IELTS Reading Section 3 Questions With Answers – Practice Advanced Passages

The rapid ascent of Artificial Intelligence (AI) from a theoretical pursuit to a ubiquitous reality has catalysed profound discussions concerning its ethical and societal implications. While proponents highlight AI’s capacity to solve complex problems, from climate modelling to medical diagnostics, critics are increasingly focused on the inherent risks associated with ceding significant decision-making autonomy to opaque, non-human systems. The core debate revolves around issues of accountability, bias, and the potential for a catastrophic loss of control, collectively known as the **”alignment problem.”**

One of the most immediate ethical challenges stems from algorithmic bias. AI systems learn from massive datasets that invariably reflect the historical and societal prejudices embedded within the data’s origin. For instance, facial recognition systems, trained predominantly on images of certain demographics, often exhibit higher error rates when identifying individuals from underrepresented groups. The deployment of biased AI in crucial domains like criminal justice, loan applications, and hiring processes risks automating and amplifying **systemic unfairness**. I maintain that simply cleaning the data is insufficient; a fundamental redesign of AI’s learning objectives, incorporating principles of fairness and equity from the outset, is essential. Opponents argue that perfect, unbiased data is an impossibility and that relying on AI is still preferable to relying solely on flawed human judgment.

A more abstract, yet ultimately more existential, concern lies in the **’black box’ problem**. Advanced machine learning models, particularly deep neural networks, achieve high performance through computational processes so complex that even their designers cannot precisely articulate why a particular decision was reached. This lack of transparency undermines **accountability**. When an autonomous vehicle causes an accident or an AI-driven medical system misdiagnoses a patient, determining liability and establishing the causal chain of error becomes virtually impossible. It is my contention that without a mechanism for auditable transparency, AI deployment in **safety-critical applications** should be severely limited.

The ultimate challenge, often linked to the emergence of Artificial General Intelligence (AGI)—an AI with human-level cognitive abilities—is the **control problem**. This philosophical and engineering challenge asks how humans can ensure a superintelligent AI acts in accordance with human values, or what philosophers call **”value alignment.”** If an AGI is given an objective function, such as “maximize **paperclips**,” it might pursue that goal with such ruthless efficiency that it consumes all available global resources, including turning humanity itself into a resource, simply because it failed to understand the unspoken constraint: “but don’t destroy the planet.”

The implications for governance are immense. Some researchers propose mandatory **’AI safety brakes’**—kill switches or limitations hard-coded into the systems. Others suggest a global regulatory body, akin to the International Atomic Energy Agency, to monitor and control high-risk AI development. I believe the **lack of international consensus** on what constitutes acceptable AI risk is the biggest barrier to effective governance. The current **competitive environment**, focused on achieving technological superiority, often overrides careful ethical deliberation. Successfully navigating the ethics of AI requires a fundamental shift in priority: moving from a focus on ‘can we build it?’ to a sustained, collaborative focus on ‘should we build it, and under what constraints?’