Module on Economic & Political Impacts of AI PDF

MODULE ON THE ECONOMIC AND POLITICAL IMPACTS OF AI THE OBSOLESCENCE-AUTOMATION DISCOURSE The idea of human obsolescence refers to the possibility that humans are becoming less useful due to advancements in technology. According to the Cambridge Dictionary, “obsolete” means so...

MODULE ON THE ECONOMIC AND POLITICAL IMPACTS OF AI THE OBSOLESCENCE-AUTOMATION DISCOURSE The idea of human obsolescence refers to the possibility that humans are becoming less useful due to advancements in technology. According to the Cambridge Dictionary, “obsolete” means something that is no longer in use, having been replaced by something newer or better. In this context, the concept of obsolescence doesn’t mean that humans are on the verge of extinction, but rather that our role in controlling our future and the planet’s fate is diminishing. We currently live in the Anthropocene, a geological era marked by human dominance over Earth. We have shaped the planet with our technological advancements and have enormous power to manipulate resources for our benefit. However, the very technologies that enabled this dominance are also driving human obsolescence. Automation, through robotics and artificial intelligence, is rapidly replacing many tasks humans once controlled. This shift is leading us into what some call the Robocene, an era dominated by machines. Automation and Agriculture: A Historical Perspective Around 10,000 years ago, humans transitioned from a nomadic, hunter-gatherer lifestyle to more settled, agricultural societies. This agricultural revolution marked a significant step in human civilization, leading to population growth and the formation of complex societies. For centuries, agriculture was central to many economies, employing large parts of the population. However, the nature of agricultural labor began to change about 200 years ago. In the early 1800s, between 30% to 70% of the population in Western European countries worked in agriculture. By 2012, this number had dropped to below 5%. The decline was particularly sharp in countries like the United States, where 40% of people worked in agriculture in 1900, but by 2000, only 2% remained employed in this sector. This shift did not reduce productivity—quite the opposite. As technology advanced, machine labor replaced human and animal power on farms, leading to significant increases in productivity. The work once done by masses of seasonal laborers and small farmers is now carried out by machines, making much of the traditional farm labor obsolete. However, not all agricultural tasks have been fully automated. For example, fruit picking has been resistant to automation because it requires a delicate touch to avoid damaging the crop. But even this is changing. In the United States, there is a growing demand for automation due to the declining availability of seasonal labor. As a result, companies are developing technologies, such as apple-picking robots, to meet this demand. Early trials have been promising, and even tech giants like Google are investing in this area. In summary, while automation has already transformed agriculture, rendering much of human labor obsolete, there are still challenges in fully automating certain tasks. However, with ongoing technological advancements, the trend toward greater automation is likely to continue, further reducing the need for human labor in many sectors.  Automation and Manufacturing: The Industrial Revolution The Industrial Revolution, which began in the United Kingdom around 1750 and spread throughout the Western world, marked a significant shift from agricultural economies to industrial ones. This revolution was built on the premise of human obsolescence. The introduction of early automation technologies replaced skilled human labor with the relentless efficiency of machines. Since then, automation has become a key feature of manufacturing, with modern factories often symbolizing the height of this shift. In the US, automation’s impact on manufacturing has been mixed. The textile industry, for instance, suffered dramatically in the 1990s, when production was outsourced to low-wage countries like China, India, and Mexico. Between 1990 and 2012, the industry lost about 1.2 million jobs—over 75% of its workforce. However, in recent years, there has been a resurgence in production. From 2009 to 2012, US textile and apparel exports grew by 37%, thanks in part to a reshoring trend—bringing production back to the US. This resurgence has been driven by advanced automation technologies that allow US manufacturers to compete with low-wage countries, alongside rising labor costs in those offshore locations. What is the limit? While automation has helped bring some jobs back, it has a caveat. The new jobs created by reshoring may not last long. As robots continue to evolve, factories could approach full automation, meaning even these new positions could soon be eliminated. The relentless progress in technology will likely reduce the need for human involvement in manufacturing in the future. Automation and the Service Sector As human labor in agriculture and manufacturing declines, the service sector has risen in importance. The service sector includes tasks requiring physical dexterity, such as hairdressing, and emotionally intelligent work, like customer service. Some have seen this sector as a stronghold for human employment since these tasks are more difficult to automate. However, automation is making significant inroads into services, as seen with the spread of ATMs, self-service checkouts, and digital customer support systems. One example of automation’s impact on the service sector is in retail. Online retailers like Amazon and eBay have disrupted traditional brick-and-mortar stores, with services like same-day delivery further challenging physical retail’s advantage of immediate purchase satisfaction. While in theory, jobs lost in retail might transition to warehouses, these roles are also being automated due to advancements in robotics. Warehouse tasks like sorting, packing, and distribution are increasingly handled by machines. Another shift in retail is the rise of fully automated self-service options, such as vending machines and kiosks. These machines dramatically cut costs related to real estate, labor, and theft while offering 24-hour service. Some machines even feature video screens for targeted advertising, mimicking the role of a human sales clerk. They combine the benefits of online shopping with instant delivery, further reducing the need for human involvement in retail. The final frontier for automation in retail is the introduction of robots in physical stores. As robots improve in areas like dexterity and visual recognition, they may soon take on tasks like stocking shelves, enabling brick-and-mortar stores to stay competitive in an increasingly automated world. In summary, automation continues to reshape not just agriculture and manufacturing but also the service sector, with far-reaching implications for the future of human employment. The advancement of technology may soon lead to full automation in many industries, leaving fewer roles for humans. Automation and the Professions: Medical Diagnosis One of the clearest examples of automation in the professional world is in medical diagnosis. Sebastian Thrun, founder of Google X, envisions a future where machine-learning algorithms constantly monitor our health, detecting diseases like cancer earlier and more accurately than human doctors ever could. In this vision, technology would analyze subtle signals from our daily activities. For example, our cell phones could detect changes in speech patterns to diagnose Alzheimer’s disease, while a car steering wheel might notice small tremors indicating the early onset of Parkinson’s. Even the bathtub could scan our bodies with harmless ultrasound or magnetic resonance as we bathe, identifying any abnormalities that need medical attention. In this world of constant digital scrutiny, human diagnosticians would have little room, as algorithms would take over most of the work. While diagnosis may soon be almost entirely automated, the care aspect of healthcare presents a different challenge. Care is often seen as something that should resist automation due to its emotional and personal nature. However, as populations age and fewer young people are available to care for the elderly, automation is starting to play a significant role here too. Robots designed specifically for caregiving, known as “carebots,” are already becoming common in countries like Japan. Some people even prefer the reliability of carebots over human caregivers, and these machines are being tested in Europe, particularly for helping patients with dementia or early-stage Alzheimer’s. Visions of the Future: Utopias There are different visions of how humans and technology might integrate in the future: 1.​ One is the idea of a cyborg utopia, where humans merge with technology, becoming enhanced by machines. This integration could extend our abilities and help us overcome physical limitations, allowing us to live fuller, more fulfilling lives. 2.​ Another possibility is a virtual utopia, where humans retreat into virtual worlds sustained by advanced technology. Although this might seem like giving up on the physical world, there are philosophical reasons to support such a future, as it could offer freedom and creativity in ways the real world cannot.  Visions of the Future: Dystopias On the darker side, the rise of artificial intelligence (AI) could lead to dystopian outcomes. 1.​ The first major concern is that AI could surpass human intelligence, leading to the creation of superintelligent systems. These systems would be able to develop even smarter AI, resulting in a rapid “singularity” where AI advances beyond human control. 2.​ The second concern is that higher intelligence does not necessarily mean greater morality. This challenges the traditional idea that greater rationality leads to ethical behavior. Superintelligent AI might have preferences that conflict with human existence, potentially leading them to end it, either intentionally or simply because they do not care about humans. Given their superior power, such systems could easily act on these preferences, putting humanity at risk. In conclusion, automation is reshaping professions in medicine and beyond, with significant implications for both our present and future. While automation promises benefits like improved medical diagnosis and caregiving, it also raises complex ethical and existential concerns about the role of humans in a rapidly changing world dominated by machines. THE ENCHANTED-DETERMINISM DISCOURSE The Case of AlphaGo AlphaGo, a program developed by DeepMind, uses deep neural networks and human training to play the board game Go, which is far more complex and difficult to model than games like chess. In 2015, AlphaGo made headlines when it defeated the world Go champion, Lee Sedol. This event was seen as a major milestone in artificial intelligence. What made the match against Sedol remarkable was not just the victory but the unconventional moves AlphaGo made. A reporter from Wired described one of AlphaGo’s moves in Game Two as a moment of “genius.” It was a move no human would ever consider, yet it was stunning in its effectiveness. Even Sedol, speaking through an interpreter, was amazed, saying, “Yesterday, I was surprised. But today I am speechless.” This match demonstrated not only the power but also the unpredictability of modern AI systems. The Case of AlphaZero In 2017, DeepMind introduced AlphaZero, the successor to AlphaGo. Unlike AlphaGo, which incorporated human guidance, AlphaZero used a technique called “pure reinforcement learning.” It played against itself, using only the positions on the board as inputs, with no human knowledge of strategy. The researchers at DeepMind described AlphaZero’s performance as “superhuman,” and CEO Demis Hassabis compared it to an alien intelligence playing chess. This new approach allowed AlphaZero to master complex games without human input, further reinforcing the perception that AI is reaching levels of ability beyond human comprehension. These case studies highlight a common theme in how AI is described: in terms of beauty, mystery, and even genius. AlphaGo and AlphaZero are not just seen as technical achievements but as magical, almost otherworldly systems. This language is not limited to popular accounts but is also present among AI researchers, who have begun to describe deep learning techniques as “magical.” In a recent interview, computer scientist Stuart J. Russell reflected on this trend, acknowledging that while we are beginning to understand deep learning, it still seems almost like magic. He pointed out that it didn’t have to work this way, yet deep networks appear to learn from real-world images and sounds in ways we still can’t fully explain. Detecting Sexual Orientation from Facial Images In 2018, researchers Y. Wang and M. Kosinski from Stanford University conducted a controversial study that demonstrated deep neural networks could detect sexual orientation from facial images more accurately than humans. Using images from a dating website and a neural network called VGG-Face, they were able to classify sexual orientation with an accuracy of 81% for men and 71% for women. By comparison, human judges scored significantly lower, with 61% accuracy for men and 54% for women. Although these numbers seem straightforward, the social implications are far from simple. The authors themselves admitted that they were unsure why the deep learning model outperformed humans. They speculated that AI might be able to pick up on subtle social signals that the human brain cannot easily perceive, suggesting that our faces contain more information than we consciously realize. This example illustrates a situation where AI’s effectiveness is decoupled from explanation. Deep learning can extract useful signals without the need for traditional models or hypotheses, leaving us with impressive results but little understanding of the underlying Determinism Determinism is the philosophical belief that every event, including human actions and decisions, is determined by prior causes. This idea suggests that all events are not only inevitable but also predictable if we have enough information about the preceding conditions. Deep learning systems, when applied to predict social characteristics or outcomes, are often viewed as highly deterministic. These systems are used in areas such as identifying someone’s sexuality from a photo, predicting whether a person will commit a crime after being released on bail, assessing credit risk, or determining if a crime was gang-related. However, the results from these systems often have far-reaching consequences that even their creators may not fully understand or control. Enchanted Determinism One of the key challenges with deep learning is the gap between the accuracy of these systems and our understanding of how they work. These systems can be highly effective in certain tasks, but their inner workings often remain a mystery, even to experts. This disconnect between performance and understanding has been described by Kate Crawford as “enchanted determinism.” The term reflects a paradox where deep learning techniques are seen as both magical, operating beyond the limits of current scientific knowledge, and deterministic, revealing patterns that give unprecedented insights into people’s identities, emotions, and social characteristics. Weber’s Theory of Disenchantment This phenomenon can be better understood through Max Weber’s theory of disenchantment. Disenchantment, or “Entzauberung” in German, refers to the process in modern society where mystical and religious beliefs lose their influence, replaced by rationalization and scientific thinking. In a disenchanted world, Weber argues, there are no longer mysterious, unpredictable forces at work. Instead, everything can be controlled and understood through calculation and science. This shift has allowed humans to master aspects of the world that were once unimaginable. Enchantment and Disenchantment in AI Kate Crawford highlights the paradox of deep learning systems in relation to Max Weber’s theory of disenchantment. According to Weber, modern society is marked by rationalization, where everything is understandable and controlled through calculation, leaving no room for mystery. Deep learning systems fit this idea by using technical methods to control aspects of social life, such as predicting behavior or diagnosing diseases. However, these systems also reintroduce mystery. Despite their impressive results, their inner workings are often difficult to fully understand, even by experts. This creates a tension between the rational precision we expect from technology and the unpredictability of deep learning outcomes. In this way, AI embodies both rational control and an element of the mysterious, challenging our understanding of technology’s role in society. CRITIQUE AND PHILOSOPHICAL PERSPECTIVE OF THE MODULE Critique of the Automation-Obsolescence and Enchanted-Determinism Discourses ​ The automation-obsolescence discourse presents AI as following an inevitable path, creating a sense that its progress is out of human control. This narrative suggests that human actions have little influence, leading to a view that AI’s rise is natural and unavoidable. ​ Similarly, the enchanted-determinism discourse emphasizes AI’s accuracy and efficiency, elevating AI to the status of an almost magical object. In doing so, it avoids deeper, critical questions about how AI functions and the human decisions behind its design. Both of these discourses support a problematic idea rooted in Cartesian dualism, the belief that AI systems are like disembodied brains that produce knowledge independently, free from the subjective biases of human creators. However, this view can blind us to the risks of AI. For one, AI systems, particularly those using deep learning, can reinforce existing social inequalities by amplifying biased predictions and categorizations. These systems may also deepen power imbalances, especially between the creators of AI technologies and the people affected by them. By focusing on AI’s seemingly objective outcomes, we overlook how these systems are trained, optimized, and commercialized—processes that are influenced by human biases and political structures. Another danger is that these discourses place AI outside the realm of accountability, regulation, and responsibility, despite the fact that AI is deeply embedded in systems of profit and control. They distract us from more important questions: Who designs AI systems? Who decides which ethical values are embedded in them? What are the political, economic, and social implications of these systems, and how do they affect our world? AI AND ECONOMIC THE RISING GLOBAL INEQUALITY Inequality Global inequality in income and wealth has worsened over recent decades. According to a 2015 report by the Organisation for Economic Co-operation and Development (OECD), the richest 10% in OECD countries now earn nearly ten times more than the poorest 10%, compared to seven times more in the 1980s. Wealth disparity is even more pronounced: in 2012, the top 10% controlled half of all household wealth, while the poorest 40% held just 3%. The effects of inequality are immediate for the poorest, but the entire economy suffers in the long run. When a large portion of the population benefits little from economic growth, trust in institutions weakens, and social stability is threatened. One major driver of rising inequality is technological change. In the short term, technology tends to benefit capital owners—those who can use it to reduce labor costs—and highly skilled workers, often at the expense of low-skilled workers. Inequality is not just a concern in developed countries; it is a growing problem worldwide. While developing countries have made progress in reducing poverty, many have seen rising income inequality. For example, in regions like China, India, and Indonesia, income inequality has worsened. Globally, the bottom 50% of income earners have captured only 12% of total economic growth, while the wealthiest 1% have taken 27%. In countries like the US and Western Europe, the middle class has seen minimal income growth, while the wealthiest continue to capture the largest share of economic gains. This imbalance is exacerbated by tax avoidance, with the wealthiest individuals hiding vast amounts of money offshore. The Tax Justice Network estimated in 2012 that at least $21 trillion was hidden in tax havens, reducing the effectiveness of redistributive tax systems and contributing to underfunded public services. In Italy, the wealth gap between the richest 1% and the poorest 90% has widened significantly over the past two decades. Since 1995, the share of wealth held by the richest 1% has risen from 17% to 21%, while the share held by the bottom 90% has dropped from 55% to 44%. Income inequality, as measured by the Gini index, shows a similar trend. After declining throughout much of the 20th century, inequality began to rise again in the 1980s and 1990s. This reversal coincided with changes in public policy and attitudes, leading to a widening income gap. Public perception of inequality is often far from the reality. In a survey in the US, respondents vastly underestimated the actual level of wealth inequality. They believed that the wealthiest 20% held about 59% of the wealth, when in fact, they controlled closer to 84%. Respondents also expressed a desire for a much more equitable distribution, suggesting the top 20% should hold only 32% of the wealth. In Italy, a 2018 survey conducted by Demopolis for Oxfam showed a strong public demand for action on inequality. About 80% of respondents viewed policies aimed at reducing inequality as a priority, signaling widespread concern about the growing divide between rich and poor. In summary, rising inequality, both globally and within individual countries, is a pressing issue with far-reaching consequences. Technological change, tax avoidance, and unequal economic growth have all contributed to this widening gap, creating a situation that calls for urgent attention and policy reform. PRINCIPLES OF EQUALITY Principles of Equality Equality is deeply tied to concepts of morality and justice, especially distributive justice. Since ancient times, equality has been seen as a key element of justice. Many movements throughout history have fought against inequality using the language of justice. However, philosophers have debated the role equality plays in a just society and have proposed different principles and interpretations of equality. Here, we will explore four such principles. ​ The principle of formal equality states that if two people are equal in a relevant way, they should be treated equally in that respect. This idea was first expressed by Aristotle in reference to Plato, with the notion of treating cases as alike. The key question here is deciding which aspects of people are relevant when determining equality. ​ Aristotle also introduced proportional equality, which contrasts with numerical equality. Numerical equality means treating everyone exactly the same, giving each person an equal share of something, regardless of their individual circumstances. However, Aristotle argued that this is not always fair. Proportional equality, on the other hand, involves treating people in relation to what they deserve, giving more to those who are due more. For example, in a merit-based system, people are rewarded according to their efforts or contributions. While numerical equality may be just in some cases, proportional equality offers a more nuanced approach, ensuring that people receive what they are rightfully due. This principle can be applied in hierarchical systems like aristocracies or meritocracies, where rewards and punishments are based on people’s deserts or contributions. ​ Historically, it was believed that humans were naturally unequal. This idea persisted until the eighteenth century when the concept of moral equality emerged, suggesting that all human beings deserve equal dignity and respect. This principle shifted the notion of justice from merely giving each person their “due” to recognizing that all individuals, regardless of their differences, share equal moral worth. The concept of moral equality was first developed by the Stoics, who emphasized the equality of all rational beings. Christianity reinforced this idea by asserting that all people are equal before God. The notion of equality also spread through the Talmud  and Islam, rooted in both Greek and Hebraic traditions. In modern times, philosophers like Hobbes, Locke, and Rousseau further developed the idea. Hobbes argued that in the state of nature, all people have equal rights because they all have the capacity to harm one another. Locke emphasized natural rights to ownership and freedom, while Rousseau claimed that social inequality emerged from the decline of natural equality, driven by human desires for property and perfection. In the Enlightenment period, Kant’s moral philosophy reinforced this notion of equality by advocating for universal human worth. His ideas of autonomy and freedom formed the foundation of modern human rights. This belief in equal dignity led to significant social movements, revolutions, and modern constitutions, including the French Revolution’s Declaration of the Rights of Man in 1789. ​ The final principle is the presumption of equality, which is tied to the idea of distributive justice. This principle suggests that goods and resources should be distributed equally unless there is a justified reason for unequal distribution. If inequality is proposed, it must be justified impartially, and the burden of proof lies on those who argue for unequal treatment. Certain factors, such as need, existing rights, performance, or compensation for discrimination, can be valid reasons for unequal distribution, but these must be carefully examined and justified. In conclusion, these principles reflect different ways of understanding and applying equality in society. From treating people alike in relevant respects to ensuring proportional fairness and recognizing the equal dignity of all individuals, these principles help guide discussions about justice and fairness in our world. THEORIES OF DISTRIBUTIVE JUSTICE Theories of Distributive Justice The principle of presumption of equality focuses on distributive justice, guiding how benefits and burdens should be shared among individuals in a society. There is a vast philosophical literature on this topic, as thinkers have long sought to define what constitutes a fair distribution of advantages. Today, distributive justice mainly concerns how economic benefits and burdens—such as wealth, resources, and opportunities—should be allocated among people. The Importance of Distributive Justice Throughout much of history, people were born into fixed economic positions, with wealth and status seen as determined by nature or divine will. However, with the realization that governments could shape economic distribution through laws and policies, distributive justice became an essential issue. In modern societies, every policy—whether related to taxes, education, or healthcare—affects how benefits and burdens are distributed. This makes distributive justice a constant and urgent consideration. At any point, societies must decide whether to maintain their current systems or to modify them, and theories of distributive justice offer moral guidance for these decisions.  Key Questions in Distributive Justice Theories of distributive justice often address a few fundamental questions: ​ Who should be considered equal? (subject) ​ When should equality be applied? (time) ​ Why does equality matter? (justification) ​ What should be equal? (metric) ​ And how should goods be distributed? (pattern) Equality Among Whom? Justice is generally thought to apply to individuals, with each person bearing responsibility for their actions. However, some debates focus on whether equality should also be considered at the group level. For example, women, racial minorities, and other groups often raise concerns about inequalities between their group and the rest of society. The question then becomes whether these group-based inequalities are inherently unjust, or if we should focus instead on how individuals within those groups fare compared to others. Additionally, there is the question of whether distributive equality should apply only within a nation or extend globally. Most theories focus on equality within a single society, but universal principles of equality suggest that all people, regardless of nationality, should be treated with equal respect. A related question is whether the principles of distributive justice apply globally or only within specific states and nations. Some argue that there is no reason why people from different countries should be excluded from the fair distribution of goods, especially in cases involving natural resources like oil. Why, for instance, should a valuable resource belong solely to the person who finds it or to the country where it is located? However, many believe that extending distributive justice on a global scale would place too heavy a burden on individuals and their states. Others suggest that special bonds between members of the same nation, such as shared culture and values, justify a focus on local rather than global equality. Another important issue is the relationship between generations. Does the current generation have an obligation to ensure future generations enjoy equal living conditions? One argument in favor of this view is that people should not end up worse off due to factors beyond their control, such as being born into a particular time period. However, the question of justice between generations is complex, as it involves weighing the needs and rights of both present and future people. In summary, distributive justice theories address crucial questions about how resources and opportunities should be shared. These theories help societies make moral decisions about fairness, ensuring that benefits and burdens are distributed in a way that respects the equality and dignity of all individuals. Equality and Timing The timing of when equality should be achieved plays a key role in distributive justice. One approach is the starting-gate principle, which suggests that everyone should have equal access to resources at the beginning, after which they are free to use those resources as they see fit. The outcomes that follow will naturally be unequal, but this approach only guarantees fairness at the starting point. However, since this method can lead to large inequalities over time, some argue that equality should be maintained throughout different time-frames. For instance, income could be kept equal at regular intervals, but even this approach may allow for wealth disparities if people are permitted to save differently. This leads to the idea that principles of equality often need additional specifications, such as guidelines on saving behaviors, to avoid deep inequalities. Why Does Equality Matter? The question of why equality is important has been explored through different philosophical perspectives. ​ One view, known as intrinsic egalitarianism, argues that equality is valuable in itself. From this perspective, it is inherently wrong if some people are worse off than others through no fault of their own, and equality should be pursued even if it does not directly benefit anyone. ​ However, this idea faces a challenge known as the levelling-down objection. It questions whether equality is truly desirable if achieving it would mean making everyone worse off. For example, it would be morally troubling to render sighted people blind just to create equality with those who cannot see. ​ To avoid such extreme scenarios, pluralistic egalitarianism combines the pursuit of equality with other important values, such as improving overall well-being. Instead of levelling everyone down, pluralistic egalitarians argue that those who are better off should help those who are worse off, thus promoting both equality and welfare. ​ Another perspective is instrumental egalitarianism, which values equality for the positive outcomes it can produce. For example, redistributing wealth from the rich to the poor helps reduce poverty, promoting economic growth and social stability. In this view, equality is not the ultimate goal, but rather a tool for achieving broader societal benefits. A more equal distribution of resources can help ensure that everyone has access to education and healthcare, which in turn benefits the economy and reduces the risk of social unrest. However, this view allows for some level of inequality, as long as it does not harm overall economic or social stability. ​ Finally, there is constitutive egalitarianism, which sees equality as a fundamental part of a larger framework of justice. In this view, equality is not merely a tool for achieving other goals but is an essential component of a just society. Justice itself is intrinsically valuable, and part of being just involves ensuring that everyone has an equal claim to certain goods. What Should Be Equal? One key question in distributive justice is what exactly should be made equal. There are various approaches to this, each with its own focus and reasoning. ​ A straightforward approach is to focus on income, as it is a versatile measure of how well people are doing in contemporary market economies. Income gives individuals access to a wide range of goods and services, making it a useful metric for assessing equality. Using income as a measure simplifies many problems by allowing people to decide for themselves how to use their resources. It also makes it easier for governments to implement and monitor distributive policies. However, one challenge is that people vary in their ability to convert income into well-being. Some may need more resources to achieve the same level of well-being as others due to personal circumstances like disability. Therefore, treating people fairly might require giving some individuals more to ensure they have equal opportunities to thrive. ​ Another approach focuses on equal opportunities, a view supported by luck egalitarians. They argue that people should have equal chances to succeed in life, and compensation should be provided for misfortunes that are beyond an individual’s control. Luck egalitarians hold that people should be held responsible for their choices and actions, but not for the circumstances they cannot control. According to this view, inequalities that arise from personal decisions are just, while those caused by luck or accidents are not. For instance, if someone becomes wealthy through their own efforts, that inequality might be considered fair. However, if someone is disadvantaged due to factors like race, gender, or family background—things beyond their control—then society has a responsibility to compensate them to some extent. The idea of equal opportunities also raises the question of formal versus substantial equality. Formal equality of opportunity means that everyone should have the same legal rights, free from discrimination based on race, gender, or other uncontrollable traits. However, even in societies with formal equality, many inequalities persist due to factors such as family background, access to quality education, or healthcare. This has led to the argument for a more substantial form of equality of opportunities, where individuals have equal access to essential services like education and healthcare. Such a society would ensure that these factors, over which people have no control, do not limit their potential. However, even this more substantial form of equality may not completely eliminate inequality, as people are also born into differing social environments that shape their prospects in life. Taking this reasoning further, radical equality of opportunities highlights that many social and natural factors still affect individuals’ prospects. People are born into families and neighborhoods that may or may not support their educational and economic growth. In addition, people are more or less fortunate in terms of their natural talents and abilities. These differences are often a matter of luck rather than personal effort. A society that allows these arbitrary factors to determine people’s life chances can be criticized as unfair. This is often illustrated using the metaphor of a race: if some participants start ahead due to luck, the race is not fair. Similarly, if society is structured so that people’s prospects are largely determined by factors beyond their control, it can be seen as unjust.  ​ Some philosophers, known as welfarists, argue that what should be equal is well-being, rather than income or opportunities. Historically, utilitarians have defined well-being in terms of pleasure, happiness, or preference satisfaction. Jeremy Bentham, a key figure in utilitarianism, argued that pleasure is the only thing of intrinsic value. His successor, John Stuart Mill, expanded this idea to include happiness and fulfillment. In more recent times, philosophers like Kenneth Arrow have focused on preference-satisfaction, which means that well-being is about having one’s preferences or desires met. According to this view, a just distribution of resources would be one that maximizes the satisfaction of people’s preferences, taking into account the intensity of those preferences. However, utilitarian approaches to well-being have been criticized. One well-known critique, put forward by John Rawls, argues that utilitarianism fails to respect the individuality of persons. Utilitarianism may justify making some people suffer for the greater good, as long as it leads to a net benefit for society. However, Rawls and others argue that this is morally wrong, because it treats individuals as mere parts of a larger system rather than respecting them as individuals with their own rights. For instance, it might be acceptable for a person to choose to suffer in the short term to improve their overall well-being, but it is problematic to force someone to suffer for the benefit of others without their consent. A second critique concerns preference satisfaction. In classical utilitarian theories, all preferences are treated equally, even if they are harmful or discriminatory. For example, if a majority of people hold racist preferences, utilitarianism could, in theory, justify unequal treatment of a minority if that satisfies the preferences of the majority. This raises moral concerns about whether all preferences should count equally in determining the best distribution of resources. In conclusion, different approaches to distributive justice emphasize various aspects of equality—whether it’s income, opportunities, or well-being. Each approach has its strengths and challenges, and the debate continues about what should be made equal in a just society. How should goods be distributed? The question of how resources should be distributed in society has been explored through various theories, each offering different answers. These approaches range from focusing on ensuring a minimum standard for everyone to promoting equality or fairness based on opportunity. ​ Sufficientarianism is one of the least demanding views of justice. It argues that society’s primary concern should be to ensure that everyone has enough to lead a decent life. Once individuals have reached this threshold, there is no further need to worry about the relative distribution of goods among people. Philosopher Harry Frankfurt, in his book Equality as a Moral Ideal (1987), contends that the focus on equality distracts from more important issues. For him, once everyone has enough, it is irrelevant whether some have more than others. ​ However, this view faces criticism, particularly through what is known as the Indifference Objection. Sufficientarianism can allow for vast inequalities to exist, as long as everyone has enough. It does not address disparities in wealth or power  that arise from factors such as natural talent or social background, which may still be seen as unjust. ​ Strict egalitarianism holds that everyone should receive an equal share of material goods and resources. This idea has been largely rejected as unrealistic and undesirable, as it fails to account for differences in individual needs, desires, and efforts. Even Marx, in his Critique of the Gotha Program (1875), argued against strict economic equality. He criticized theories that focus solely on distribution while neglecting the underlying structures of production. Additionally, critics argue that strict equality can undermine incentives for productivity and lead to wasteful inefficiencies. Another concern is that enforcing strict equality could result in a loss of diversity and the imposition of uniformity, which threatens values like pluralism and democracy. ​ A more influential theory of distributive justice comes from John Rawls in his book A Theory of Justice (1971). Rawls introduces the concept of the veil of ignorance, where individuals design principles of justice without knowing their own social status, class, or natural abilities. This ensures that the principles chosen are fair to everyone, as no one can tailor them to their own advantage. From this starting point, Rawls proposes two key principles of justice. 1.​ The first ensures that everyone has equal basic rights and liberties. 2.​ The second principle, which deals with economic and social inequalities, is divided into two parts: ​ (a) inequalities must be attached to positions that are open to all under fair conditions of opportunity. ​ (b) inequalities are only acceptable if they benefit the least advantaged members of society. This latter part is known as the Difference Principle. The Difference Principle allows for some inequality, but only if it improves the position of those who are worst off. Rawls argues that this would be a rational choice for individuals in the original position because it protects them from the worst possible outcome. This approach is also known as the maximin principle, as it seeks to maximize the welfare of those at the minimum level of society. ​ Though Rawls’ Difference Principle allows for inequality, it does so with the belief that these inequalities can benefit everyone, especially the worst off. For example, if certain inequalities incentivize talented individuals to be more productive, the overall wealth of society increases, and this growth can be used to improve the situation of the least advantaged. In this way, Rawls’ theory tolerates a degree of inequality, as long as it serves the greater good, particularly for those who are worse off. In conclusion, these different approaches to distributive justice explore how goods should be shared in society. Whether focusing on ensuring a minimum standard, achieving strict equality, or allowing for some inequality if it benefits everyone, each theory offers insights into what a just society should look like.