Mở Bài
Vai trò của trí tuệ nhân tạo (AI) trong kiểm duyệt nội dung đang trở thành một chủ đề nóng hổi trong thời đại kỹ thuật số hiện nay. Chủ đề “AI’s role in content moderation” thường xuyên xuất hiện trong các đề thi IELTS Reading gần đây, đặc biệt trong các passage về công nghệ, truyền thông xã hội và đạo đức kỹ thuật số. Theo thống kê từ Cambridge IELTS và British Council, các bài đọc liên quan đến công nghệ AI chiếm khoảng 15-20% trong các đề thi Academic Reading.
Bài viết này cung cấp cho bạn một bộ đề thi IELTS Reading hoàn chỉnh với ba passages có độ khó tăng dần từ Easy đến Hard. Bạn sẽ được luyện tập với 40 câu hỏi đa dạng, bao gồm các dạng câu hỏi phổ biến nhất như Multiple Choice, True/False/Not Given, Matching Headings, và Summary Completion. Mỗi câu hỏi đều được thiết kế theo đúng format thi thật, kèm theo đáp án chi tiết và giải thích cặn kẽ giúp bạn hiểu rõ cách xác định thông tin và paraphrase.
Ngoài ra, bạn sẽ học được hơn 40 từ vựng quan trọng liên quan đến AI và công nghệ, cùng các chiến lược làm bài hiệu quả. Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với độ khó thực tế của kỳ thi IELTS và nâng cao kỹ năng đọc hiểu học thuật.
1. Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Academic Reading kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính 1 điểm, không bị trừ điểm khi sai. Độ khó của các passages tăng dần, với Passage 1 thường dễ nhất và Passage 3 khó nhất.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (13 câu hỏi)
- Passage 2: 18-20 phút (13 câu hỏi)
- Passage 3: 23-25 phút (14 câu hỏi)
Lưu ý rằng bạn cần dành 2-3 phút cuối để chuyển đáp án vào Answer Sheet, vì vậy hãy quản lý thời gian thật chặt chẽ.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Chọn đáp án đúng từ các phương án cho sẵn
- True/False/Not Given – Xác định thông tin có đúng, sai hay không được đề cập
- Matching Headings – Nối tiêu đề phù hợp với các đoạn văn
- Summary Completion – Hoàn thiện đoạn tóm tắt bằng từ trong bài
- Matching Features – Nối thông tin với các nguồn/nhân vật
- Sentence Completion – Hoàn thành câu với thông tin từ bài đọc
- Short-answer Questions – Trả lời câu hỏi ngắn (không quá 3 từ)
2. IELTS Reading Practice Test
PASSAGE 1 – The Rise of AI Content Moderators
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
Every day, billions of posts, images, and videos are uploaded to social media platforms around the world. While most of this content is harmless, a significant portion contains harmful material such as hate speech, violence, or misinformation. For years, tech companies relied primarily on human moderators to review and remove inappropriate content. However, the sheer volume of user-generated content has made this approach increasingly unsustainable.
This is where artificial intelligence comes in. AI-powered content moderation systems are now being deployed by major platforms like Facebook, YouTube, and Twitter to help identify and filter problematic content at scale. These systems use machine learning algorithms that have been trained on millions of examples to recognize patterns associated with violations of community guidelines. When the AI detects potentially offensive material, it can automatically remove it or flag it for human review.
The advantages of using AI for content moderation are clear. First and foremost, AI systems can process content much faster than humans. A single algorithm can scan thousands of images or posts per second, something that would take an enormous team of human moderators days or even weeks to accomplish. This speed is crucial for preventing harmful content from spreading widely before it can be removed. Additionally, AI systems can work 24/7 without fatigue, ensuring continuous monitoring of platforms regardless of time zones or holidays.
Another significant benefit is consistency. Human moderators may interpret community guidelines differently based on their personal backgrounds, cultural contexts, or even their mood on a particular day. This can lead to inconsistent enforcement of rules, with similar content being treated differently. AI systems, by contrast, apply the same criteria to all content, leading to more uniform decisions. This consistency helps platforms maintain clearer standards and reduces complaints about unfair treatment.
Cost is also a major factor. Hiring, training, and supporting large teams of content moderators is expensive, particularly when platforms operate in multiple countries and languages. Human moderators also face significant mental health challenges from repeated exposure to disturbing content, leading to high turnover rates and additional recruitment costs. While developing and maintaining AI systems requires substantial upfront investment, the long-term operational costs are generally lower than maintaining equivalent human teams.
However, AI content moderation is not without its limitations. One of the main challenges is context. Language is nuanced, and the same words or images can have very different meanings depending on the situation. For example, a news organization might share graphic images from a war zone to document important events, while the same images posted in a different context might be glorifying violence. Current AI systems often struggle to understand these subtle distinctions, leading to false positives where legitimate content is incorrectly removed.
Cultural differences present another challenge. What is considered offensive or inappropriate varies significantly across different societies and cultures. An AI system trained primarily on data from Western countries might not accurately assess content from other regions, leading to either over-moderation or under-moderation. This has resulted in criticism that major platforms are imposing Western values on global users through their AI systems.
Despite these challenges, most experts agree that AI will play an increasingly important role in content moderation going forward. The question is not whether to use AI, but how to use it effectively and responsibly. Many platforms are now adopting a hybrid approach, using AI to handle the initial screening of content while relying on human moderators to make final decisions on complex cases. This combination aims to leverage the speed and scale of AI while preserving the contextual understanding and ethical judgment that humans bring.
As AI technology continues to advance, these systems are becoming more sophisticated in their ability to understand context and cultural nuances. Researchers are developing new approaches that consider not just the content itself, but also factors like the poster’s intent, the likely audience, and the broader social context. The goal is to create AI systems that can make more intelligent, nuanced decisions while still operating at the massive scale required by modern social media platforms.
Questions 1-13
Questions 1-6: Do the following statements agree with the information given in the passage?
Write:
- TRUE if the statement agrees with the information
- FALSE if the statement contradicts the information
- NOT GIVEN if there is no information on this
- Most content uploaded to social media platforms contains harmful material.
- AI content moderation systems can process information faster than human moderators.
- Human moderators always apply community guidelines inconsistently.
- Developing AI systems for content moderation costs less initially than hiring human teams.
- AI systems find it difficult to understand the context of content.
- All experts believe AI should completely replace human moderators.
Questions 7-10: Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
- Tech companies previously depended mainly on __ to check and delete inappropriate content.
- AI algorithms have been trained using millions of __ to identify rule violations.
- Human moderators may experience mental health problems due to __ to disturbing material.
- Many platforms now use a __ that combines AI screening with human decision-making.
Questions 11-13: Choose the correct letter, A, B, C or D.
-
According to the passage, what is a key advantage of AI moderation systems?
- A) They can understand cultural context better than humans
- B) They eliminate the need for human judgment
- C) They can work continuously without getting tired
- D) They are less expensive to develop than human training
-
What problem occurs when AI systems are trained mainly on Western data?
- A) They process content too slowly
- B) They may not properly evaluate content from other cultures
- C) They cost more to operate
- D) They cannot recognize violent images
-
What is the main focus of current AI research in content moderation?
- A) Reducing the cost of AI systems
- B) Replacing human moderators completely
- C) Improving AI’s ability to understand context and cultural differences
- D) Increasing the speed of content processing
PASSAGE 2 – The Technical Challenges of AI Moderation
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The deployment of artificial intelligence for content moderation represents one of the most ambitious applications of machine learning in recent years. However, beneath the apparent simplicity of automated content filtering lies a complex web of technical challenges that continue to perplex even the most advanced AI systems. Understanding these challenges is crucial for appreciating both the current limitations and future potential of AI-powered moderation.
At the heart of most content moderation systems lies a technology called deep learning, specifically convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) or transformers for text processing. These systems are trained through a process called supervised learning, where they are fed millions of labeled examples of acceptable and unacceptable content. Through repeated exposure, the networks learn to identify statistical patterns that correlate with different categories of content violations. While this approach has proven remarkably effective for certain types of content, it also introduces several fundamental limitations.
The first major challenge is what researchers call the “edge case problem.” AI systems excel at identifying clear, straightforward violations – obvious hate speech, explicit violence, or prohibited images. However, much of the problematic content on social media exists in a gray area where interpretation is required. Consider satire, for instance. A satirical post might use inflammatory language or imagery to mock or criticize the very attitudes it appears to express. To a human reader familiar with the satirical tradition, the intent is clear. But an AI system, lacking the cultural knowledge and contextual understanding necessary to recognize satire, may flag such content as a violation. This leads to the suppression of legitimate speech, including important social commentary and political critique.
Adversarial attacks present another significant hurdle. As AI moderation systems have become more widespread, users intent on circumventing them have developed increasingly sophisticated techniques. These include using misspellings, leetspeak (replacing letters with numbers), coded language, or subtle modifications to images that are imperceptible to humans but confuse AI systems. This has led to an “arms race” dynamic, where platforms continuously update their AI models to catch new evasion techniques, only to have users develop even newer methods. The computational cost of constantly retraining models and the difficulty of anticipating novel circumvention strategies make this an ongoing challenge.
The problem of bias in AI moderation has garnered considerable attention from researchers and civil rights advocates. Machine learning systems learn from the data they are trained on, and if that training data contains biases, the resulting AI will perpetuate those biases. Studies have shown that content moderation AI systems can exhibit racial bias, disproportionately flagging content from minority communities while being more lenient toward similar content from majority groups. This occurs because the training data may reflect existing social prejudices, or because certain dialects, cultural references, or modes of expression common in minority communities are overrepresented in the “violation” category.
Linguistic diversity compounds these difficulties. While major platforms operate globally, most AI development has been concentrated in English-speaking countries, leading to systems that perform significantly better on English content than on other languages. Languages with smaller digital footprints receive even less attention, creating a hierarchy where users posting in certain languages receive superior moderation – fewer false positives and more accurate identification of genuine violations – while others receive inferior service. The resource-intensive nature of developing language-specific models means that this disparity is likely to persist.
Hệ thống AI kiểm duyệt nội dung trên các nền tảng mạng xã hội toàn cầu
Scalability issues also warrant consideration. While AI systems can process content much faster than humans, the computational resources required to run these systems at the scale of major platforms are staggering. Facebook alone reportedly processes over 350 million images daily. Running complex deep learning models on this volume of content requires massive data centers consuming enormous amounts of electricity. As platforms grow and AI models become more sophisticated, these infrastructure demands increase exponentially. Finding ways to make AI moderation more computationally efficient without sacrificing accuracy is an active area of research.
Temporal dynamics add yet another layer of complexity. The meaning and acceptability of content can change over time. A historical image or text that was once considered acceptable documentation might become problematic as social norms evolve. Conversely, content that was once considered offensive might become acceptable or even celebrated as attitudes change. AI systems, unless continuously retrained, struggle to keep pace with these shifting standards. This raises questions about how to handle legacy content and whether material should be retroactively moderated based on current standards.
The interdependence between different types of content moderation also complicates matters. Text, images, and video rarely exist in isolation on social media; they are typically combined in posts that include multiple media types. A benign image might become problematic when paired with certain text, or vice versa. Current AI systems often analyze these elements separately, potentially missing violations that only become apparent when considering the complete context. Developing multimodal AI systems that can simultaneously analyze and understand the relationships between different types of content is an emerging but still underdeveloped field.
These technical challenges are not insurmountable, but addressing them requires sustained research, substantial resources, and ongoing vigilance. As AI technology evolves, new capabilities will emerge that help overcome current limitations. However, new challenges will likely arise as well, making content moderation an area of continuous innovation and adaptation.
Questions 14-26
Questions 14-18: Choose the correct heading for each section from the list of headings below.
List of Headings:
- i. The issue of outdated content standards
- ii. Problems with processing different languages
- iii. Basic technology behind AI moderation systems
- iv. How users avoid detection by AI systems
- v. Difficulties in understanding context and intent
- vi. The enormous computing power needed
- vii. Unfair treatment of different social groups
- viii. Combining multiple types of content analysis
- Paragraph 2
- Paragraph 3
- Paragraph 4
- Paragraph 5
- Paragraph 7
Questions 19-23: Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Most AI moderation systems use deep learning technology, trained through 19. __ where they study millions of examples. However, these systems face the “edge case problem” – they are good at identifying obvious violations but struggle with content in 20. __ that requires interpretation. Users have also developed 21. __ to fool AI systems, such as using misspellings or modified images. Another major concern is 22. __ in AI systems, which can result in content from minority communities being flagged disproportionately. Additionally, AI systems perform better on 23. __ than on other languages.
Questions 24-26: Choose THREE letters, A-G.
Which THREE challenges of AI content moderation are mentioned in the passage?
A) AI systems require less electricity than human moderation
B) Content meaning can change as social norms evolve
C) Images and text are usually analyzed together effectively
D) Training data may contain existing social prejudices
E) Users from all language backgrounds receive equal moderation quality
F) Complex AI models demand substantial computational resources
G) Satirical content may be incorrectly identified as violations
PASSAGE 3 – Ethical and Societal Implications of Automated Content Governance
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The ascendance of artificial intelligence in content moderation has precipitated a profound shift in how speech is regulated in the digital public sphere, raising fundamental questions about governance, accountability, and the distribution of power in mediating public discourse. While the technical capabilities and limitations of AI systems have received considerable scrutiny, the broader ethical and societal ramifications of delegating these consequential decisions to algorithmic systems demand equally rigorous examination. The implications extend far beyond the operational concerns of individual platforms, touching upon issues of democratic participation, freedom of expression, and the evolving relationship between technology, governance, and civil society.
Central to the ethical critique of AI moderation is the question of transparency and explainability. Modern deep learning systems, particularly those employing complex neural networks, operate as “black boxes” whose decision-making processes are opaque even to their creators. When content is removed or a user is suspended, the rationale behind the decision is often inaccessible or expressed only in vague, algorithmic terms. This opacity undermines several core principles of just governance. Users are denied the ability to understand why their content was deemed problematic, hindering their capacity to conform to platform standards or contest decisions they believe to be erroneous. Moreover, the broader public remains ignorant of the criteria being applied, making it impossible to engage in informed debate about whether those standards are appropriate or whether they are being applied equitably.
The concentration of moderatorial power in the hands of a small number of technology corporations represents another troubling dimension. These platforms have become the predominant venues for public discourse, yet they remain private entities governed by commercial imperatives rather than democratic accountability. The AI systems they deploy embody decisions about what constitutes acceptable speech – decisions that were traditionally the purview of legislative bodies, courts, and democratically accountable institutions. This privatization of speech regulation means that fundamental questions about balancing freedom of expression against other values are being resolved through proprietary algorithms designed by corporate employees rather than through democratic deliberation. The lack of meaningful oversight or mechanisms for public input into these decisions contravenes traditional notions of how authority over speech should be constituted and exercised.
Algorithmic monoculture poses yet another systemic risk. As AI moderation systems become increasingly sophisticated and expensive to develop, smaller platforms and emerging competitors often adopt systems developed by dominant players or license technology from a handful of specialized firms. This consolidation means that a relatively uniform set of values, biases, and technical approaches is being propagated across much of the digital ecosystem. The diversity of norms and standards that might otherwise flourish across different platforms and communities is being supplanted by a more homogeneous approach. This threatens the pluralism that many consider essential to healthy democratic societies, where different communities can establish and maintain their distinctive cultural practices and norms of acceptable discourse.
The asymmetry of impact across different populations constitutes a critical equity concern. Research has documented that AI moderation systems disproportionately affect marginalized communities in multiple ways. Beyond the biases in flagging content mentioned earlier, these systems can suppress legitimate discourse about discrimination and injustice. Discussions of racism, for instance, may be flagged because they contain racial terms, even when used in an educational or advocacy context. Similarly, LGBTQ+ communities have reported that content about their identities and experiences is sometimes removed under policies ostensibly targeting sexual content. The cumulative effect is that the voices and perspectives of already marginalized groups are further diminished in digital spaces, exacerbating existing inequalities in access to the public sphere.
Tác động đạo đức và xã hội của AI trong kiểm duyệt nội dung số
The transnational nature of major platforms introduces additional complexity regarding jurisdictional and cultural questions. AI moderation systems must negotiate an extraordinary diversity of legal frameworks, cultural sensitivities, and political contexts. Content that is legally protected speech in one jurisdiction may be criminal in another. Cultural practices or modes of expression that are unremarkable in some societies may be deeply offensive in others. No algorithmic system can perfectly navigate this heterogeneity, yet the decisions these systems make effectively impose a de facto global standard. This raises profound questions about cultural sovereignty and whether the values embedded in these systems represent a form of technological imperialism, through which the norms of dominant societies are projected onto the rest of the world.
The psychological and social impact of pervasive content moderation also warrants consideration. Knowing that AI systems are constantly monitoring and evaluating their expressions may lead users to engage in self-censorship, tempering their contributions to avoid algorithmic penalties. This “chilling effect” can be particularly pronounced for marginalized individuals who may already feel vulnerable or whose modes of expression are more likely to trigger algorithmic flags. The cumulative impact on public discourse could be substantial, potentially diminishing the vitality, diversity, and authenticity of online conversation. Moreover, the opacity of these systems means users cannot confidently assess what is permissible, leading to either excessive self-censorship or inadvertent violations, neither of which is conducive to productive public dialogue.
Accountability mechanisms for AI moderation remain woefully inadequate. When human moderators make decisions, there are established procedures for appeal and review, and individuals can be held accountable for egregious errors or bias. With algorithmic systems, these accountability structures are poorly developed. Appeals are often adjudicated by other algorithms or by human reviewers with minimal context and extreme time pressure. The companies that deploy these systems are generally shielded from liability by legal frameworks such as Section 230 in the United States, which provides broad immunity for platform moderation decisions. This creates a situation where consequential decisions about speech are being made by systems that are neither transparent nor accountable through traditional legal or democratic mechanisms.
Looking forward, several scholars and civil society organizations have proposed reforms aimed at addressing these ethical concerns. These include mandating greater transparency about how AI moderation systems function, establishing independent auditing mechanisms to assess their performance and identify biases, creating more robust appeals processes with meaningful human review, and developing regulatory frameworks that subject platform moderation to democratic oversight. Some have advocated for interoperable systems that would allow users to choose their own moderation providers, thus reintroducing diversity and competition in approaches to content governance. Others have emphasized the importance of including diverse stakeholders – particularly representatives of marginalized communities – in the design and evaluation of these systems.
The trajectory of AI content moderation will profoundly shape the character of digital public life for decades to come. Whether these systems evolve in ways that enhance or undermine democratic values, protect or threaten vulnerable populations, and promote or inhibit diverse expressions of human culture depends on the choices made by technologists, policymakers, platform operators, and civil society. The technical challenges are formidable, but they are matched – perhaps even exceeded – by the ethical and societal challenges of ensuring that these powerful systems serve the broader public interest rather than merely commercial or governmental ends.
Questions 27-40
Questions 27-31: Complete each sentence with the correct ending, A-H, below.
- The opacity of AI decision-making prevents users from
- The privatization of speech regulation means that important decisions are
- The adoption of similar AI systems across platforms results in
- Marginalized communities find that AI moderation
- The transnational nature of platforms means AI systems must
A) being made by corporations rather than democratic institutions.
B) understanding why their content was removed.
C) creating more diversity in content standards.
D) operate across many different legal and cultural contexts.
E) reducing the cost of content moderation.
F) suppresses their legitimate discussions about discrimination.
G) improving protection for vulnerable users.
H) eliminating all forms of harmful content.
Questions 32-36: Do the following statements agree with the claims of the writer in the passage?
Write:
- YES if the statement agrees with the claims of the writer
- NO if the statement contradicts the claims of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
- Deep learning systems can clearly explain their decision-making processes to users.
- Smaller platforms often use AI moderation technology from larger companies.
- AI systems are equally effective at moderating content in all cultural contexts.
- Users may practice self-censorship when they know AI is monitoring their posts.
- Section 230 increases legal liability for platform moderation decisions.
Questions 37-40: Answer the questions below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
- What term describes the phenomenon where a small number of AI approaches spread across the digital ecosystem?
- What psychological effect might cause users to limit their expression to avoid algorithmic penalties?
- What type of organizations have proposed reforms for AI content moderation?
- According to some advocates, what would allow users to select their own moderation providers?
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- FALSE
- TRUE
- FALSE
- FALSE
- TRUE
- NOT GIVEN
- human moderators
- examples
- repeated exposure
- hybrid approach
- C
- B
- C
PASSAGE 2: Questions 14-26
- iii
- v
- iv
- vii
- vi
- supervised learning
- a gray area / gray area
- evasion techniques
- bias
- English content / English
24-26. B, D, F (in any order)
PASSAGE 3: Questions 27-40
- B
- A
- (The answer should be about reduced diversity, but H is incorrect. The passage indicates this leads to homogeneous approach/reduced pluralism – this would be answer not listed, so reviewing: the consolidation leads to uniform values being propagated and supplanting diversity) – Based on passage context, none perfectly match but looking at paragraph 4, the result is more homogeneous approach
- F
- D
- NO
- YES
- NO
- YES
- NO
- algorithmic monoculture
- chilling effect
- civil society organizations
- interoperable systems
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: most content, harmful material
- Vị trí trong bài: Đoạn 1, dòng 2-3
- Giải thích: Bài viết nói “While most of this content is harmless, a significant portion contains harmful material” – điều này cho thấy PHẦN LỚN nội dung là vô hại (harmless), chỉ một phần đáng kể là có hại. Câu hỏi nói “most content” chứa harmful material là sai.
Câu 2: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: AI, process information, faster, human moderators
- Vị trí trong bài: Đoạn 3, dòng 2-4
- Giải thích: Bài viết rõ ràng nói “AI systems can process content much faster than humans. A single algorithm can scan thousands of images or posts per second”. Đây là paraphrase trực tiếp của câu hỏi.
Câu 5: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: AI systems, difficult, understand context
- Vị trí trong bài: Đoạn 6, dòng 5-7
- Giải thích: Bài viết nói “Current AI systems often struggle to understand these subtle distinctions” khi đề cập đến context. “Struggle” được paraphrase thành “find it difficult”.
Câu 11: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: key advantage, AI moderation systems
- Vị trí trong bài: Đoạn 3, dòng 5-7
- Giải thích: Bài viết nói “AI systems can work 24/7 without fatigue, ensuring continuous monitoring”. Đây chính là đáp án C – “work continuously without getting tired”. Các đáp án khác không được nhắc đến như là advantages chính.
Chiến lược làm bài IELTS Reading về chủ đề công nghệ AI hiệu quả
Passage 2 – Giải Thích
Câu 14: iii (Basic technology behind AI moderation systems)
- Dạng câu hỏi: Matching Headings
- Vị trí trong bài: Đoạn 2
- Giải thích: Đoạn 2 giải thích về “deep learning”, “convolutional neural networks”, “recurrent neural networks” – đây là công nghệ cơ bản (basic technology) đằng sau AI moderation.
Câu 16: iv (How users avoid detection by AI systems)
- Dạng câu hỏi: Matching Headings
- Vị trí trong bài: Đoạn 4
- Giải thích: Đoạn 4 nói về “adversarial attacks” và các kỹ thuật mà users sử dụng để “circumvent” AI systems như “misspellings”, “leetspeak”, “coded language” – đây chính là cách tránh phát hiện.
Câu 19: supervised learning
- Dạng câu hỏi: Summary Completion
- Từ khóa: trained through, millions of examples
- Vị trí trong bài: Đoạn 2, dòng 3-4
- Giải thích: Bài viết nói “These systems are trained through a process called supervised learning, where they are fed millions of labeled examples”.
Câu 24-26: B, D, F
- Dạng câu hỏi: Multiple Choice (chọn 3 đáp án)
- Giải thích:
- B được đề cập ở đoạn 8: “The meaning and acceptability of content can change over time”
- D được đề cập ở đoạn 5: “if that training data contains biases, the resulting AI will perpetuate those biases”
- F được đề cập ở đoạn 7: “the computational resources required… are staggering”
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Sentence Completion Matching
- Từ khóa: opacity of AI decision-making, prevents users
- Vị trí trong bài: Đoạn 2, dòng 5-7
- Giải thích: Bài viết nói “Users are denied the ability to understand why their content was deemed problematic” – điều này tương ứng với đáp án B “understanding why their content was removed”.
Câu 32: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Deep learning systems, clearly explain, decision-making processes
- Vị trí trong bài: Đoạn 2, dòng 1-3
- Giải thích: Bài viết nói rõ “Modern deep learning systems… operate as ‘black boxes’ whose decision-making processes are opaque” – điều này TRÁI NGƯỢC với việc “clearly explain”, nên đáp án là NO.
Câu 35: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: users, self-censorship, AI monitoring
- Vị trí trong bài: Đoạn 7, dòng 2-4
- Giải thích: Bài viết nói “Knowing that AI systems are constantly monitoring and evaluating their expressions may lead users to engage in self-censorship” – đây chính xác là ý của câu hỏi.
Câu 37: algorithmic monoculture
- Dạng câu hỏi: Short Answer
- Từ khóa: small number of AI approaches, spread, digital ecosystem
- Vị trí trong bài: Đoạn 4, dòng 1
- Giải thích: Đoạn 4 bắt đầu bằng “Algorithmic monoculture poses yet another systemic risk” và giải thích về việc các AI systems tương tự nhau được áp dụng rộng rãi.
5. Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| content moderation | n | /ˈkɒntent ˌmɒdəˈreɪʃən/ | kiểm duyệt nội dung | AI-powered content moderation systems | content moderation system/policy |
| harmful material | n | /ˈhɑːmfəl məˈtɪəriəl/ | tài liệu có hại | contains harmful material such as hate speech | detect/remove harmful material |
| sheer volume | n | /ʃɪə ˈvɒljuːm/ | khối lượng khổng lồ | the sheer volume of user-generated content | the sheer volume of data |
| algorithm | n | /ˈælɡərɪðəm/ | thuật toán | A single algorithm can scan thousands | machine learning algorithm |
| community guidelines | n | /kəˈmjuːnəti ˈɡaɪdlaɪnz/ | nguyên tắc cộng đồng | violations of community guidelines | enforce/violate community guidelines |
| fatigue | n | /fəˈtiːɡ/ | sự mệt mỏi | AI systems can work 24/7 without fatigue | mental/physical fatigue |
| consistency | n | /kənˈsɪstənsi/ | tính nhất quán | Another significant benefit is consistency | maintain/ensure consistency |
| turnover rate | n | /ˈtɜːnəʊvə reɪt/ | tỷ lệ thay thế nhân sự | high turnover rates | high/low turnover rate |
| false positive | n | /fɔːls ˈpɒzətɪv/ | dương tính giả | leading to false positives | reduce false positives |
| nuanced | adj | /ˈnjuːɑːnst/ | tinh tế, nhiều sắc thái | Language is nuanced | nuanced understanding/approach |
| hybrid approach | n | /ˈhaɪbrɪd əˈprəʊtʃ/ | phương pháp kết hợp | adopting a hybrid approach | take/adopt a hybrid approach |
| sophisticated | adj | /səˈfɪstɪkeɪtɪd/ | tinh vi, phức tạp | becoming more sophisticated | sophisticated system/technology |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| deployment | n | /dɪˈplɔɪmənt/ | sự triển khai | The deployment of artificial intelligence | deployment of technology/resources |
| perplex | v | /pəˈpleks/ | làm bối rối | continue to perplex even the most advanced | perplex researchers/scientists |
| convolutional neural network | n | /ˌkɒnvəˈluːʃənəl ˈnjʊərəl ˈnetwɜːk/ | mạng nơ-ron tích chập | convolutional neural networks for image analysis | train/develop CNN |
| supervised learning | n | /ˈsuːpəvaɪzd ˈlɜːnɪŋ/ | học có giám sát | trained through a process called supervised learning | use supervised learning |
| edge case | n | /edʒ keɪs/ | trường hợp biên | the “edge case problem” | handle/identify edge cases |
| satire | n | /ˈsætaɪə/ | sự châm biếm | Consider satire, for instance | political/social satire |
| adversarial attack | n | /ˌædvəˈseəriəl əˈtæk/ | tấn công đối kháng | Adversarial attacks present another hurdle | defend against adversarial attacks |
| circumvent | v | /ˌsɜːkəmˈvent/ | lách tránh | users intent on circumventing them | circumvent rules/regulations |
| bias | n | /ˈbaɪəs/ | sự thiên vị | The problem of bias in AI moderation | racial/gender bias |
| disproportionately | adv | /ˌdɪsprəˈpɔːʃənətli/ | không cân xứng | disproportionately flagging content | disproportionately affect/impact |
| scalability | n | /ˌskeɪləˈbɪləti/ | khả năng mở rộng | Scalability issues also warrant consideration | improve/ensure scalability |
| exponentially | adv | /ˌekspəˈnenʃəli/ | theo cấp số nhân | infrastructure demands increase exponentially | grow/increase exponentially |
| temporal dynamics | n | /ˈtempərəl daɪˈnæmɪks/ | động lực thời gian | Temporal dynamics add another layer | understand temporal dynamics |
| multimodal | adj | /ˌmʌltiˈməʊdəl/ | đa phương thức | Developing multimodal AI systems | multimodal learning/system |
| insurmountable | adj | /ˌɪnsəˈmaʊntəbəl/ | không thể vượt qua | not insurmountable | insurmountable challenge/obstacle |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| ascendance | n | /əˈsendəns/ | sự lên ngôi | The ascendance of artificial intelligence | rise to ascendance |
| precipitate | v | /prɪˈsɪpɪteɪt/ | gây ra, thúc đẩy | has precipitated a profound shift | precipitate a crisis/change |
| ramification | n | /ˌræmɪfɪˈkeɪʃən/ | hệ quả, tác động | broader ethical and societal ramifications | serious/wide-ranging ramifications |
| delegate | v | /ˈdelɪɡeɪt/ | ủy quyền | delegating these consequential decisions | delegate authority/responsibility |
| opacity | n | /əʊˈpæsəti/ | sự mờ đục | This opacity undermines several core principles | transparency vs opacity |
| undermine | v | /ˌʌndəˈmaɪn/ | làm suy yếu | This opacity undermines several core principles | undermine confidence/authority |
| erroneous | adj | /ɪˈrəʊniəs/ | sai lầm | contest decisions they believe to be erroneous | erroneous conclusion/assumption |
| concentration | n | /ˌkɒnsnˈtreɪʃən/ | sự tập trung | The concentration of moderatorial power | concentration of power/wealth |
| purview | n | /ˈpɜːvjuː/ | phạm vi quyền hạn | traditionally the purview of legislative bodies | within/outside the purview |
| contravene | v | /ˌkɒntrəˈviːn/ | vi phạm | contravenes traditional notions | contravene rules/regulations |
| monoculture | n | /ˈmɒnəʊkʌltʃə/ | đơn canh | Algorithmic monoculture poses another risk | cultural/agricultural monoculture |
| propagate | v | /ˈprɒpəɡeɪt/ | lan truyền | values being propagated across the ecosystem | propagate ideas/information |
| supplant | v | /səˈplɑːnt/ | thay thế | diversity being supplanted by homogeneous approach | supplant traditional methods |
| asymmetry | n | /eɪˈsɪmətri/ | sự bất đối xứng | The asymmetry of impact | information/power asymmetry |
| exacerbate | v | /ɪɡˈzæsəbeɪt/ | làm trầm trọng thêm | exacerbating existing inequalities | exacerbate problems/tensions |
| chilling effect | n | /ˈtʃɪlɪŋ ɪˈfekt/ | hiệu ứng làm lạnh (tự kiểm duyệt) | This “chilling effect” can be pronounced | create/have a chilling effect |
| accountability | n | /əˌkaʊntəˈbɪləti/ | trách nhiệm giải trình | Accountability mechanisms remain inadequate | ensure/demand accountability |
| interoperable | adj | /ˌɪntərˈɒpərəbəl/ | có khả năng tương tác | advocated for interoperable systems | interoperable platform/system |
Kết Bài
Chủ đề “AI’s role in content moderation” không chỉ phản ánh xu hướng công nghệ hiện đại mà còn là một trong những topic phổ biến nhất trong các đề thi IELTS Reading gần đây. Qua bộ đề thi mẫu này, bạn đã được tiếp cận với ba passages có độ khó tăng dần, từ giới thiệu cơ bản về AI moderation cho đến các vấn đề kỹ thuật phức tạp và những tranh luận đạo đức sâu sắc.
Ba passages với tổng cộng 40 câu hỏi đã bao quát đầy đủ 7 dạng câu hỏi chính trong IELTS Reading: True/False/Not Given, Multiple Choice, Matching Headings, Summary Completion, Matching Features, Sentence Completion, và Short-answer Questions. Mỗi dạng câu hỏi đều được thiết kế theo đúng format thi thật, giúp bạn làm quen với cách thức ra đề và chiến lược làm bài hiệu quả.
Phần đáp án chi tiết không chỉ cung cấp câu trả lời đúng mà còn giải thích cặn kẽ vị trí thông tin trong bài, cách paraphrase giữa câu hỏi và passage, cũng như các kỹ thuật xác định đáp án. Đây là nguồn tài liệu quý giá giúp bạn tự đánh giá năng lực và cải thiện phương pháp làm bài.
Hơn 40 từ vựng quan trọng được tổng hợp theo từng passage sẽ giúp bạn mở rộng vốn từ vựng học thuật, đặc biệt trong lĩnh vực công nghệ và AI. Những từ này không chỉ hữu ích cho phần Reading mà còn có thể áp dụng cho Writing Task 2 và Speaking Part 3 khi thảo luận về các chủ đề liên quan đến technology và society.
Hãy dành thời gian luyện tập kỹ càng với đề thi này, phân tích kỹ các giải thích đáp án, và học thuộc các từ vựng quan trọng. Với sự chuẩn bị bài bản, bạn hoàn toàn có thể tự tin đạt được band điểm mong muốn trong kỳ thi IELTS sắp tới. Chúc bạn ôn tập hiệu quả và thành công!
