IELTS Reading: Trí Tuệ Nhân Tạo Trong Ngành Luật - Đề Thi Mẫu Có Đáp Án » IELTS.NET

Mở Bài

Chủ đề về trí tuệ nhân tạo (AI) và sự chuyển đổi số trong các ngành nghề hiện đại ngày càng xuất hiện thường xuyên trong các đề thi IELTS Reading. Đặc biệt, câu hỏi “How Is AI Transforming The Legal Industry?” (AI đang chuyển đổi ngành luật như thế nào?) là một chủ đề nóng, kết hợp giữa công nghệ và pháp luật – hai lĩnh vực quan trọng của xã hội đương đại.

Trong bài viết này, bạn sẽ được trải nghiệm một bài thi IELTS Reading hoàn chỉnh với ba passages có độ khó tăng dần từ Easy đến Hard. Bộ đề này được thiết kế dựa trên cấu trúc của Cambridge IELTS, bao gồm đầy đủ 40 câu hỏi với các dạng bài đa dạng như Multiple Choice, True/False/Not Given, Matching Headings, và Summary Completion. Bạn cũng sẽ nhận được đáp án chi tiết kèm giải thích cụ thể và bảng từ vựng quan trọng giúp nâng cao vốn từ học thuật.

Đề thi này phù hợp cho học viên có trình độ từ band 5.0 trở lên, giúp bạn làm quen với chủ đề công nghệ trong bối cảnh pháp lý và rèn luyện kỹ năng đọc hiểu theo đúng format thi thật.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test là một trong bốn kỹ năng được đánh giá trong kỳ thi IELTS, với những đặc điểm cơ bản sau:

Thời gian: 60 phút cho 3 passages (không có thời gian thêm để chuyển đáp án)
Tổng số câu hỏi: 40 câu
Điểm số: Mỗi câu đúng được 1 điểm, tổng 40 điểm được quy đổi thành band score từ 1-9
Độ dài văn bản: Khoảng 2000-2750 từ tổng cộng

Phân bổ thời gian khuyến nghị:

Passage 1 (Easy): 15-17 phút
Passage 2 (Medium): 18-20 phút
Passage 3 (Hard): 23-25 phút
Thời gian kiểm tra lại: 2-4 phút

Các Dạng Câu Hỏi Trong Đề Này

Trong bài thi mẫu này, bạn sẽ gặp các dạng câu hỏi phổ biến nhất của IELTS Reading:

Multiple Choice – Câu hỏi trắc nghiệm nhiều lựa chọn
True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập
Matching Headings – Nối tiêu đề với đoạn văn
Summary Completion – Hoàn thành đoạn tóm tắt
Matching Features – Nối đặc điểm với nhân vật/tổ chức
Short-answer Questions – Câu hỏi trả lời ngắn

Tương tự như AI in predicting financial markets, chủ đề về trí tuệ nhân tạo trong các ngành chuyên môn đang ngày càng phổ biến trong IELTS Reading. Việc làm quen với các thuật ngữ và cấu trúc câu học thuật trong lĩnh vực này sẽ giúp bạn tự tin hơn khi đối mặt với các đề thi thực tế.

2. IELTS Reading Practice Test

PASSAGE 1 – The Dawn of Legal Technology

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The legal profession has long been regarded as one of the most traditional and conservative fields, where precedent and established practices reign supreme. However, the rapid advancement of artificial intelligence (AI) is beginning to transform the way legal services are delivered, creating both opportunities and challenges for lawyers, law firms, and clients alike.

At its most basic level, AI in the legal industry refers to computer systems that can perform tasks that typically require human intelligence. These tasks include analyzing documents, identifying patterns in legal cases, predicting case outcomes, and even drafting simple legal documents. The technology has become increasingly sophisticated, moving beyond simple automation to more complex forms of machine learning that can actually improve over time.

One of the earliest applications of AI in law was in the area of legal research. Traditionally, lawyers and their assistants would spend countless hours searching through physical law books and case reports to find relevant precedents and legal principles. This process was not only time-consuming but also expensive, with clients often paying hundreds of dollars per hour for what was essentially manual research work. Today, AI-powered research tools can scan millions of documents in seconds, identifying the most relevant cases and statutes with remarkable accuracy. Systems like ROSS Intelligence and Westlaw Edge use natural language processing to understand legal questions posed in plain English and deliver precise answers drawn from vast legal databases.

Công nghệ trí tuệ nhân tạo hỗ trợ nghiên cứu pháp luật và phân tích tài liệu trong ngành luật hiện đại

Document review represents another area where AI has made significant inroads. In large-scale litigation or corporate transactions, lawyers must often review thousands or even millions of documents to find those that are relevant to a case. This process, known as discovery, has traditionally been one of the most labor-intensive aspects of legal work. AI systems can now perform this task much more efficiently. Using supervised machine learning, these systems are “trained” on a sample of documents that human lawyers have classified as relevant or irrelevant. The AI then applies this learning to the remaining documents, flagging those that are likely to be important while filtering out the rest. Studies have shown that AI can achieve accuracy rates of 95% or higher in document review, often outperforming human reviewers who may become fatigued or distracted during long review sessions.

Contract analysis is yet another application that has gained considerable traction. Businesses regularly deal with hundreds or thousands of contracts, from employment agreements to supplier contracts. AI tools can extract key information from these documents, such as payment terms, termination clauses, and liability provisions, and present them in an easily digestible format. This allows legal teams to quickly identify potential risks or inconsistencies across their contract portfolio. Some AI systems can even flag unusual or non-standard clauses that might warrant closer human review.

The adoption of AI in law firms has been gradual but steady. Many large firms have established dedicated innovation teams or partnered with legal technology companies to integrate AI tools into their practices. The benefits are clear: AI can handle routine tasks more quickly and cheaply than human lawyers, allowing legal professionals to focus on more complex and strategic work that requires human judgment and creativity. This shift has the potential to make legal services more accessible and affordable for clients who previously could not afford traditional legal fees.

However, the integration of AI into legal practice is not without its challenges. One major concern is the “black box” problem – many AI systems, particularly those using deep learning, make decisions in ways that are not easily understood or explained by humans. In a field where transparency and the ability to justify decisions are paramount, this lack of explainability can be problematic. Additionally, there are questions about liability when AI systems make errors. If an AI tool misses a crucial case or misclassifies an important document, who is responsible – the lawyer, the law firm, or the technology provider?

Despite these concerns, the trajectory is clear: AI is becoming an increasingly integral part of the legal profession. As the technology continues to evolve and mature, its role will likely expand beyond basic tasks to more sophisticated applications. The lawyers who embrace this change and learn to work alongside AI tools will be best positioned to thrive in this new era of legal practice.

Questions 1-13

Questions 1-5: Multiple Choice

Choose the correct letter, A, B, C, or D.

According to the passage, the legal profession has traditionally been characterized as:
A. innovative and fast-paced
B. conservative and traditional
C. technologically advanced
D. affordable and accessible
AI-powered legal research tools can:
A. replace all human lawyers
B. only search physical law books
C. scan millions of documents quickly
D. work slower than manual research
The process of reviewing documents to find relevant information in legal cases is called:
A. litigation
B. automation
C. discovery
D. classification
AI systems in document review achieve accuracy rates of:
A. 50% or lower
B. 75% on average
C. 95% or higher
D. 100% in all cases
The “black box” problem refers to:
A. the cost of AI systems
B. the difficulty in understanding how AI makes decisions
C. the color of computer equipment
D. the storage capacity of AI systems

Questions 6-9: True/False/Not Given

Write TRUE if the statement agrees with the information, FALSE if the statement contradicts the information, or NOT GIVEN if there is no information on this.

Legal research traditionally required lawyers to spend many hours searching through physical books.
All law firms have completely replaced human lawyers with AI systems.
AI can extract key information from business contracts such as payment terms and termination clauses.
Legal technology companies earn more profit than traditional law firms.

Questions 10-13: Sentence Completion

Complete the sentences below. Choose NO MORE THAN TWO WORDS from the passage for each answer.

AI systems use __ __ to understand legal questions written in plain English.
In contract analysis, AI tools can present information in an easily __ format.
Many large law firms have created dedicated __ teams to work with AI technology.
Lawyers who learn to work with AI tools will be best __ to succeed in modern legal practice.

PASSAGE 2 – Artificial Intelligence and Legal Decision-Making

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

As artificial intelligence continues to permeate various sectors of the legal industry, a particularly contentious debate has emerged around its potential role in judicial decision-making. While AI has proven remarkably effective at handling routine, data-intensive tasks such as document review and legal research, the question of whether it should participate in more substantive legal decisions – or even render judgments – raises profound ethical, practical, and philosophical questions about the nature of justice itself.

Proponents of AI-assisted judicial decision-making point to several compelling advantages. First and foremost is the issue of consistency. Human judges, despite their training and experience, are subject to various cognitive biases that can inadvertently affect their decisions. Research has documented the existence of what has been termed the “post-lunch effect,” where judges are statistically more likely to grant parole or deliver lenient sentences after a meal break when their energy levels are restored. Similarly, studies have shown that factors as arbitrary as the weather, the outcome of local sporting events, or even the judge’s personal circumstances can subtly influence judicial decisions. AI systems, devoid of such biological and emotional factors, could theoretically deliver more consistent and predictable outcomes.

Hệ thống trí tuệ nhân tạo hỗ trợ phân tích dữ liệu và đưa ra quyết định pháp lý trong tòa án

Moreover, AI systems can process and synthesize vast amounts of legal precedent far more comprehensively than any human judge could manage. A complex legal case might require consideration of hundreds or thousands of previous decisions, statutes, and legal commentaries. While even the most diligent human judge can only retain and actively consider a fraction of this information, AI systems can instantaneously access and weigh all relevant precedents, potentially leading to more thoroughly grounded decisions. This capability could be particularly valuable in jurisdictions with limited judicial resources, where overworked judges must handle heavy caseloads with insufficient time for comprehensive legal analysis.

Cost efficiency represents another significant advantage. The legal system in many countries faces a crisis of accessibility, with prolonged trial delays and expensive legal representation putting justice out of reach for many citizens. AI systems could potentially streamline certain types of decisions, particularly in routine cases involving straightforward application of established legal principles, thereby reducing backlogs and making the justice system more responsive and affordable. Điều này có điểm tương đồng với How is blockchain technology transforming supply chain management? khi công nghệ giúp tối ưu hóa quy trình và giảm chi phí trong các hệ thống phức tạp.

However, critics raise numerous substantive objections to the use of AI in judicial decision-making. Perhaps most fundamentally, they argue that judging is not merely a mechanical process of applying rules to facts, but rather requires nuanced understanding of human behavior, context, and moral reasoning – qualities that AI systems currently lack. Legal cases often involve ambiguous facts, competing values, and situations where strict application of the law might produce unjust outcomes. Human judges possess the discretion to temper legal rules with mercy, to consider extenuating circumstances, and to interpret laws in ways that align with evolving social values. An AI system, no matter how sophisticated, operates according to its programming and training data, potentially lacking the flexibility and wisdom that characterize the best human judgment.

The problem of algorithmic bias presents another serious concern. AI systems learn from historical data, and if that data reflects past prejudices and inequalities, the AI will perpetuate these biases in its decisions. For instance, AI systems used in the American criminal justice system to predict recidivism rates have been found to disproportionately flag African American defendants as high-risk, reflecting historical overpolicing and discrimination in the training data. Incorporating such systems into judicial decision-making could thus entrench existing injustices rather than eliminate them, creating a veneer of objectivity that masks underlying bias.

Transparency and accountability issues further complicate the picture. Modern AI systems, particularly those using deep learning techniques, often function as “black boxes” whose decision-making processes are opaque even to their creators. In legal systems founded on principles of due process and the right to understand the reasoning behind decisions affecting one’s rights, this lack of explainability is deeply problematic. If a person cannot understand why an AI system reached a particular decision, how can they effectively challenge it or learn from it? Moreover, the question of accountability becomes murky – if an AI system makes an erroneous or unjust decision, who bears responsibility?

There is also the fundamental question of legitimacy and public acceptance. The authority of the judicial system rests partly on the perception that human judges, through their training, experience, and moral character, have earned the right to make decisions that profoundly affect people’s lives. Whether citizens would accord similar legitimacy to decisions made by algorithms remains uncertain. The symbolic importance of being “judged by one’s peers” or having one’s case heard by a human being who can empathize with one’s situation should not be underestimated.

Looking forward, the most pragmatic approach may lie not in replacing human judges with AI, but rather in developing hybrid systems where AI serves as a sophisticated tool to augment human decision-making. AI could handle preliminary analysis, identify relevant precedents, flag potential issues, and even suggest possible outcomes, but ultimate decisions would remain with human judges who can apply contextual understanding, ethical reasoning, and discretionary judgment. This approach could harness the efficiency and consistency benefits of AI while preserving the essential human elements of judicial decision-making.

Questions 14-26

Questions 14-18: Yes/No/Not Given

Do the following statements agree with the views of the writer in the passage? Write:

YES if the statement agrees with the views of the writer
NO if the statement contradicts the views of the writer
NOT GIVEN if it is impossible to say what the writer thinks about this

AI systems are completely free from all forms of bias in decision-making.
Human judges can be influenced by factors such as meal breaks and personal circumstances.
AI systems cost more to maintain than human judges.
The use of AI in American criminal justice has revealed problems with algorithmic bias.
Citizens will automatically accept decisions made by AI systems.

Questions 19-23: Matching Headings

Choose the correct heading for paragraphs B-F from the list of headings below.

List of Headings:
i. The challenge of explaining AI decisions
ii. Financial benefits of AI in the justice system
iii. The problem of historical prejudice in data
iv. Advantages of consistency in judicial decisions
v. The comprehensive nature of AI analysis
vi. A balanced approach for the future
vii. Questions about who deserves to make judgments
viii. The mechanical nature of legal work

Paragraph B
Paragraph C
Paragraph D
Paragraph E
Paragraph F

Questions 24-26: Summary Completion

Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.

AI systems have shown advantages in judicial contexts, particularly in providing more 24 __ outcomes than human judges who may be affected by cognitive biases. They can also process legal precedents more 25 __ than humans. However, critics argue that AI lacks the 26 __ understanding necessary for complex moral decisions in legal cases.

PASSAGE 3 – The Future Landscape: Ethical Frameworks and Regulatory Challenges in AI-Driven Legal Services

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The inexorable integration of artificial intelligence into the legal profession necessitates a fundamental reconceptualization of the regulatory frameworks and ethical paradigms that have traditionally governed legal practice. As AI systems assume increasingly sophisticated roles – from predictive analytics that forecast litigation outcomes to automated contract generation and even rudimentary forms of legal advice – the legal profession confronts a constellation of challenges that transcend mere technological adaptation, touching upon the very essence of what it means to practice law and administer justice in a technologically mediated society.

At the forefront of these challenges lies the question of professional responsibility and accountability in an age of algorithmic decision-making. Traditional legal ethics codes, promulgated in an era when legal services were exclusively delivered through human-to-human interaction, predicate themselves on principles of professional judgment, client confidentiality, and fiduciary duty. These frameworks presume that a human legal professional – be it a lawyer, judge, or paralegal – serves as the locus of decision-making and the bearer of responsibility for outcomes. However, the introduction of AI systems, which can operate with varying degrees of autonomy and opacity, fundamentally disrupts this model.

Consider, for instance, an AI system that provides erroneous legal advice, leading a client to make decisions that result in financial loss or legal liability. Under traditional frameworks, the responsible lawyer would bear professional and potentially legal responsibility for such malpractice. However, when an AI system mediates the advice-giving process, the attribution of responsibility becomes markedly more complex. Should liability rest with the lawyer who deployed the AI tool, the law firm that implemented it, the developers who created the algorithm, or the vendors who sold the software? Each of these actors contributes to the ultimate outcome but in different ways and with different levels of understanding and control over the system’s decision-making processes. Để hiểu rõ hơn về What are the impacts of artificial intelligence on healthcare?, chúng ta thấy các câu hỏi về trách nhiệm và quy định tương tự cũng xuất hiện trong lĩnh vực y tế.

Jurisdictions worldwide are grappling with these questions through various regulatory approaches. The European Union, characteristically proactive in technology regulation, has proposed an AI Act that would classify AI systems according to their risk level and impose stringent requirements on “high-risk” applications, potentially including certain legal AI systems. These requirements might encompass transparency obligations, algorithmic auditing, human oversight, and mechanisms for redress when AI systems cause harm. The proposed framework embodies a precautionary approach, seeking to mitigate risks before they materialize at scale.

In contrast, the United States has adopted a more fragmented and market-driven approach, with different states developing disparate rules regarding AI in legal practice. Some jurisdictions have amended their professional responsibility rules to explicitly address AI, requiring lawyers to possess sufficient understanding of the AI tools they employ and to supervise their use appropriately. The American Bar Association has issued opinions suggesting that lawyers have an ethical duty to stay abreast of changes in law and practice, including technological changes, implicitly encompassing AI literacy within professional competence requirements.

Khung quy định và đạo đức cho việc sử dụng trí tuệ nhân tạo trong ngành luật và hệ thống tư pháp

The challenge of algorithmic transparency and explainability constitutes another critical dimension of the regulatory landscape. Many contemporary AI systems, particularly those employing deep learning methodologies, function as “black boxes” – they produce outputs without providing intelligible explanations of their reasoning processes. This opacity conflicts sharply with foundational legal principles regarding due process, the right to understand decisions that affect one’s legal rights, and the adversarial nature of legal proceedings where parties must be able to examine and challenge the evidence and reasoning against them.

Addressing this challenge requires multifaceted approaches. Technologically, researchers are developing “explainable AI” or “XAI” systems designed to provide human-interpretable justifications for their outputs. However, these systems often face a trade-off between performance and explainability – more interpretable models may be less accurate, while more accurate models may be more opaque. Legally, some jurisdictions are considering requirements that AI systems used in legal contexts must be capable of providing meaningful explanations of their decisions, though implementing such requirements presents considerable technical challenges.

The phenomenon of algorithmic bias – wherein AI systems perpetuate or amplify existing societal prejudices – represents perhaps the most insidious threat posed by AI in legal contexts. Machine learning systems learn patterns from historical data, and when that data reflects discriminatory patterns – whether in policing, sentencing, lending, or other domains – the AI system will reproduce these patterns in its predictions and recommendations. Research has documented numerous instances of such bias, from facial recognition systems that perform poorly on individuals with darker skin tones to recidivism prediction tools that disproportionately label minority defendants as high-risk.

Mitigating algorithmic bias requires interventions at multiple stages of the AI development lifecycle. During data collection and preparation, efforts must be made to ensure training datasets are representative and do not encode historical discriminatory patterns. During model development, fairness metrics – mathematical definitions of what constitutes equitable treatment – must be incorporated into the optimization process, though choosing among competing fairness definitions itself involves normative judgments. Post-deployment, ongoing monitoring is essential to detect bias that may emerge as the system encounters new situations or as societal contexts evolve.

Beyond these technical and regulatory challenges lies a more philosophical question about the role of technology in the administration of justice. Law is not merely a technical enterprise of applying rules to facts; it embodies societal values, mediates social relationships, and evolves in response to changing moral and political sensibilities. The interpretation and application of legal principles often requires judgment calls that balance competing values and interests – precisely the kind of contextual, value-laden decision-making that AI systems, despite their computational power, currently struggle to replicate.

Some scholars argue that certain legal functions – particularly judicial decision-making and areas requiring substantial discretion – should remain exclusively human domains, preserved from algorithmic incursion not merely because current AI technology is inadequate, but because the legitimacy of the legal system depends upon human accountability, moral reasoning, and the symbolic importance of being judged by one’s peers rather than by algorithms. Others contend that this position romanticizes human decision-making, overlooking its well-documented flaws and biases, and that appropriately designed AI systems could enhance rather than undermine justice.

The path forward likely requires what might be termed “human-centered AI” – systems designed not to replace human legal professionals but to augment their capabilities while keeping humans firmly in the decision-making loop. Such an approach would leverage AI’s strengths in processing vast amounts of information, identifying patterns, and performing routine tasks, while preserving human oversight for judgmental functions requiring contextual understanding, ethical reasoning, and accountability. Implementing this vision will require continued collaboration among legal professionals, technologists, ethicists, policymakers, and the public to craft regulatory frameworks that foster beneficial innovation while safeguarding the integrity and fairness of the legal system.

Questions 27-40

Questions 27-31: Multiple Choice

Choose the correct letter, A, B, C, or D.

According to the passage, traditional legal ethics codes were developed:
A. specifically for artificial intelligence systems
B. in an era of exclusively human-delivered legal services
C. to regulate technology companies
D. after the introduction of AI in law
The European Union’s approach to AI regulation can be characterized as:
A. non-existent
B. identical to the United States
C. proactive and precautionary
D. entirely market-driven
The term “black box” in relation to AI systems refers to:
A. the physical appearance of computers
B. storage devices for legal documents
C. systems that produce outputs without intelligible explanations
D. encrypted legal databases
The trade-off in explainable AI systems is between:
A. cost and efficiency
B. performance and explainability
C. speed and accuracy
D. size and power
According to the passage, algorithmic bias occurs when:
A. AI systems are programmed to discriminate
B. developers intentionally create unfair systems
C. AI systems reproduce discriminatory patterns from historical data
D. computers malfunction during operation

Questions 32-36: Matching Features

Match each regulatory approach (A-C) with the correct characteristic (32-36). You may use any letter more than once.

A. European Union approach
B. United States approach
C. Both approaches

Has proposed classifying AI systems by risk level
Involves multiple jurisdictions with different rules
Addresses questions of professional responsibility
Requires algorithmic auditing for high-risk applications
Suggests lawyers need AI literacy

Questions 37-40: Short-answer Questions

Answer the questions below. Choose NO MORE THAN THREE WORDS from the passage for each answer.

What type of AI systems are researchers developing to provide justifications for outputs?
What must be incorporated into the optimization process during AI model development?
What kind of AI approach keeps humans in the decision-making loop?
Who must collaborate to create effective regulatory frameworks for AI in law?

3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

B
C
C
C
B
TRUE
FALSE
TRUE
NOT GIVEN
natural language
digestible
innovation
positioned

PASSAGE 2: Questions 14-26

NO
YES
NOT GIVEN
YES
NO
iv
v
ii
iii
vii
consistent
comprehensively
nuanced

PASSAGE 3: Questions 27-40

B
C
C
B
C
A
B
C
A
B
explainable AI
fairness metrics
human-centered AI
legal professionals, technologists, ethicists, policymakers

4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

Dạng câu hỏi: Multiple Choice
Từ khóa: legal profession, traditionally characterized
Vị trí trong bài: Đoạn A, dòng 1-2
Giải thích: Bài viết nói rõ “The legal profession has long been regarded as one of the most traditional and conservative fields” (Nghề luật từ lâu được xem là một trong những lĩnh vực truyền thống và bảo thủ nhất). Đáp án B “conservative and traditional” trùng khớp chính xác với mô tả này.

Câu 2: C

Dạng câu hỏi: Multiple Choice
Từ khóa: AI-powered legal research tools, can
Vị trí trong bài: Đoạn C, dòng 6-7
Giải thích: Đoạn văn nói “AI-powered research tools can scan millions of documents in seconds” (Các công cụ nghiên cứu chạy bằng AI có thể quét hàng triệu tài liệu trong vài giây). Đây chính là paraphrase của đáp án C.

Câu 3: C

Dạng câu hỏi: Multiple Choice
Từ khóa: process, reviewing documents, relevant information
Vị trí trong bài: Đoạn D, dòng 3-4
Giải thích: Bài viết nêu rõ “This process, known as discovery” khi đề cập đến việc xem xét tài liệu để tìm thông tin liên quan trong vụ kiện.

Câu 4: C

Dạng câu hỏi: Multiple Choice
Từ khóa: AI systems, document review, accuracy rates
Vị trí trong bài: Đoạn D, dòng 8-9
Giải thích: Đoạn văn nói “Studies have shown that AI can achieve accuracy rates of 95% or higher in document review”.

Câu 5: B

Dạng câu hỏi: Multiple Choice
Từ khóa: black box problem, refers to
Vị trí trong bài: Đoạn G, dòng 2-3
Giải thích: Bài viết giải thích “the ‘black box’ problem – many AI systems… make decisions in ways that are not easily understood or explained by humans” (vấn đề hộp đen – nhiều hệ thống AI đưa ra quyết định theo cách không dễ hiểu hoặc giải thích bởi con người).

Câu 6: TRUE

Dạng câu hỏi: True/False/Not Given
Từ khóa: legal research, traditionally, many hours, physical books
Vị trí trong bài: Đoạn C, dòng 1-3
Giải thích: “Traditionally, lawyers and their assistants would spend countless hours searching through physical law books” – thông tin này khớp hoàn toàn với câu phát biểu.

Câu 7: FALSE

Dạng câu hỏi: True/False/Not Given
Từ khóa: all law firms, completely replaced, human lawyers, AI
Vị trí trong bài: Đoạn F, toàn bộ
Giải thích: Đoạn F nói về việc áp dụng AI là “gradual but steady” và AI giúp luật sư tập trung vào công việc phức tạp hơn, chứ không phải thay thế hoàn toàn. Đây là thông tin mâu thuẫn với câu phát biểu.

Câu 8: TRUE

Dạng câu hỏi: True/False/Not Given
Từ khóa: AI, extract, contracts, payment terms, termination clauses
Vị trí trong bài: Đoạn E, dòng 2-3
Giải thích: “AI tools can extract key information from these documents, such as payment terms, termination clauses, and liability provisions” – thông tin trùng khớp chính xác.

Câu 9: NOT GIVEN

Dạng câu hỏi: True/False/Not Given
Từ khóa: legal technology companies, earn more profit, law firms
Vị trí trong bài: Không có thông tin
Giải thích: Bài viết không đề cập đến so sánh lợi nhuận giữa công ty công nghệ pháp lý và văn phòng luật.

Câu 10: natural language

Dạng câu hỏi: Sentence Completion
Từ khóa: AI systems, understand, legal questions, plain English
Vị trí trong bài: Đoạn C, dòng 8-9
Giải thích: “Systems like ROSS Intelligence and Westlaw Edge use natural language processing to understand legal questions posed in plain English”.

Câu 11: digestible

Dạng câu hỏi: Sentence Completion
Từ khóa: contract analysis, AI tools, present information, format
Vị trí trong bài: Đoạn E, dòng 3-4
Giải thích: “present them in an easily digestible format” – từ “digestible” là câu trả lời chính xác.

Câu 12: innovation

Dạng câu hỏi: Sentence Completion
Từ khóa: large law firms, dedicated teams, AI technology
Vị trí trong bài: Đoạn F, dòng 1-2
Giải thích: “Many large firms have established dedicated innovation teams” – từ cần điền là “innovation”.

Câu 13: positioned

Dạng câu hỏi: Sentence Completion
Từ khóa: lawyers, work with AI tools, succeed
Vị trí trong bài: Đoạn H, dòng cuối
Giải thích: “The lawyers who embrace this change and learn to work alongside AI tools will be best positioned to thrive” – “positioned” là từ phù hợp.

Passage 2 – Giải Thích

Câu 14: NO

Dạng câu hỏi: Yes/No/Not Given
Từ khóa: AI systems, completely free, all forms of bias
Vị trí trong bài: Đoạn E, toàn bộ
Giải thích: Đoạn E nói rõ về vấn đề “algorithmic bias” và AI có thể “perpetuate these biases” (duy trì những thành kiến này). Điều này mâu thuẫn hoàn toàn với quan điểm rằng AI hoàn toàn không có thành kiến.

Câu 15: YES

Dạng câu hỏi: Yes/No/Not Given
Từ khóa: human judges, influenced, meal breaks, personal circumstances
Vị trí trong bài: Đoạn B, dòng 3-7
Giải thích: Bài viết nêu rõ về “post-lunch effect” và các yếu tố như thời tiết, kết quả thể thao, hoặc hoàn cảnh cá nhân có thể “subtly influence judicial decisions”.

Câu 16: NOT GIVEN

Dạng câu hỏi: Yes/No/Not Given
Từ khóa: AI systems, cost more, maintain, human judges
Vị trí trong bài: Không có thông tin so sánh chi phí bảo trì
Giải thích: Bài viết chỉ đề cập đến “cost efficiency” và việc giảm chi phí chung, nhưng không so sánh chi phí bảo trì cụ thể.

Câu 17: YES

Dạng câu hỏi: Yes/No/Not Given
Từ khóa: AI, American criminal justice, algorithmic bias
Vị trí trong bài: Đoạn E, dòng 3-5
Giải thích: “AI systems used in the American criminal justice system to predict recidivism rates have been found to disproportionately flag African American defendants as high-risk” – xác nhận vấn đề về bias.

Câu 18: NO

Dạng câu hỏi: Yes/No/Not Given
Từ khóa: citizens, automatically accept, AI decisions
Vị trí trong bài: Đoạn G, cuối đoạn
Giải thích: “Whether citizens would accord similar legitimacy to decisions made by algorithms remains uncertain” – điều này cho thấy người dân không tự động chấp nhận quyết định của AI.

Câu 19: iv (Advantages of consistency in judicial decisions)

Vị trí: Đoạn B
Giải thích: Đoạn B tập trung vào vấn đề “consistency” và các “cognitive biases” của thẩm phán con người, cũng như AI có thể mang lại kết quả nhất quán hơn.

Câu 20: v (The comprehensive nature of AI analysis)

Vị trí: Đoạn C
Giải thích: Đoạn C nói về khả năng của AI trong việc “process and synthesize vast amounts of legal precedent” và “instantaneously access and weigh all relevant precedents”.

Câu 21: ii (Financial benefits of AI in the justice system)

Vị trí: Đoạn D
Giải thích: Đoạn D thảo luận về “cost efficiency” và việc AI có thể “streamline certain types of decisions” và “reducing backlogs”.

Câu 22: iii (The problem of historical prejudice in data)

Vị trí: Đoạn E
Giải thích: Đoạn E tập trung vào “algorithmic bias” và việc AI học từ dữ liệu lịch sử có thể “reflects past prejudices and inequalities”.

Câu 23: vii (Questions about who deserves to make judgments)

Vị trí: Đoạn F (thực chất là đoạn G)
Giải thích: Đoạn này thảo luận về “legitimacy and public acceptance” và câu hỏi về việc ai có quyền đưa ra phán quyết.

Câu 24: consistent

Dạng câu hỏi: Summary Completion
Từ khóa: AI systems, outcomes, human judges, cognitive biases
Vị trí trong bài: Đoạn B, cuối đoạn
Giải thích: “AI systems… could theoretically deliver more consistent and predictable outcomes”.

Câu 25: comprehensively

Dạng câu hỏi: Summary Completion
Từ khóa: process, legal precedents, than humans
Vị trí trong bài: Đoạn C, dòng 1
Giải thích: “AI systems can process and synthesize vast amounts of legal precedent far more comprehensively than any human judge”.

Câu 26: nuanced

Dạng câu hỏi: Summary Completion
Từ khóa: critics, AI lacks, understanding, moral decisions
Vị trí trong bài: Đoạn D, dòng 2-3
Giải thích: “judging… requires nuanced understanding of human behavior, context, and moral reasoning”.

Passage 3 – Giải Thích

Câu 27: B

Dạng câu hỏi: Multiple Choice
Từ khóa: traditional legal ethics codes, developed
Vị trí trong bài: Đoạn B, dòng 1-2
Giải thích: “Traditional legal ethics codes, promulgated in an era when legal services were exclusively delivered through human-to-human interaction”.

Câu 28: C

Dạng câu hỏi: Multiple Choice
Từ khóa: European Union, approach, AI regulation
Vị trí trong bài: Đoạn D, dòng 1-2
Giải thích: “The European Union, characteristically proactive in technology regulation” và đoạn văn mô tả “precautionary approach”.

Câu 29: C

Dạng câu hỏi: Multiple Choice
Từ khóa: black box, AI systems, refers to
Vị trí trong bài: Đoạn F, dòng 1-2
Giải thích: “function as ‘black boxes’ whose decision-making processes are opaque” và “they produce outputs without providing intelligible explanations”.

Câu 30: B

Dạng câu hỏi: Multiple Choice
Từ khóa: trade-off, explainable AI systems
Vị trí trong bài: Đoạn G, dòng 3-4
Giải thích: “these systems often face a trade-off between performance and explainability”.

Câu 31: C

Dạng câu hỏi: Multiple Choice
Từ khóa: algorithmic bias, occurs when
Vị trí trong bài: Đoạn H, dòng 1-3
Giải thích: “Machine learning systems learn patterns from historical data, and when that data reflects discriminatory patterns… the AI system will reproduce these patterns”.

Câu 32: A

Dạng câu hỏi: Matching Features
Giải thích: Đoạn D nói rõ EU “has proposed an AI Act that would classify AI systems according to their risk level”.

Câu 33: B

Dạng câu hỏi: Matching Features
Giải thích: Đoạn E nói về US “has adopted a more fragmented and market-driven approach, with different states developing disparate rules”.

Câu 34: C

Dạng câu hỏi: Matching Features
Giải thích: Cả hai cách tiếp cận đều đề cập đến trách nhiệm nghề nghiệp trong các đoạn D và E.

Câu 35: A

Dạng câu hỏi: Matching Features
Giải thích: Đoạn D nói rõ về yêu cầu “algorithmic auditing” trong đề xuất của EU.

Câu 36: B

Dạng câu hỏi: Matching Features
Giải thích: Đoạn E đề cập “The American Bar Association has issued opinions suggesting that lawyers have an ethical duty to stay abreast of changes… implicitly encompassing AI literacy”.

Câu 37: explainable AI

Dạng câu hỏi: Short-answer Questions
Từ khóa: researchers, developing, provide justifications
Vị trí trong bài: Đoạn G, dòng 2-3
Giải thích: “researchers are developing ‘explainable AI’ or ‘XAI’ systems designed to provide human-interpretable justifications for their outputs”.

Câu 38: fairness metrics

Dạng câu hỏi: Short-answer Questions
Từ khóa: incorporated, optimization process, model development
Vị trí trong bài: Đoạn I, dòng 3-4
Giải thích: “During model development, fairness metrics… must be incorporated into the optimization process”.

Câu 39: human-centered AI

Dạng câu hỏi: Short-answer Questions
Từ khóa: approach, keeps humans, decision-making loop
Vị trí trong bài: Đoạn K, dòng 1-2
Giải thích: “The path forward likely requires what might be termed ‘human-centered AI’ – systems designed not to replace human legal professionals”.

Câu 40: legal professionals, technologists, ethicists, policymakers (có thể chấp nhận bất kỳ 3 trong số này)

Dạng câu hỏi: Short-answer Questions
Từ khóa: collaborate, create, regulatory frameworks
Vị trí trong bài: Đoạn K, dòng cuối
Giải thích: “Implementing this vision will require continued collaboration among legal professionals, technologists, ethicists, policymakers, and the public”.

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
precedent	n	/ˈpresɪdənt/	Tiền lệ	where precedent and established practices reign supreme	legal precedent, set a precedent
transform	v	/trænsˈfɔːrm/	Chuyển đổi, biến đổi	AI is beginning to transform the way legal services are delivered	transform completely, transform radically
sophisticated	adj	/səˈfɪstɪkeɪtɪd/	Tinh vi, phức tạp	The technology has become increasingly sophisticated	highly sophisticated, technologically sophisticated
automation	n	/ˌɔːtəˈmeɪʃn/	Tự động hóa	moving beyond simple automation	automation process, automation technology
relevant	adj	/ˈreləvənt/	Liên quan, thích hợp	identifying the most relevant cases	highly relevant, directly relevant
accuracy	n	/ˈækjərəsi/	Độ chính xác	with remarkable accuracy	high accuracy, improve accuracy
litigation	n	/ˌlɪtɪˈɡeɪʃn/	Tố tụng	In large-scale litigation	civil litigation, litigation process
labor-intensive	adj	/ˈleɪbər ɪnˈtensɪv/	Tốn nhiều nhân công	one of the most labor-intensive aspects	labor-intensive work, labor-intensive industry
supervised machine learning	n phrase	/ˈsuːpərvaɪzd məˈʃiːn ˈlɜːrnɪŋ/	Học máy có giám sát	Using supervised machine learning	supervised learning algorithm, supervised learning model
outperform	v	/ˌaʊtpərˈfɔːrm/	Hoạt động tốt hơn	often outperforming human reviewers	outperform competitors, significantly outperform
adoption	n	/əˈdɑːpʃn/	Sự áp dụng, chấp nhận	The adoption of AI in law firms	widespread adoption, technology adoption
integrate	v	/ˈɪntɪɡreɪt/	Tích hợp	to integrate AI tools into their practices	integrate seamlessly, fully integrate

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
permeate	v	/ˈpɜːrmieɪt/	Thấm vào, lan tỏa	As AI continues to permeate various sectors	permeate through, permeate society
contentious	adj	/kənˈtenʃəs/	Gây tranh cãi	a particularly contentious debate	highly contentious, contentious issue
cognitive bias	n phrase	/ˈkɑːɡnətɪv ˈbaɪəs/	Thành kiến nhận thức	subject to various cognitive biases	cognitive bias effect, unconscious cognitive bias
inadvertently	adv	/ˌɪnədˈvɜːrtəntli/	Vô tình, không chủ ý	can inadvertently affect their decisions	inadvertently cause, inadvertently reveal
lenient	adj	/ˈliːniənt/	Khoan dung, nhẹ nhàng	deliver lenient sentences	lenient punishment, lenient approach
arbitrary	adj	/ˈɑːrbɪtreri/	Tùy tiện, độc đoán	factors as arbitrary as the weather	arbitrary decision, arbitrary rule
devoid	adj	/dɪˈvɔɪd/	Thiếu vắng, không có	AI systems, devoid of such biological factors	devoid of emotion, completely devoid
comprehensively	adv	/ˌkɑːmprɪˈhensɪvli/	Một cách toàn diện	synthesize legal precedent comprehensively	comprehensively review, comprehensively cover
diligent	adj	/ˈdɪlɪdʒənt/	Siêng năng, cần mẫn	even the most diligent human judge	diligent worker, diligent student
streamline	v	/ˈstriːmlaɪn/	Tinh giản, tối ưu hóa	AI could potentially streamline certain decisions	streamline process, streamline operations
nuanced	adj	/ˈnuːɑːnst/	Tinh tế, phức tạp	requires nuanced understanding	nuanced approach, nuanced view
extenuating	adj	/ɪkˈstenjueɪtɪŋ/	Giảm nhẹ (tội lỗi)	consider extenuating circumstances	extenuating circumstances, extenuating factors
recidivism	n	/rɪˈsɪdɪvɪzəm/	Tái phạm	predict recidivism rates	recidivism rate, reduce recidivism
disproportionately	adv	/ˌdɪsprəˈpɔːrʃənətli/	Không cân xứng, quá mức	disproportionately flag African American defendants	disproportionately affect, disproportionately impact
entrench	v	/ɪnˈtrentʃ/	Khắc sâu, củng cố	could entrench existing injustices	deeply entrenched, entrench position

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
inexorable	adj	/ɪnˈeksərəbl/	Không thể ngăn cản	The inexorable integration of AI	inexorable rise, inexorable decline
necessitate	v	/nəˈsesɪteɪt/	Đòi hỏi, cần thiết	necessitates a fundamental reconceptualization	necessitate change, necessitate action
constellation	n	/ˌkɑːnstəˈleɪʃn/	Chòm sao, tập hợp	confronts a constellation of challenges	constellation of factors, constellation of issues
transcend	v	/trænˈsend/	Vượt qua, siêu việt	challenges that transcend mere technological adaptation	transcend boundaries, transcend limitations
promulgate	v	/ˈprɑːmlɡeɪt/	Ban hành, công bố	legal ethics codes, promulgated in an era	promulgate law, promulgate regulation
predicate	v	/ˈpredɪkeɪt/	Dựa trên, lập cơ sở	predicate themselves on principles	predicate on assumption, predicate on belief
fiduciary duty	n phrase	/fɪˈduːʃieri ˈduːti/	Nghĩa vụ tín thác	principles of fiduciary duty	fiduciary duty breach, fiduciary duty obligation
locus	n	/ˈloʊkəs/	Trung tâm, điểm tập trung	serves as the locus of decision-making	locus of control, locus of power
malpractice	n	/mælˈpræktɪs/	Hành vi sai trái nghề nghiệp	legal responsibility for such malpractice	medical malpractice, malpractice lawsuit
attribution	n	/ˌætrɪˈbjuːʃn/	Sự quy cho	the attribution of responsibility	attribution of blame, causal attribution
grapple	v	/ˈɡræpl/	Vật lộn, đối mặt	Jurisdictions worldwide are grappling with	grapple with problem, grapple with issue
stringent	adj	/ˈstrɪndʒənt/	Nghiêm ngặt	impose stringent requirements	stringent measures, stringent rules
precautionary	adj	/prɪˈkɔːʃəneri/	Phòng ngừa	embodies a precautionary approach	precautionary measure, precautionary principle
fragmented	adj	/ˈfræɡmentɪd/	Phân mảnh, rời rạc	a more fragmented approach	fragmented market, fragmented system
disparate	adj	/ˈdɪspərət/	Khác biệt, không đồng nhất	developing disparate rules	disparate elements, disparate groups
opacity	n	/oʊˈpæsəti/	Sự mờ đục, không rõ ràng	This opacity conflicts sharply	opacity of process, lack of opacity
adversarial	adj	/ˌædvərˈseriəl/	Đối nghịch, thù địch	the adversarial nature of legal proceedings	adversarial system, adversarial relationship
insidious	adj	/ɪnˈsɪdiəs/	Ngấm ngầm, âm hiểm	the most insidious threat	insidious nature, insidious influence
mitigate	v	/ˈmɪtɪɡeɪt/	Giảm nhẹ, làm dịu	Mitigating algorithmic bias	mitigate risk, mitigate damage
normative	adj	/ˈnɔːrmətɪv/	Chuẩn mực, quy phạm	involves normative judgments	normative framework, normative standard
post-deployment	adj	/poʊst dɪˈplɔɪmənt/	Sau khi triển khai	Post-deployment monitoring	post-deployment testing, post-deployment phase
incursion	n	/ɪnˈkɜːrʒn/	Sự xâm nhập	preserved from algorithmic incursion	military incursion, incursion into territory
romanticize	v	/roʊˈmæntɪsaɪz/	Lý tưởng hóa	this position romanticizes human decision-making	romanticize past, romanticize notion
augment	v	/ɔːɡˈment/	Tăng cường, bổ sung	designed to augment their capabilities	augment income, augment workforce

Kết Bài

Chủ đề về cách AI đang chuyển đổi ngành luật (How is AI transforming the legal industry) không chỉ là một nội dung thú vị trong IELTS Reading mà còn phản ánh xu hướng công nghệ quan trọng của thế giới hiện đại. Qua ba passages với độ khó tăng dần, bạn đã được trải nghiệm một bài thi hoàn chỉnh từ mức Easy (Band 5.0-6.5) đến Hard (Band 7.0-9.0), giúp bạn làm quen với cấu trúc và yêu cầu của kỳ thi thực tế.

Bộ 40 câu hỏi đa dạng bao gồm Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion, Matching Features, và Short-answer Questions đã cung cấp cho bạn cơ hội luyện tập toàn diện các dạng bài trong IELTS Reading. Phần đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin, cách paraphrase, và lý do chọn đáp án sẽ giúp bạn tự đánh giá năng lực và hiểu rõ phương pháp làm bài hiệu quả.

Bảng từ vựng quan trọng với hơn 40 từ và cụm từ học thuật, kèm phiên âm, nghĩa tiếng Việt, ví dụ ngữ cảnh, và collocations phổ biến là tài liệu quý giá để bạn nâng cao vốn từ vựng. Đây là những từ thường xuyên xuất hiện trong các đề thi IELTS Reading, đặc biệt với chủ đề công nghệ và pháp luật.

Hãy dành thời gian xem lại những câu trả lời sai, phân tích kỹ phần giải thích, và học thuộc các từ vựng mới. Với sự luyện tập đều đặn và phương pháp đúng đắn, bạn hoàn toàn có thể đạt được band điểm mục tiêu trong phần thi IELTS Reading. Chúc bạn học tập hiệu quả và thành công trong kỳ thi IELTS sắp tới!

What are You Looking For?

IELTS Reading: Trí Tuệ Nhân Tạo Trong Ngành Luật – Đề Thi Mẫu Có Đáp Án

Mở Bài