Wealth, with its diverse facets, has consistently captivated, inspired, and been the subject of rigorous analysis. Tracing back to ancient civilizations and progressing to contemporary societies, the amassing of material assets is frequently viewed as a marker of achievement, authority, and sway. Particularly in the context of American cultural values, the quest for financial success extends beyond just economic metrics and is closely associated with ideals of individual accomplishment, liberty, and the aspiration for an improved life quality. While the ambition to attain millionaire or billionaire status grips the societal consciousness, the psychological dimensions linked to wealth are multifaceted and nuanced. Moving past the allure of opulence and the comfort of financial stability, there exists a detailed realm of drives, anticipations, pleasures, and hurdles. When exploring the psychology of wealth, especially set against the backdrop of American societal norms, one encounters a comprehensive history, diverse personal narratives, and evolving societal dynamics that highlight the intricate bond between prosperity and the contours of the human mind.
The allure of wealth, particularly in American culture, has historically captivated the masses. The notion of becoming a millionaire, a term rooted in French origin and introduced by Disraeli in 1826, didn’t gain traction in the United States until the late 19th century. An early mention of this term is traced back to the obituary of Pierre Lorillard I, the founder of a prominent tobacco empire, highlighting the sentiment that his success stemmed from offering a product that catered to the unique desires of the populace.
The trajectory of wealth accumulation in the United States is profound. In 1861, the country boasted only three millionaires, but by 1961 this number had surged to approximately 100,000. Such a significant number of the wealthy suggested the emergence of a distinct subculture within the American landscape. This proliferation naturally sparked curiosity about the shared psychological traits, if any, among the ultra-rich.
Welsh journalist Goronwy Rees, in his 1961 study titled “Multimillionaires: Six Studies in Wealth,” endeavoured to discern common traits among six multimillionaires. These included figures like J. Paul Getty and Aristotle Onassis. The most prominent shared characteristic Rees identified was a willingness to embrace enormous risk. Such individuals didn’t merely gamble, but they also possessed the tenacity to push forward, staking all their gains. In contrast, “The New Millionaires and How They Made Their Fortunes,” a book from the same period, pinpointed extreme self-confidence as the distinguishing trait of the super-rich. Intriguingly, factors often assumed to be pivotal in achieving immense wealth, such as background, familial wealth, or educational attainment, were found to be negligible. This underscores the idea that the apex of wealth often rests on individual endeavours rather than inherited advantage.
However, the narrative changes when examining the children born into opulence. By the late 1970s, psychological evaluations started to shed light on the “poor little rich kid” syndrome. Burton Wixen’s “Children of the Rich” painted a picture of a “golden ghetto” existing within the wealthy American culture. This subset of affluent youth, often characterised as narcissistic and emotionally detached, were seen to share similar behavioural patterns with their less-privileged counterparts. Substance abuse, run-ins with the law, and promiscuity were common themes. The root causes for such behaviours were widely speculated upon, with theories ranging from absentee parents to excessively high expectations.
The 1980s brought forth a new term to describe the psychological strain observed in the wealthy youth: “affluenza.” This affliction manifested in stages, from the initial realisation of their vast wealth to excessive consumption, culminating in a loss of identity and purpose. Such aimlessness was frequently intertwined with substance abuse and other reckless behaviours. An interesting observation was the lack of genuine career pursuits among this group, with many claiming titles such as “movie producers” without substantial accomplishments.
As the dynamics of wealth evolved, particularly with the rise of hedge funds in the mid-2000s, the gap between the ultra-wealthy and the merely wealthy expanded dramatically. Yet, research indicates that the accumulation of vast fortunes doesn’t necessarily equate to heightened happiness. Studies from the Journal of Personality and Social Psychology, both from the mid-1990s and a decade prior, have consistently demonstrated that wealth has a minimal correlation with happiness. Genetic factors have been identified as the predominant determinant of one’s happiness quotient, rendering the size of one’s financial assets relatively inconsequential.
The relationship between affluence and psychological wellbeing offers a profound examination of societal norms, personal goals, and the foundational aspects of satisfaction. Historically, wealth has served as both a symbol of accomplishment and a reflection of the multifaceted nature of human ambition and realisation. This analysis highlights that the appeal of substantial financial assets is deeply rooted in the American cultural ethos. However, achieving such affluence brings with it a spectrum of psychological considerations that surpass simple financial standing. This dynamic poses challenges to conventional perceptions of success, prompting a reevaluation of the true definition of prosperity.
The diverse experiences of the affluent, ranging from self-made tycoons to those inheriting significant wealth, emphasise that wealth alone neither provides solutions to life’s adversities nor assures lasting contentment. Factors propelling individuals to financial accomplishments, such as willingness to take risks and robust self-assuredness, can also introduce personal dilemmas and broader societal issues. Additionally, emerging psychological patterns, like “affluenza,” indicate an urgent need for societies to reassess the standards by which genuine wellbeing is gauged.
In today’s swiftly changing global context, where disparities between the exceedingly wealthy and the general populace expand, and concepts of affluence transform, it is essential to perceive wealth not solely as a financial concept but also as a significant determinant of personal identity, societal interactions, and cultural ambitions. The sustained pursuit for a more profound comprehension of this association encourages academics, decision-makers, and the public to contemplate the core nature of wealth and its implications on human contentment and societal advancements. Progressing in this era marked by unparalleled prosperity and discrepancy, the psychology of wealth persists as a vital perspective to discern the nuances of human goals, triumphs, and the relentless endeavour for a meaningful existence.
In reflecting upon the psychological implications of wealth, it becomes evident that while the quest for financial success is deeply embedded in cultural aspirations, the attainment of such wealth brings with it a complex array of emotional and behavioural challenges. The pursuit of affluence, it seems, is less about the destination and more about the journey and the inherent traits that propel individuals forward.
Japan, a recognised global technological and economic leader, faces a concerning economic narrative steeped in gender disparities. These disparities not only underscore the underuse of the female workforce but also extend to market innovation, corporate governance, and societal evolution. By marginalising a significant portion of its skilled population, Japan is inadvertently limiting its potential for robust and diverse economic growth.
Historically, Japan’s trajectory reveals a pattern of diminished female labour participation, especially when benchmarked against its OECD counterparts. Notably, during the late 20th century, while countries like the U.S. and UK witnessed amplified female engagement in the workforce, Japan lagged behind. Although recent years have seen a moderate upturn in female participation, the impediments to a balanced gender representation remain entrenched.
One prominent barrier is direct discrimination. Many Japanese companies, influenced by ingrained societal norms, have shown a proclivity to favour men for senior roles, operating under the misguided belief that women might prioritise familial commitments over professional duties. Consequently, Japan’s percentage of women in managerial roles pales in comparison to other developed nations. Furthermore, cultural norms which position women primarily as caregivers have led to indirect challenges, particularly in metropolitan areas like Tokyo. Here, working mothers often confront difficulties in accessing childcare, prompting some to leave their jobs.
This gender discrepancy has critical implications for Japan’s economic development. It results in a discernible loss of human capital, thereby impeding the optimal utilisation of the nation’s talent pool. This is manifested not only in workforce figures but also in missed chances for creativity, innovation, and holistic perspectives. Moreover, with Japan grappling with demographic challenges like plummeting birth rates and an aging populace, there’s an impending labour supply crisis. Research by the Recruit Works Institute indicates a potential shortage of 3.41 million workers by 2030, escalating to over 11 million by 2040. Amidst such a backdrop, integrating a larger segment of the female populace into the labour market could prove vital.
Furthermore, gender diversity in decision-making often results in a broad spectrum of products and services. Without adequate representation of women in influential roles, Japan risks sidelining innovations that cater to diverse segments of the population. Evidence also suggests that companies with varied leadership often surpass their homogeneous counterparts in performance. Thus, Japan’s glaring gender gap, particularly in senior corporate roles, could signify missed economic opportunities.
However, initiatives aimed at rectifying these disparities, such as ‘Womenomics’ championed by Prime Minister Shinzo Abe, have yielded only incremental results. More recently, Prime Minister Fumio Kishida has recognised the need to bolster Japan’s birth rate and announced ambitious targets to raise the proportion of female executives in Tokyo stock exchange-listed firms to 30% or more by 2030. Yet, Japan has set and missed similar goals in the past, largely due to pervasive gender norms rooted deeply in the societal fabric.
Historical influences like Confucianism have shaped Japan’s patriarchal hierarchies, positioning men as breadwinners and heads of families, while women are relegated to caregiving roles. These constructs are imbibed from a young age, with educational settings reinforcing gendered behavioural patterns. Such norms invariably affect workplace dynamics, leading to hiring practices and organisational behaviour that echo these traditional gender roles.
Furthermore, Japan’s workplace expectations, characterised by long hours and unwavering commitment to the company, make it difficult for women to ascend to leadership positions. Adding to this is the societal expectation that they shoulder a disproportionate share of domestic responsibilities. Despite offering generous paternity leave provisions, only a mere 14% of Japanese men availed of this benefit in 2021. The resultant unequal division of household labour often results in women either missing out on promotions, settling for lower-paying roles, or reconsidering their family planning decisions.
Past governmental measures aimed at rectifying gender imbalances, whether by introducing leadership quotas, expanding childcare provisions, or enhancing parental leave benefits, have often missed their mark. Recent undertakings have even reportedly exacerbated gender inequality and, in some instances, pushed women into poverty.
Drawing parallels with Singapore’s recent gender equality review could offer Japan insights. A comprehensive review encompassing all life stages and societal segments, combined with feedback from the younger generation, could pave the way for meaningful reforms. Research indicates a growing disillusionment among younger Japanese with traditional gender roles, prompting them to explore alternative lifestyles outside the traditional power structures.
In summary, gender inequality has left an indelible mark on Japan’s economic narrative. Addressing these disparities requires more than just policy modifications; it necessitates a paradigm shift in societal mindsets. Championing gender equality might just be the catalyst Japan needs to achieve unparalleled economic success.
This CreateAnEssay4U special edition brings together the work of previous essays and provides a comprehensive overview of an important technological area of study. For source information, see also:
The advent and subsequent proliferation of Artificial Intelligence (AI) have ushered in an era of profound transformation across various sectors. Notably, within the domain of healthcare, and more specifically within the context of the United Kingdom’s National Health Service (NHS), AI’s incorporation has engendered a myriad of both unparalleled opportunities and formidable challenges. From an academic perspective, there is a burgeoning consensus that AI might be poised to rank among the most salient and transformative developments in the annals of human progression. It is neither hyperbole nor mere conjecture to assert that the innovations stemming from AI hold the potential to redefine the contours of our societal paradigms. In the ensuing discourse, we shall embark on a rigorous exploration of the multifaceted impacts of AI within the NHS, striving to delineate the promise it holds while concurrently interrogating the potential pitfalls and challenges intrinsic to such profound technological integration.
Medical Imaging and Diagnostic Services play a pivotal role in the modern healthcare landscape, and the integration of AI within this domain has brought forth noteworthy advancements. AI’s robust capabilities for image analysis have not only enhanced the precision in diagnostics but also broadened the scope of early detection across a variety of diseases. Radiology professionals, for instance, increasingly leverage these advanced tools to identify diseases at early stages and thereby minimise diagnostic errors. Echocardiography charts, used to gauge heart patterns and detect conditions such as ischemic heart disease, are another beneficiary of AI’s analytical prowess. An example of this is the Ultromics platform from a hospital in Oxford, which employs AI to meticulously analyse echocardiography scans.
Moreover, the application of AI in diagnostics transcends cardiological needs. From detecting skin and breast cancer, eye diseases, pneumonia, to even predicting psychotic occurrences, AI’s potential in medical diagnostics is vast and promising. Neurological conditions like Parkinson’s disease can be identified through AI tools that examine speech patterns, predicting its onset and progression. In the realm of endocrinology, a study used machine learning models to foretell the onset of diabetes, revealing that a two-class augmented decision tree was most effective in predicting diabetes-associated variables.
Furthermore, the global threat of COVID-19 in 2019 also saw AI playing a crucial role in early detection and diagnosis. Numerous medical imaging tools, encompassing X-rays, CT scans, and ultrasounds, employed AI techniques to assist in the timely diagnosis of the virus. Recent studies have spotlighted AI’s efficacy in differentiating COVID-19 from other conditions like pneumonia using imaging modalities like CT scans and X-rays. The surge in AI-based diagnostic tools, such as the deep learning model known as the transformer, facilitates efficient management of COVID-19 cases by offering rapid and precise analyses. Notably, the ImageNet-pretrained vision transformer was used to identify COVID-19 cases using chest X-ray images, showcasing the adaptability and precision of AI in response to pressing global health challenges.
Moreover, advancements in AI aren’t limited to diagnostic models alone. The field has seen the emergence of tools like Generative Adversarial Networks (GANs), which have considerably influenced radiological practices. Comprising a generator that produces images mirroring real ones, and a discriminator that differentiates between the two, GANs have the potential to redefine radiological operations. Such networks can replicate training images and create new ones with the training dataset’s characteristics. This technological advancement has not only aided in tasks like abnormal detection and image synthesis but has also posed challenges even for experienced radiologists, as discerning between GAN-generated and real images becomes increasingly intricate.
Education and research also stand to benefit immensely from such advancements. GANs have the potential to swiftly generate training material and simulations, addressing gaps in student understanding. As an example, if students struggle to differentiate between specific medical conditions in radiographs, GANs could produce relevant samples for clearer understanding. Additionally, GANs’ capacity to model placebo groups based on historical data can revolutionise clinical trials by minimising costs and broadening the scope of treatment arms.
Furthermore, the role of AI in offering virtual patient care cannot be overstated. In a time where in-person visits to medical facilities posed risks, AI-powered tools bridged the gap by facilitating remote consultations and care. Moreover, the management of electronic health records has been vastly streamlined due to AI, reducing the administrative workload of healthcare professionals. It’s also reshaping the dynamics of patient engagement, ensuring they adhere to their treatment plans more effectively.
The impact of AI on healthcare has transcended beyond diagnostics, imaging, and patient care, making significant inroads into drug discovery and development. AI-driven technologies, drawing upon machine learning, bioinformatics, and cheminformatics, are revolutionising the realm of pharmacology and therapeutics. With the increasing challenges and sky-high costs associated with drug discovery, these technologies streamline the processes and drastically reduce the time and financial investments required. Historical precedents, like the AI-based robot scientist named Eve, stand as a testament to this potential. Eve not only accelerated the drug development process but also ensured its cost-effectiveness.
AI’s capabilities are not just confined to the initial phase of scouting potential molecules in the field of drug discovery. There’s a promise that AI could engage more dynamically throughout the drug discovery continuum in the near future. The numerous AI-aided drug discovery successes in the literature are a testament to this potential. A notable instance is the work by Toronto-based firm, deep genomics. Harnessing the power of an AI workbench platform, they identified a novel genetic target and consequently developed the drug candidate DG12P1, aimed at treating a rare genetic variant of Wilsons’ disease.
One of the crucial aspects of drug development lies in identifying novel drug targets, as this could pave the way for pioneering first-in-class clinical drugs. AI proves indispensable here. It not only helps in spotting potential hit and lead compounds but also facilitates rapid validation of drug targets and the subsequent refinement in drug structure design. Another noteworthy application of AI in drug development is its ability to predict potential interactions between drugs and their targets. This capability is invaluable for drug repurposing, enabling existing drugs to swiftly progress to subsequent phases of clinical trials.
Moreover, with the data-intensive nature of pharmacological research, AI tools can be harnessed to sift through massive repositories of scientific literature, including patents and research publications. By doing so, these tools can identify novel drug targets and generate innovative therapeutic concepts. For effective drug development, models can be trained on extensive volumes of scientific data, ensuring that the ensuing predictions or recommendations are rooted in comprehensive research.
Furthermore, AI’s applications aren’t just limited to drug discovery and design. It’s making tangible contributions in drug screening as well. Numerous algorithms, such as extreme learning machines, deep neural networks (DNNs), random forests (RF), support vector machines (SVMs), and nearest-neighbour classifiers, are now at the forefront of virtual screening. These are employed based on their synthesis viability and their capacity to predict in vivo toxicity and activity, thereby ensuring that potential drug candidates are both effective and safe.
The proliferation of AI in various sectors has brought along with it a range of ethical and social concerns that intersect with broader questions about technology, data usage, and automation. Central among these concerns is the question of accountability. As AI systems become more integrated into decision-making processes, especially in sensitive areas like healthcare, who is held accountable when things go wrong? The possibility of AI systems making flawed decisions, often due to intrinsic biases in the datasets they are trained on, can lead to catastrophic outcomes. An illustration of such a flaw was observed in an AI application that misjudged pneumonia-related complications and potentially jeopardised patients’ health. These erroneous decisions, often opaque in nature due to the intricate inner workings of machine learning algorithms, further fuel concerns about transparency and accountability.
Transparency, or the lack thereof, in AI systems poses its own set of challenges. As machine learning models continually refine and recalibrate their parameters, understanding their decision-making process becomes elusive. This obfuscation often referred to as the ‘black-box’ phenomenon, hampers trust and understanding. The branch of AI research known as “Explainable Artificial Intelligence (XAI)” attempts to remedy this by making the decision-making processes of AI models understandable to humans. Through XAI, healthcare professionals and patients can glean insights into the rationale behind diagnostic decisions made by AI systems. Furthermore, this enhances the trust quotient, as evidenced by studies that underscore the importance of visual feedback in fostering trust in AI models.
Another prominent concern is the potential reinforcement of existing societal biases. AI systems, trained on historically accumulated data, can inadvertently perpetuate and even amplify biases present in the data, leading to skewed and unjust outcomes. This is particularly alarming in healthcare, where decisions can be a matter of life and death. This threat is further compounded by data privacy and security issues. AI systems that process sensitive patient information become prime targets for cyberattacks, risking unauthorised access or tampering of data, with motives ranging from financial gain to malicious intent.
The rapid integration of AI technologies in healthcare underscores the need for robust governance. Proper governance structures ensure that regulatory, ethical, and trust-related challenges are proactively addressed, thereby fostering confidence and optimising health outcomes. On an international level, regulatory measures are being established to guide the application of AI in domains requiring stringent oversight, such as healthcare. The European Union, for instance, introduced the GDPR in 2018, setting forth data protection standards. More recently, the European Commission proposed the Artificial Intelligence Act (AIA), a regulatory framework designed to ensure the responsible adoption of AI technologies, mandating rigorous assessments for high-risk AI systems.
From a technical standpoint, there are further substantial challenges to surmount. For AI to be practically beneficial in healthcare settings, it needs to be user-friendly for healthcare professionals (HCPs). The technical intricacies involved in setting up and maintaining AI infrastructure, along with concerns of data storage and validity, often act as deterrents. AI models, while potent, are not infallible. They can manifest shortcomings, such as biases or a susceptibility to being easily misled. It is, therefore, imperative for healthcare providers to strategise effectively for the seamless implementation of AI systems, addressing costs, infrastructure needs, and training requirements for HCPs.
The perceived opaqueness of AI-driven clinical decision support systems often makes HCPs sceptical. This, combined with concerns about the potential risks associated with AI, acts as a barrier to its widespread adoption. It is thus imperative to emphasise solutions like XAI to bolster trust and overcome the hesitancy surrounding AI adoption. Furthermore, integrating AI training into medical curricula can go a long way in ensuring its safe and informed usage in the future. Addressing these challenges head-on, in tandem with fostering a collaborative environment involving all stakeholders, will be pivotal for the responsible and effective proliferation of AI in healthcare. Recent events, such as the COVID-19 pandemic and its global implications alongside the Ukraine war, underline the pressing need for transformative technologies like AI, especially when health systems are stretched thin.
Given these advancements, it is pivotal however to scrutinise the sources of this information. Although formal conflicts of interest should be declared in publications, authors may have subconscious biases, for and against, the implementation of AI in healthcare, which may influence the authors’ interpretations of the data. Discussions are inevitable regarding published research, particularly since the concept of ‘false positive findings’ came to the forefront in 2005 in a review by John Ioannidis (“Why Most Published Research Findings Are False”). The observation that journals are biased in publishing more papers that have positive rather than negative findings both skews the total body of the evidence and underscores the need for studies to be accurate, representative, and negligibly biased. When dealing with AI, where the risks are substantial, relying solely on justifiable scientific evidence becomes imperative. Studies that are used for the implementation of AI systems should be well mediated by a neutral and independent third party to ensure that any advancements in AI system implementations are based solely on justified scientific evidence, and not on personal opinions, commercial interests or political views.
The evidence reviewed undeniably points to the potential of AI in healthcare. There is no doubt that there is real benefit in a wide range of areas. AI can enable services to be run more efficiently, allow selection of patients who are most likely to benefit from a treatment, boost the development of drugs, and accurately recognise, diagnose, and treat diseases and conditions.
However, with these advancements come challenges. We identified some key areas of risk: the creation of good quality big data and the importance of consent; the data risks such as bias and poor data quality; the issue of a black box (lack of transparency of algorithms); data poisoning; and data security. Workforce issues were also identified: how AI works with the current workforce and the fear of workforce replacement; the risk of de-skilling; and the need for education and training, and embedding change. It was also identified that there is a current need for research into use, cost-effectiveness, and long-term outcomes of AI systems. There will always be a risk of bias, error chance statistical improbabilities, in research and published studies fundamentally due to the nature of science itself. Yet, the aim is to have a body of evidence that helps create a consensus of opinion.
In summary, the transformative power of AI in the healthcare sector is unequivocal, offering advancements that have the potential to reshape patient care, diagnostics, drug development, and a myriad of other domains. These innovations, while promising, come hand in hand with significant ethical, social, and technical challenges that require careful navigation. The dual-edged sword of AI’s potential brings to light the importance of transparency, ethical considerations, and robust governance in its application. Equally paramount is the need for rigorous scientific evaluation, with an emphasis on neutrality and comprehensive evidence to ensure AI’s benefits are realised without compromising patient safety and care quality. As the healthcare landscape continues to evolve, it becomes imperative for stakeholders to strike a balance between leveraging AI’s revolutionary capabilities and addressing its inherent challenges, all while placing the well-being of patients at the forefront.
This CreateAnEssay4U special edition brings together the work of previous essays and provides a comprehensive overview of an important technological area of study. For source information, see also:
Disability is an integral part of the human experience, manifesting in myriad forms and affecting countless lives across the globe. A staggering 1 billion individuals worldwide, approximately 15% of the global populace, live with some form of disability. This ratio swells even more in nations such as the US and the UK, where nearly one in four of the population identify as having a disability. Such figures are not merely statistics on paper; they represent people—people with stories, dreams, challenges, and desires much like everyone else. Yet, despite their significant presence, the pervasive spectre of discrimination continues to cast a long shadow over the lives of the disabled, subjecting them to prejudices and biases, both overt and insidious. Among the various manifestations of this discrimination, ‘ableism’ stands out—a term that encapsulates the range of prejudices against those with disabilities. While many understand ableism in its overt forms, such as physical violence or blatant derogatory comments, a subtler, yet equally harmful form of ableism lurks in the language we use daily. Words, often casually employed in conversations, can unwittingly perpetuate negative stereotypes and further entrench divisive beliefs about disabilities in society’s collective psyche. This essay delves into the depths of ableism, especially as manifested in language, and unpacks its ramifications, drawing insights from Jim Ferris’ poignant “Poems with Disabilities” and the voices of advocates and experts from around the world.
Ableism’s insidious nature often lies in subtle linguistic micro-aggressions, deeply entrenched in our culture and passed down through generations. In our everyday conversations, words and phrases that might seem innocuous, perhaps even colloquial or humorous to some, reinforce negative perceptions about disability. These linguistic choices, born from ignorance or carelessness, permeate popular culture, music, films, and even literature, amplifying their reach and influence. Terms such as “dumb” or “lame” or exclamatory remarks like “I’m so OCD!” trivialise and misrepresent the lived experiences of disabled individuals. They not only diminish the complexities and nuances of their challenges but also perpetuate stereotypes that further alienate and marginalise them. Jamie Hale, a disability advocate and leader of a UK charity run for people with neuromuscular conditions, underscores the gravity of this issue. Hale emphasises that such language, even if not directed at someone with a disability, perpetuates the harmful and limiting notion that being disabled equates to being less than or undesirable. This, in turn, serves to bolster broader systemic biases and barriers faced by the disabled community.
Language, as a reflection and influencer of cultural norms and values, plays a pivotal role in shaping societal perspectives. Every word we choose, every phrase we employ, has the power to either challenge or reinforce prevailing biases. When disability is used metaphorically in colloquial speech and writing, it not only perpetuates stereotypes but also distorts meaning, further exacerbating misunderstandings about disabilities. For instance, the idiom “fall on deaf ears” is not just a benign expression but a problematic one. While its intended meaning is to convey an intentional disregard or wilful ignorance, equating such an act with deafness—a non-choice, involuntary condition—is a misrepresentation. Such expressions shift the blame from an individual actively choosing to neglect or disregard information to an involuntary state, creating a dichotomy where the disabled community is unjustly represented. Over time, and through constant repetition, these idioms and metaphors can embed harmful biases in societal consciousness, perpetuating misconceptions and fostering environments where prejudice can thrive.
Hale’s observations shed light on the deeper issues surrounding our choice of words. He underscores that disability metaphors not only spread misconceptions but further bolster negative outlooks and inadvertently sustain systems of oppression. Such metaphorical usage acts as a double-edged sword, simultaneously misrepresenting the disabled community and perpetuating biases against them. So, faced with the question of why ableist language persists, especially when its damaging effects are apparent, one potential explanation could be the mimetic nature of language acquisition. From childhood, individuals learn language patterns based on repetition and mimicry, often adopting phrases and expressions from family, peers, media, and broader societal interactions. People often parrot what they hear without necessarily delving into the deeper meanings or origins of those expressions. However, the widespread adoption of slang and these potentially harmful idioms might also suggest underlying biases embedded within the collective psyche. It’s a telling sign that while many remain blissfully unaware or dismissive of the weight such words carry, the deaf and disabled communities have long been in dialogue about these issues. The pejorative connotations of terms like “dumb”, once referring to those unable to speak and now synonymous with stupidity, have been discussed and debated for ages within these communities. The persistence of these terms in modern lexicon, despite these discussions, highlights the urgent need for broader societal introspection and education.
Rosa Lee Timm, an ardent advocate for the rights of the deaf, underscores a significant hurdle in addressing ableism: the fact that these pivotal conversations about the impact of ableist language often remain marginalised or on the fringes of mainstream discourse. The primary reason, she posits, is that the majority, being comfortably nestled in their privileges, operates under the misguided belief that ableism doesn’t directly impact them, thereby absolving themselves of responsibility or involvement. This bystander attitude, characterised by indifference or neglect, not only perpetuates harmful stereotypes but further widens the chasm between the disabled and non-disabled communities, fostering an environment of exclusion and misunderstanding.
Yet, an overlooked consequence of such pervasive ableist language is its potential to boomerang back on those who may have once considered themselves immune to its effects. As life is unpredictable, non-disabled individuals might later find themselves grappling with disabilities due to accidents, illnesses, or the natural aging process. When they confront this new reality, the same ableist language they once casually used or ignored becomes a mirror reflecting the restrictive and unrealistic standards they have internalised over time. These internalised beliefs can exacerbate feelings of inadequacy, alienation, or depression. In this sense, ableist language doesn’t just harm its immediate targets; it has a more insidious, broader impact, shaping societal attitudes and norms in ways that can be psychologically damaging to everyone within that society.
Countering ableism is a call to action that transcends mere advocacy for the disabled community; it’s a transformative pursuit towards creating a society that values, respects, and includes all its members irrespective of their physical or cognitive abilities. Jamie Hale, with deep insight into the matter, aptly emphasises that the overarching mission to dismantle entrenched ableist structures might not exclusively start with the reformation of language, but language remains a powerful and indispensable tool in this crusade. Words, after all, are not just mere utterances; they are carriers of culture, history, values, and beliefs. By being more mindful of our lexicon, consciously opting for straightforward and respectful expressions, and actively eschewing ableist euphemisms and colloquialisms, we’re not just altering sentences but reshaping societal mindsets. This isn’t merely a gesture of solidarity; it’s a foundational step towards creating a society where everyone feels seen, valued, and heard, thereby fostering a truly inclusive and empathetic environment.
The early 21st century marked a turning point in the discourse surrounding disability poetry. As with all forms of art and expression, disability poetry sought to capture the vast spectrum of human experience, and in 2004, Jim Ferris pioneered the theoretical framing of what could be labelled as ‘disability poetry.’ His essay “The Enjambed Body: A Step Toward a Cripple Poetics” was groundbreaking, not only for its recognition of an emerging genre but for its bold stance on redefining societal norms. His subsequent publication, “The Hospital Poems,” provided tangible evidence of these concepts, seamlessly merging the abstract with the palpable. It was a profound call to poets to reimagine, restructure, and reclaim narratives around disability.
But while the inception of “Crip Poetry” in 2006 offered an initial blueprint for disability poetry, it was only the beginning of a broader conversation. The essence of poetry is evolution, and as with all art forms, disability poetry has witnessed a continuous transformation. At the Disability Poetics Symposium at the University of Pennsylvania, this evolution was evident. The ideas and characteristics Ferris had set down years before were still revered, but they had also expanded, fragmented, and reformed, allowing a multitude of interpretations and styles to emerge.
For poets in the world of disability, “Crip Poetry” was not just a reference point, but a challenge—a beacon that prompted them to revisit, redefine, and reshape their understanding of disability. Contemporary poets’ reflections on Ferris’s essay are a testament to its ripple effect across the literary world. Avra Wing’s response epitomises the essence of this evolution. As a facilitator at the NYWC Workshop at the CIDNY, Wing’s insights reflect the diverse experiences that comprise the disability narrative.
Wing’s perspective, especially as someone who encountered disability later in life, adds a poignant dimension to the discourse. While Ferris champions the value found within the disability experience, Wing highlights the journey of reconciliation and acceptance that many face. The juxtaposition of loss against empowerment, of grief against growth, brings forth the complex tapestry of emotions that disability can entail. For poets like Wing, their craft becomes a medium to navigate, understand, and ultimately, redefine their identity in the face of disability. As she rightly points out, there isn’t a single narrative of disability; instead, there’s a vast, intricate mosaic of stories, each distinct, each valuable.
In this evolving landscape of disability poetics, poets are continually pushing boundaries, challenging conventions, and adding layers of depth to the genre. From Ferris’s pioneering framework to Wing’s introspective reflections, the world of disability poetry showcases the resilience, diversity, and depth of the human spirit. Through their words, these poets remind us that while physical or cognitive abilities might differ, the quest for understanding, expression, and identity remains a universal journey.
In his evocative collection of 2011, “Slouching Toward Guantanamo,” Jim Ferris crafts a symphony of voices that resonates with authenticity and challenges the status quo. Drawing inspiration from the rich, expansive verse of Whitman, Ferris combines elements of charm, irony, and poignant reflection to present a fresh perspective on the lived experience of disability. With lines such as, “This is my body/Look if you like,” Ferris audaciously confronts the reader, pushing them to reconsider their preconceived notions. His fusion of satire and genuine insight is evident in the lines, “I’m sorry–this space is reserved/for poems with disabilities,” a clever nod to the socio-political aspects of disability rights. His works, reminiscent of Roberts’ poetry, are deeply rooted in the physicality of existence, emphasizing the beauty and complexity of varied bodily experiences. “Glory be to God for crippled things–/…Growths that thrive and work left incomplete;/All legs get tired, all clocks get their hands stuck,” writes Ferris, showcasing his adeptness in melding lyrical elegance with profound themes of acceptance and celebration. Through this collection, Ferris not only confronts societal discomfort around disability but also revels in the uniqueness of every body, heralding its differences with grace and verve.
In conclusion, the intricate interplay between disability, language, and poetry offers a profound lens into the human condition. The omnipresence of disability, as demonstrated by staggering global statistics, emphasises its universality, yet its experience remains diverse and multifaceted. As this essay has highlighted, our everyday language, rife with subtle ableisms, can unintentionally perpetuate negative stereotypes about disabilities. It’s a poignant reminder of the weight our words carry and the profound impact they can have on shaping societal attitudes and values. Poetry, especially disability poetry, emerges as a potent medium to challenge these ingrained beliefs, offering both a reflection on and a reimagining of the disability experience. The works of pioneers like Jim Ferris and contemporary voices like Avra Wing emphasise the dynamic nature of this literary genre, one that remains ever-evolving, much like society’s perceptions of disability itself. By confronting our inherent biases and embracing the richness of diverse narratives, we move closer to a more inclusive, empathetic, and understanding society—one where every individual’s story holds value and where differences are celebrated as part of our collective human tapestry.
The COVID-19 pandemic has hit disadvantaged communities particularly hard, worsened by systemic racism, marginalisation, and structural inequality, leading to adverse health outcomes. These communities have faced increased economic instability, higher disease exposure, and more severe infections and deaths. In the realm of health informatics, AI technologies play a crucial role. However, inherent biases in their algorithms can unintentionally amplify these existing inequalities. This issue is of significant concern in managing COVID-19, where biased AI models might adversely affect underrepresented groups in training datasets, or deepen health disparities in clinical decision-making.
Health inequalities in AI systems are often due to issues like unrepresentative training data and development biases. Vulnerable groups frequently are underrepresented, stemming from limited healthcare access or biases in data collection. This results in AI systems that are not as effective for these groups. Furthermore, failing to include important demographic variables in AI models can lead to unequal performance across different subgroups, disproportionately affecting vulnerable populations. This highlights the importance of creating AI systems in healthcare that are inclusive and equitable. Addressing biases and ensuring fair use of AI is vital to reduce health inequalities, particularly during the pandemic.
In healthcare, AI technologies depend heavily on large datasets for their algorithms. Yet, these datasets can carry biases from existing practices and institutional norms. Consequently, AI models developed with these biased datasets often replicate existing inequities. In clinical and public health settings, a range of factors contribute to biases in AI systems. Biased judgment and decision-making, discriminatory healthcare processes, policies, and governance can influence various sources of data, including electronic health records, clinical notes, training curriculums, clinical trials, academic studies, and public health monitoring records. For example, during clinical decision-making, established biases against marginalised groups, such as African American and LGBTQ+ patients, can influence the clinical notes taken by healthcare workers.
Currently, natural language processing AI, which involves reading written text and interpreting it for coding, can also be a source of unconscious biases in AI systems. It can process these medical documents and then code them as data. This data is then used to make inferences from large datasets. If an AI system interprets medical notes where there are well-established human biases, such as the disproportionate recording of particular questions towards African American or LGBTQ+ patients, the AI could then generate a link between these characteristics. Consequently, these real-world biases will be silently reinforced and multiplied in the AI system, potentially leading to systematic racial and homophobic biases.
When these notes, often in free text, are used by natural language processing technologies to identify symptom profiles or phenotypic characteristics, the biases inherent in them are also likely to be transferred into the AI systems. This cycle of bias from human to machine perpetuates and amplifies discriminatory patterns within AI applications in healthcare: if, during development, an AI model learns a poor approximation of the true relationship between data, it is likely that at least some of the outputted results will be incorrect. This issue is known as poor ‘Predictive Accuracy’. Additionally, biases originating from the raw data itself present another challenge. Even with access to sufficient ‘Big Data’ during development, the algorithm’s use could lead to clinical errors.
These datasets, forming the foundation of data-driven AI and machine learning models, reflect complex and historically situated practices, norms, and attitudes. As a result, AI models used for diagnosis or prognosis might incorporate biases from previous inequitable practices. Using models trained on these datasets could reinforce or amplify discriminatory structures.
The risk of such discrimination is particularly acute during the COVID-19 pandemic. Hospitals are increasingly using natural language processing technologies to extract diagnostic information from various clinical documents. As these technologies are adapted for identifying symptoms of SARS-CoV-2 infection, the potential for embedding inequality in these AI models increases. If human biases are recorded in clinical notes, these discriminatory patterns are likely to infiltrate the AI models based on them. Moreover, if these models are trained on electronic health records that are unrepresentative or incomplete, reflecting disparities in healthcare access and quality, the resulting AI systems will likely perpetuate and compound pre-existing structural discrimination. This situation highlights the need for more careful and unbiased data collection and model training to ensure AI technologies in healthcare do not exacerbate existing inequalities. It is important to be aware of the potential for data biases, so that during the development of AI systems, the risks can be mitigated, and monitoring can take place to ensure that AI systems do not create biased outputs.
The datasets used to train, test, and validate Artificial Intelligence (AI) models often do not adequately represent the general public. This is especially evident in healthcare, where datasets like electronic health records, genome databases, and biobanks frequently miss out on capturing data from those who have sporadic or limited access to healthcare. This often includes minority ethnic groups, immigrants, and socioeconomically disadvantaged individuals. The issue of representativeness is further compounded by the increased reliance on digital technologies for health monitoring, such as smartphones and symptom tracking apps. In the UK, for example, a significant portion of the population lacks essential digital skills, and a notable percentage do not own smartphones. This digital divide means that datasets derived from mobile technologies and social media may not include or may under-represent individuals without digital access. If an AI system is trained on a specific data set, and then the algorithm is applied to a data set with a slightly varied relative distribution of qualitative data (skin colour, ethnicity, age etc), it can result in a phenomenon called ‘Dataset Shift’, which in turn can lead to erroneous results. A recent study (Yan et al., 2020) found that dataset shift can even be caused by scans from MRI machines with different manufacturers.
Biased datasets in biomedical AI, like those combining pervasive sensing with electronic health records from certain hospitals, exacerbate unrepresentativeness. This becomes critical when disease prevalence and risk factors, which vary across populations, are not adequately represented, leading to AI models with lower sensitivity and underdetection of conditions.
The COVID-19 pandemic highlights this issue, with health data silos in wealthier areas creating biased datasets that, if used for AI training, result in unfair outcomes. Such biases are further compounded by institutional racism and implicit biases in AI development, affecting design choices and leading to discriminatory health-related AI outcomes. This is particularly evident during the pandemic, where urgent solution-seeking and top-down decision-making amplify disparities.
In data handling, errors and improper consolidation can introduce biases against disadvantaged groups. Important design choices, such as including personal data like ethnicity, can significantly affect AI performance for these groups, sometimes embedding structural racism in clinical tools. For instance, consider an AI system developed in the US using a large volume of suitable Big Data and proven to be highly accurate for US patients. If this same system is employed in a UK hospital for patient diagnosis, it might result in a higher rate of misdiagnoses and errors for UK patients. This discrepancy could arise from differences between the AI’s training data and the data it processes subsequently. The training data from the US may include diverse groups of people with variations in ethnicity, race, age, sex, socioeconomic, and environmental factors, influencing the algorithm’s correlations. However, when this system is applied to the UK population, which has its own unique diversity, the AI might deliver less accurate results due to algorithmic bias. This inaccuracy could disproportionately affect certain minority groups in the UK, stemming from a mismatch between the representation in the AI’s training data sets and the data it is later used on.
These examples emphasise the importance of cautious and responsible AI implementation, particularly in critical public health scenarios. It is essential to balance innovative AI development with a keen awareness of health inequities and potential biases. Developing AI safely in healthcare requires a comprehensive approach that encompasses bias mitigation, clinical expertise, community involvement, and an understanding of social determinants such as race and socioeconomic status. Stakeholders must be vigilant about AI biases, utilising public health and ethical frameworks to verify the appropriateness and safety of AI applications. Policymaking in this realm should be inclusive, engaging all stakeholders and prioritising informed consent. Overall, addressing systemic racism and structural inequities is fundamental to ensuring that AI contributes to reducing inequalities rather than perpetuating them.
Links
Yan, W., Huang, L., Xia, L., Gu, S., Yan, F., Wang, Y., & Tao, Q. (2020). MRI Manufacturer Shift and Adaptation: Increasing the Generalizability of Deep Learning Segmentation for MR Images Acquired with Different Scanners. Radiology. Artificial intelligence, 2(4), e190195. https://doi.org/10.1148/ryai.2020190195
The advent of the 21st century has brought with it technological innovations that are rapidly changing the face of policing. One such groundbreaking technology is artificial intelligence (AI).
In the United Kingdom, the complexities of implementing AI-driven predictive policing models have been evident in the experiences of the Durham Constabulary. Their ambition was to craft an algorithm that could more accurately gauge the risk a potential offender might pose, guiding the police force in their bail decisions. However, it became evident that the algorithm had a potentially discriminatory bias against impoverished individuals.
The core of the issue lies in the data points chosen. One might believe these data-driven approaches are neutral, using pure, objective information to make decisions. But Durham Constabulary’s inclusion of postcodes as a data determinant raised eyebrows. Using postcodes was found to reinforce negative stereotypes associated with certain neighbourhoods, indirectly causing repercussions like increasing house insurance premiums and reducing property values for everyone in those areas. This revelation prompted the removal of postcode data from the algorithm.
Yet, despite these modifications, inherent issues with data-driven justice persist. Bernard Harcourt, a prominent US law professor, describes a phenomenon termed “the ratchet effect.” As police increasingly rely on AI predictions, individuals and communities that have previously been on their radar continue to be heavily scrutinised. This spirals into these individuals being profiled even more, leading to the identification of more offences within these groups. On the flip side, those who haven’t been heavily surveilled by the police continue their offences undetected, escaping this “ratchet effect.” A clear example of this is the disparity between a street drug user and a middle-class professional procuring drugs online. The former becomes more ensnared in this feedback loop, while the latter largely escapes scrutiny.
The allure of “big data” in policing is undeniable. The potential benefits are substantial. Police resources, increasingly limited, can be more strategically deployed. Bail decisions can be streamlined to ensure only high-risk individuals are incarcerated before trial. The allure rests in proactive rather than reactive policing – addressing crimes before they even occur, thereby saving both resources and the immeasurable societal costs associated with offences.
With advancements in technology, police forces worldwide are leaning heavily into this predictive model. In the US, intricate datasets, considering factors ranging from local weather patterns to social media activity, are used to anticipate potential crime hotspots. Some cities employ acoustic sensors, hidden throughout urban landscapes, to detect and predict gunfire based on background noises.
Recently, New Zealand police have integrated AI tools like SearchX to enhance their tactics. This tool, developed in light of rising gun violence and tragic events such as the demise of police constable Matthew Hunt, is pivotal in instantly drawing connections between suspects, locations, and other risk-related factors, emphasising officer safety. However, the application of such tools is bringing forth serious questions concerning individual privacy, technological bias, and the adequacy of existing legal safeguards.
Given the clandestine nature of many of these AI programmes, New Zealanders possess only a fragmented understanding of the extent to which the police are leveraging these technologies. Cellebrite, a prominent tool that extracts personal data from smartphones and accesses a broad range of social media platforms, and BriefCam, a tool that synthesises video footage, including facial recognition and vehicle licence plates, are both known to be in use. With tools like BriefCam, the police have managed to exponentially speed up the process of analysing CCTV footage. Still, the deployment of certain tools, like Clearview AI, without the necessary approvals underscores the overarching concerns around transparency.
The major allure of AI in policing is its touted capability to foresee and forestall criminal activities. Yet, the utilisation of tools like Cellebrite and BriefCam comes with the palpable erosion of personal privacy. Current legislation, such as The Privacy Act 2020, permits police to gather and scrutinise personal data, sometimes without the knowledge or consent of the individuals in question.
Moreover, AI tools are not exempt from flaws. Their decisions are often influenced by the data they’re trained on, which can contain biases from historical practices and societal prejudices. Often, there’s a predisposition to place unwarranted trust in AI-driven decisions. For instance, in some US cities, AI-driven tools have mistakenly predicted higher crime rates in predominantly African-American neighbourhoods, reflecting historical biases rather than real-time threats. Even when they might be less accurate than human judgement, the allure of seemingly objective technology can override caution. This over-reliance can inadvertently spotlight certain individuals, like an innocent individual repeatedly misidentified due to algorithmic errors, sidelining other potential suspects.
Furthermore, biased algorithms have been observed to disproportionately affect the economically disadvantaged and ethnic minorities. A study from MIT Media Lab revealed that certain facial recognition software had higher error rates for darker-skinned individuals, leading to potential misidentifications. The use of AI in predictive policing, if informed by data from heavily surveilled neighbourhoods, can perpetuate existing prejudices. For example, if a neighbourhood with a high immigrant population is over-policed due to historical biases, AI might predict higher crime rates there purely based on past data, rather than the current situation. Such skewed data guides more police attention to these areas, further intensifying the disparities, creating a vicious cycle where over-policing results in more recorded incidents, which in turn results in even more policing.
Locally, concerns around transparency, privacy, and the handling of “dirty data”—information already tinged with human biases—have been raised in the context of the New Zealand government’s AI usage. Unfortunately, a legal structure tailored to the policing applications of AI is non-existent in New Zealand. While voluntary codes like the Australia New Zealand Police Artificial Intelligence Principles and the Algorithm Charter for Aotearoa New Zealand lay down ethical and operational guidelines, they fall short in establishing a robust, enforceable framework.
Despite the promise of constant AI system oversight and public channels for inquiry and challenges, there are evident lapses. The police’s nonchalance is evident from the absence of dedicated avenues for AI-related queries or concerns on their official website. With the police essentially overseeing themselves, coupled with the lack of an independent body to scrutinise their actions, the citizens are left in a precarious position.
As AI continues to gain momentum in the realm of governance, New Zealand, akin to its European counterparts, is confronted with the pressing need to introduce regulations. Such legal frameworks are paramount to ensuring that the police’s deployment of AI contributes constructively to society and doesn’t inadvertently exacerbate existing issues.
In conclusion, while the integration of AI into predictive policing offers an exciting frontier for enhancing law enforcement capabilities and efficiency, it is not without its challenges and ethical dilemmas. From the experiences of Durham Constabulary in the UK to the evolving landscape in New Zealand, the complexities of ensuring fairness, transparency, and privacy become evident. The juxtaposition of AI’s promise with its potential pitfalls underscores the imperative for stringent oversight, comprehensive legislation, and public engagement. As technology’s role in policing evolves, so must our approach to its governance, ensuring that in our quest for a safer society, we don’t inadvertently compromise the very values we aim to uphold.
The foundation of governance lays the groundwork for the realisation of human rights and equality. However, the type of governance in place can dramatically impact how these rights and equality are achieved or inhibited. A striking difference is seen when contrasting the concepts of democracy and theocracy. These two forms of government stand at opposing ends, especially when we evaluate their perspectives on human rights and equality.
Theocratical governments, at their core, are driven by religious ideologies, often promoting the idea that certain individuals are divinely entitled to power, privilege, and resource distribution. Such a belief system inherently creates inequality. In theocracies, the governing ethos is deeply embedded in religious teachings and practices, which significantly influence societal norms and values. Consequently, the inability of individuals to freely choose or change their religious affiliation under a theocratic regime can lead to their marginalisation and violation of their basic human rights.
Democracies, on the other hand, emphasise secular independence, a principle that underscores that laws should be justifiable without the need for religious reasons. Such a distinction not only allows freedom of religious belief but also ensures that no group is marginalised or discriminated against solely based on religious precepts.
Afghanistan’s treatment of women under its theocratic regime is a glaring example of how theocracy can breed inequality. Under the umbrella of religious governance, women in Afghanistan have faced extreme oppression. The nation’s alarming distinction as one of the most challenging places for women is a testament to the theocracy’s failures in ensuring equal rights. The pervasive cultural and religious norms deeply entrenched in society have perpetuated this gender inequality. This imbalance is not just symbolic but has concrete ramifications. From a lack of representation in leadership roles and higher education to systematic violence, financial dependence, and curtailed decision-making rights, women in Afghanistan face daily challenges that reflect the inherent flaws of a theocratic system.
The discrimination faced by the Muhamasheen community in Yemen further emphasises the detrimental impact of theocratical governance on minority rights. This marginalised Black community is often subjected to violence, including gender-based violence, perpetuated by deep-rooted racial and religious prejudice. Their social standing, akin to servants, further illustrates the state-endorsed discrimination they face, making it challenging to access essential services like education, healthcare, housing, and dignified work. The systemic discrimination against Muhamasheen, driven by Yemen’s lineage-based theocratical system, paints a grim picture of life for minorities under religious rule.
The root of the issue with theocratic governments lies in their foundational principle that merges state and religion, thereby making religious decrees synonymous with state laws. This blend of spiritual beliefs and legal frameworks can be problematic, as it tends to suppress diverse views or beliefs that deviate from the established religious doctrines. As such, the theocratic system might inadvertently promote intolerance and prejudice against any group that doesn’t adhere strictly to the sanctioned beliefs, resulting in systemic discrimination.
Furthermore, another troubling facet of theocratic governance is its lack of a transparent system of checks and balances. In many democratic systems, the separation of powers ensures that no single entity gains unchecked authority. However, in theocracies, religious leaders or their representatives wield significant influence over both spiritual and secular aspects of life. This concentration of power can often lead to autocratic tendencies, further marginalising minority groups and stifling voices of dissent. As there is an inherent belief that these leaders are divinely appointed or inspired, challenging their decisions becomes not just a political or social risk but also a religious transgression.
Moreover, while religion can provide a moral compass and a sense of purpose, making it the sole basis for governance often results in the exclusion of scientific, economic, and sociological perspectives. Decisions driven purely by religious tenets might not always align with modern understandings of human rights, economic realities, or global diplomatic dynamics. By sidelining pragmatic and rational deliberations in favor of religious dogma, theocratic governments might inadvertently hinder progress, development, and adaptability. Such an approach can further exacerbate inequalities, particularly if religious interpretations remain rigid and unyielding in the face of evolving societal needs and global challenges.
In conclusion, the consequences of theocratic governance on marginalised communities are palpable and deeply troubling. From the plight of Afghan women to the struggles of the Muhamasheen in Yemen, it is evident that theocracy amplifies inequality. If societies genuinely aim for the realisation of human rights for all, theocratic principles that propagate discrimination, suppression, and denial of basic rights need to be reevaluated and replaced with more inclusive and equitable governance systems.
Create An Essay4U 