Đề thi thử số 19 — Reading 40 câu (có bài đọc)

As artificial intelligence becomes increasingly integrated into our daily lives, the ethical questions it raises have moved from academic debate to urgent practical concern. AI systems now determine who gets hired, who receives loans, what news people see, how law enforcement allocates resources, and even who gets medical treatment. The decisions made by these systems can profoundly affect individual lives and shape society as a whole. Yet the development and deployment of AI often outpace our ability to understand its implications, let alone regulate it effectively.

One of the most fundamental ethical concerns is algorithmic bias. AI systems learn from data, and if that data reflects historical inequalities or societal prejudices, the AI will reproduce and often amplify those biases. There are now well-documented cases of hiring tools that discriminated against women, facial recognition systems that performed poorly on people with darker skin, and predictive policing algorithms that disproportionately targeted minority neighborhoods. These are not edge cases—they reflect systemic problems with how AI systems are designed, trained, and deployed.

Transparency and explainability are also major ethical concerns. Many advanced AI systems, particularly those based on deep learning, function as "black boxes" that produce outputs without providing clear explanations of how they arrived at their decisions. This is problematic in high-stakes domains: if a loan application is denied, a person deserves to know why. If a medical diagnosis is made by an AI, doctors need to understand its reasoning. The European Union's General Data Protection Regulation has established a "right to explanation" for automated decisions, but implementing this in practice remains technically challenging.

Beyond bias and transparency, AI raises profound questions about privacy, accountability, and the future of work. AI systems often require vast amounts of data to function, raising concerns about surveillance and the commodification of personal information. When AI systems cause harm, it is often unclear who should be held responsible—the developer, the deployer, the user, or the AI itself. As AI becomes more capable, questions about its impact on employment, inequality, and human autonomy become increasingly urgent. Addressing these ethical challenges will require not just technical solutions but also thoughtful policy, robust public dialogue, and a commitment to ensuring that the benefits of AI are broadly shared rather than concentrated among a few.

The release of ChatGPT in November 2022 marked a watershed moment in the history of artificial intelligence, bringing generative AI into the mainstream consciousness almost overnight. Within just two months of its launch, ChatGPT had attracted 100 million users, making it the fastest-growing consumer application in history. The technology behind this revolution—large language models trained on massive amounts of text data—had been developing for years, but the accessible chat interface made its power suddenly available to anyone with an internet connection.

Generative AI encompasses a broad category of systems that can create new content, including text, images, audio, video, and even computer code. While ChatGPT brought text generation to public attention, similar breakthroughs were happening in other domains. Image generation systems like DALL-E, Midjourney, and Stable Diffusion can create stunning visuals from simple text descriptions. Music generation tools can compose original pieces in specific styles. Code generation tools like GitHub Copilot are transforming software development by suggesting code completions and even generating entire functions from natural language descriptions.

The implications of generative AI are being felt across industries. In education, teachers are grappling with how to adapt assignments and assessments in a world where students can generate essays in seconds. In journalism, news organizations are experimenting with AI-assisted reporting while grappling with concerns about accuracy and disclosure. In creative industries, artists and writers are debating the implications of AI trained on existing works, raising questions about copyright, originality, and the value of human creativity. In software development, AI coding assistants are boosting productivity but also raising concerns about code quality, security, and the future of programming as a profession.

The technology is not without limitations and risks. Generative AI systems can produce confident-sounding but false information—a problem sometimes called "hallucination." They can perpetuate biases present in their training data. They raise difficult questions about intellectual property, as many AI systems are trained on copyrighted works without explicit permission or compensation to the creators. The energy and computational resources required to train and run large AI models are also significant, raising environmental concerns. As generative AI continues to evolve at a rapid pace, governments, organizations, and individuals are grappling with how to harness its benefits while managing its risks—a challenge that will shape technology, society, and the economy for years to come.

Artificial intelligence is transforming healthcare in ways that were once the stuff of science fiction. AI systems are now assisting doctors in diagnosing diseases, discovering new drugs, personalizing treatment plans, and predicting patient outcomes with unprecedented accuracy. The potential benefits are enormous: faster diagnoses, more effective treatments, reduced healthcare costs, and—perhaps most importantly—access to expert-level medical knowledge in underserved areas around the world.

One of the most promising applications of AI in healthcare is in medical imaging. AI systems trained on millions of medical images can now detect signs of disease—tumors, fractures, diabetic retinopathy, and many other conditions—sometimes with accuracy comparable to or exceeding that of human specialists. Google's DeepMind developed an AI system that can detect over 50 eye diseases from retinal scans with accuracy matching world-leading experts. Similar systems are being developed for cancer detection in mammograms, lung nodules in CT scans, and stroke identification in brain imaging. These tools are not meant to replace radiologists but to augment their capabilities, helping them work faster and catch things they might miss.

AI is also accelerating drug discovery, one of the most time-consuming and expensive parts of pharmaceutical research. Traditional drug development can take a decade or more and cost over a billion dollars per successful drug. AI can analyze vast chemical libraries, predict how molecules will interact with biological targets, and identify promising drug candidates much faster than traditional methods. During the COVID-19 pandemic, AI tools helped identify potential treatments and accelerate vaccine development. While the technology is still maturing, AI-assisted drug discovery promises to bring new treatments to patients faster and more cheaply.

However, the integration of AI into healthcare also raises important concerns. Questions about accountability arise when AI systems make errors—should the developer, the hospital, or the doctor be held responsible? Patient data privacy is a major issue, as AI systems require access to vast amounts of sensitive health information. There are also concerns about bias—AI systems trained primarily on data from certain populations may perform poorly on underrepresented groups. The human element in medicine remains essential: empathy, ethical judgment, and the doctor-patient relationship cannot be replicated by algorithms. The most promising future for AI in healthcare is one in which AI augments and empowers healthcare professionals rather than replacing them, leading to better outcomes for patients and a more efficient healthcare system overall.

The dream of self-driving cars has captivated human imagination for decades, from science fiction visions to the futuristic highways depicted in films. Today, that dream is becoming a reality, albeit more gradually than some predicted. Companies like Waymo (a subsidiary of Google's parent company Alphabet), Tesla, Cruise, and others have invested billions of dollars in developing autonomous vehicle technology, and limited self-driving services are already operating in several cities around the world. The question is no longer whether autonomous vehicles will become a major part of transportation, but how quickly, and how society will navigate the complex technical, regulatory, ethical, and economic challenges they present.

Autonomous vehicles rely on a sophisticated array of sensors, including cameras, radar, and lidar (light detection and ranging), combined with powerful AI systems that process the sensor data and make driving decisions in real time. The technology has made remarkable progress in recent years, with some systems now capable of handling complex urban environments, navigating around pedestrians and cyclists, and responding appropriately to unexpected situations. However, truly fully autonomous vehicles—those capable of operating without any human intervention in all conditions—remain elusive. Edge cases, unusual weather, complex interactions with human drivers, and unpredictable events continue to challenge even the most advanced systems.

The potential benefits of autonomous vehicles are significant. Proponents argue that self-driving cars could dramatically reduce traffic accidents, which currently kill over a million people worldwide each year, with the vast majority caused by human error. Autonomous vehicles do not get drunk, distracted, or tired. They could provide mobility to those who cannot drive themselves, including the elderly and disabled. They could reduce traffic congestion through more efficient driving, lower emissions through optimized routing and electric powertrains, and free up time currently spent driving for other activities.

However, the challenges are equally significant. Beyond the technical difficulties, autonomous vehicles raise complex legal and ethical questions. If a self-driving car must choose between hitting a pedestrian or swerving and risking its passenger, what should it do? Who is liable when an autonomous vehicle causes an accident—the owner, the manufacturer, or the software developer? How should data collected by these vehicles be handled, and who owns it? Regulators are grappling with how to certify the safety of these systems, while traditional automotive industry workers fear the economic disruption that widespread adoption could bring. Despite these challenges, the trajectory toward increasing vehicle autonomy seems clear, with most major automakers and tech companies investing heavily in the technology.