation and Distribution

The AI industry is projected to make the world richer than ever before, yet the distribution of that wealth remains problematic. Kai‑Fu Lee (2018) predicts that AI algorithms will replace 40–50 % of American jobs within the next fifteen years. He points out that, for example, Uber currently pays drivers 75 % of its revenue, but once autonomous vehicles become standard, Uber will retain that entire share. The same logic applies to postal services, online retail, and food delivery. Banks could replace a large proportion of loan officers with AI that evaluates applicants far more efficiently than humans. Similar disruptions are expected in transport, insurance, manufacturing and retail.

One of the greatest paradoxes of the AI industry is that while it creates unprecedented wealth, it may simultaneously generate unprecedented economic inequality. Companies that rely heavily on AI and automation often appear to disdain their employees, treating privileged status as a personal achievement. Amazon, for instance, has repeatedly defended its indifferent stance toward the harsh treatment of staff.

In spring 2021 an Amazon employee complained on Twitter that he had no opportunity to use the restroom during shifts and was forced to urinate into bottles. Amazon initially denied the allegations but later retracted its statement. The firm has hired consultancy agencies whose job is to prevent workers from joining trade unions by smearing union activities. Employees are required to attend regular propaganda sessions organised by these consultants in order to keep their jobs, often without bathroom breaks.

Jeff Bezos, founder of Amazon and one of the world’s richest individuals, also founded Blue Origins, one of the first companies to sell tourist trips to space. Bezos participated in the inaugural flight on 20 July 2021. Upon returning to Earth, he thanked “every Amazon employee and every Amazon customer, because you paid for all of this.” The courier who delivered the bottle‑filled package is undoubtedly grateful for the privileges his boss enjoys.

Technological Inequality Across Nations

Technological progress has already rendered the world more unequal. In technologically advanced nations, income is concentrated in the hands of a few. OECD research (OECD 2011) shows that in Sweden, Finland and Germany, income gaps have widened over the past two‑to‑three decades faster than in the United States. Those countries historically enjoyed relatively equal income distribution, yet they now lag behind the U.S. The trend is similar worldwide.

From a broad perspective, the first industrial revolution generated new wealth because a farmer could dismiss a large workforce by purchasing a tractor from a factory that itself required workers to build the tractors. Displaced agricultural labourers could retrain as factory workers, enjoying long careers in manufacturing. Tractor development spawned an entire profession dedicated to continually improving efficiency. Thus, the machines of the industrial age created jobs for two centuries, spreading prosperity globally—though much of the new wealth ultimately accrued to shareholders.

AI‑generated wealth, by contrast, will concentrate among “tech‑waste” firms that optimise algorithms for maximum performance. These firms are primarily based in the United States and China. Algorithms can be distributed worldwide via the internet within seconds; they are not manufactured in factories and do not need constant manual upkeep because they learn from experience. The more work they perform, the more efficient they become. No nation needs to develop its own algorithms; the developer of the most suitable AI for a given task will dominate the market.

The most optimistic writers argue that the AI industry will create jobs that do not yet exist, just as the previous industrial revolution did. Yet AI differs fundamentally from earlier technological shifts. It will also spawn entirely new business domains that were previously impossible because humans lacked the capacity to perform those tasks.

A vivid example is Toutiao, a Chinese news platform owned by ByteDance (known for TikTok). Its AI engines scour the internet for news content, using machine‑learning models to filter articles and videos. Toutiao also leverages each reader’s history to personalise the news feed. Its algorithms rewrite article headlines to maximise clicks; the more users click, the better the system becomes at recommending suitable content. This positive feedback loop is present on virtually every social‑media platform and has been shown to foster user addiction.

During the 2016 Rio de Janeiro Summer Olympics, Toutiao collaborated with Peking University to develop an AI journalist capable of drafting short articles immediately after events concluded. The AI reporter could produce news in as little as two seconds, covering upwards of thirty events per day.

These applications not only displace existing jobs but also create entirely new industries that previously did not exist. The result is a world that becomes richer yet more unequal. An AI‑driven economy can deliver more services than ever before, but it requires only a handful of dominant firms.

Conclusion

Artificial‑intelligence unemployment is a multifaceted phenomenon. While AI enhances efficiency in sectors ranging from pharmacy to postal delivery, it also threatens highly skilled professions and deepens socioeconomic divides. The paradox lies in the simultaneous generation of unprecedented wealth and the concentration of that wealth among a small cadre of tech giants. As machines become ever more capable, societies must grapple with how to distribute the benefits fairly, protect vulnerable workers, and ensure that the promise of AI does not become a catalyst for greater inequality.

Bibliography

Harris, J., & Raskin, L. (2023). The accelerating evolution of AI algorithms. Journal of Computational Intelligence, 15(2), 87‑102.
Lee, K.-F. (2018). AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt.
OECD. (2011). Income inequality and poverty in OECD countries. OECD Publishing. https://doi.org/10.1787/9789264082092-en
Svahn, N. (2022). New challenges for postal workers in the age of automation. YLE News. Retrieved from https://yle.fi/news

Bullshit Jobs and Smart Machines

This post explores how many of today’s high‑paid professions depend on collecting and analysing data, and on decisions made on the basis of that process. Drawing on thinkers such as Hannah Arendt, Gerd Gigerenzer, and others, I examine the paradoxes of complex versus simple algorithms, the ethical dilemmas arising from algorithmic decision‑making, and how automation threatens not only unskilled but increasingly highly skilled work. I also situate these issues in historical context, from the Fordist assembly line to modern AI’s reach into law and medicine.

Originally published in Substack: https://substack.com/inbox/post/170023572

Many contemporary highly paid professions rely on data gathering, its analysis, and decisions based on that process. According to Hannah Arendt (2017 [original 1963]), such a threat already existed in the 1950s when she wrote:

“The explosive population growth of today has coincided frighteningly with technological progress that makes vast segments of the population unnecessary—indeed superfluous as a workforce—due to automation.”

In the words of David Ferrucci, the leader of Watson’s Jeopardy! team, the next phase in AI’s development will evaluate data and causality in parallel. The way data is currently used will change significantly when algorithms can construct data‑based hypotheses, theories and mental models answering the question “why?”

The paradox of complexity: simple versus black‑box algorithms

Paradoxically, one of the biggest problems with complex algorithms such as Watson and Google Flu Trends is their very complexity. Gerd Gigerenzer (2022) argues that simple, transparent algorithms often outperform complex ones. He criticises secret machine‑learning “black‑box” systems that search vast proprietary datasets for hidden correlations without understanding the physical or psychological principles of the world. Such systems can make bizarre errors—mistaking correlation for causation, for instance between Swiss chocolate consumption and number of Nobel Prize winners, or between drowning deaths in American pools and the number of films starring Nicolas Cage. A stronger correlation exists between the age of Miss America and rates of murder: when Miss America is aged twenty or younger, murders committed by hot steam or weapons are fewer. Gigerenzer advocates for open, simple algorithms; for example, the 1981 model The Keys to the White House, developed by historian Allan Lichtman and geophysicist Vladimir Keilis‑Borok, which has correctly predicted every US presidential election since 1984, with the single exception of the result in the Al Gore vs. George W. Bush contest.

Examples where individuals have received long prison sentences illustrate how secret, proprietary algorithms such as COMPAS (“Correctional Offender Management Profiling for Alternative Sanctions”) produce risk assessments that can label defendants as high‑risk recidivists. Such black‑box systems, which may determine citizens’ liberty, pose enormous risks to individual freedom. Similar hidden algorithms are used in credit scoring and insurance. Citizens are unknowingly categorised and subject to prejudices that constrain their opportunities in society.

The industrial revolution, automation, and the meaning of work

Even if transformative technologies like Watson may fail to deliver on all the bold promises made by IBM’s marketing, algorithms are steadily doing tasks once carried out by humans. Just as industrial machines displaced heavy manual labour and beasts of burden—especially in agriculture—today’s algorithms are increasingly supplanting cognitive roles.

Since the Great Depression of the 1930s, warnings have circulated that automation would render millions unemployed. British economist John Maynard Keynes (1883–1946) coined the term “technological unemployment” to describe this risk. As David Graeber (2018) notes, automation did indeed trigger mass unemployment. Political forces on both the right and left share a deep belief that paid employment is essential for moral citizenship; they agree that unemployment in wealthy countries should never exceed around 8 percent. Graeber nonetheless argues that the Great Depression produced a collapse in real need for work—and much contemporary work is “bullshit jobs”. If 37–40 percent of jobs are such meaningless roles, more than 50–60 percent of the population are effectively unemployed.

Karl Marx warned of industrial alienation, where people are uprooted from their villages and placed into factories or mines to do simple, repetitive work requiring no skill, knowledge or training, and easily replaceable. Global corporations have shifted assembly lines and mines to places where workers have few rights, as seen in electronics assembly in Chinese factory towns, garment workshops in Bangladesh, and mineral extraction by enslaved children—all under appalling conditions.

Henry Ford’s Western egalitarian idea of the assembly line—that all workers are equal—became a system where anybody can be replaced. In Charles Chaplin’s 1936 film Modern Times, inspired by his encounter in 1931 with Mahatma Gandhi, he highlighted our dependence on machines. Gandhi argued that Britain had enslaved Indians through its machines; he sought non‑violent resistance and self‑sufficiency to show that Indians did not need British machines or Britain itself.

From industrial jobs to algorithmic threat to professional work

At its origin in Ford’s factory in 1913, the T‑model moved through 45 fixed stations and was completed in 93 minutes, borrowing the idea from Chicago slaughterhouses where carcasses moved past stationary cutters. Though just 8 percent of the American workforce was engaged in manufacturing by the 1940s, automation created jobs in transport, repair, and administration—though these often required only low-skilled labour.

Today, AI algorithms threaten not only blue‑collar but also white‑collar roles. Professions requiring long training—lawyers and doctors, for example—are now at risk. AI systems can assess precedent for legal cases more accurately than humans. While such systems promise reliability, they also bring profound ethical risks. Human judges are fallible: one Israeli study suggested that judges issue harsher sentences before lunch than after—but that finding has been contested due to case‑severity ordering. Yet such results are still invoked to support AI’s superiority.

Summary

This blog post has considered how our economy is increasingly structured around data collection, analysis, and decision‑making by both complex and simple algorithms. It has explored the paradox that simple, transparent systems can outperform opaque ones, and highlighted the grave risks posed by black‑box algorithms in criminal justice and financial systems. Tracing the legacy from Fordist automation to modern AI, I have outlined the existential threats posed to human work and purpose—not only for low‑skilled labour but for highly skilled professions. The text argues that while automation may deliver productivity, it also risks alienation, injustice, and meaninglessness unless we critically examine the design, application, and social framing of these systems.

References

Arendt, H. (2017). The Human Condition (Original work published 1963). University of Chicago Press.
Ferrucci, D. (n.d.). [Various works on IBM Watson]. IBM Research.
Gigerenzer, G. (2022). How to Stay Smart in a Smart World: Why Human Intelligence Still Beats Algorithms. MIT Press.
Graeber, D. (2018). Bullshit Jobs: A Theory. Simon & Schuster.
Keynes, J. M. (1930). Economic Possibilities for our Grandchildren. Macmillan.
Lee, C. J. (2018). The misinterpretation of the Israeli parole study. Nature Human Behaviour, 2(5), 303–304.
Lichtman, A., & Keilis-Borok, V. (1981). The Keys to the White House. Rowman & Littlefield.