IELTS Reading: Các Thách Thức Trong Phát Triển AI Có Đạo Đức – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở bài

Chủ đề về Trí tuệ nhân tạo (AI) và đạo đức công nghệ đang là một trong những lĩnh vực được quan tâm hàng đầu trong các đề thi IELTS Reading gần đây. Câu hỏi “What Are The Challenges Of Ensuring Ethical AI Development?” (Những thách thức trong việc đảm bảo phát triển AI có đạo đức là gì?) không chỉ phản ánh xu hướng phát triển công nghệ toàn cầu mà còn thể hiện mối quan tâm về tác động xã hội của những tiến bộ khoa học hiện đại.

Trong bài viết này, bạn sẽ được trải nghiệm một đề thi IELTS Reading hoàn chỉnh với ba passages có độ khó tăng dần từ Easy đến Hard. Mỗi passage được thiết kế dựa trên format chuẩn Cambridge IELTS, bao gồm đủ 40 câu hỏi với các dạng bài đa dạng như Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion và nhiều dạng khác. Bài viết còn cung cấp đáp án chi tiết kèm giải thích cụ thể, từ vựng quan trọng với phiên âm và ví dụ, 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 chủ đề công nghệ – một topic thường xuyên xuất hiện trong IELTS Reading thực chiến.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút với tổng cộng 40 câu hỏi được chia đều qua 3 passages. Mỗi passage có độ dài khoảng 700-900 từ và độ khó tăng dần. Điểm số được tính dựa trên số câu trả lời đúng, không bị trừ điểm khi sai.

Phân bổ thời gian khuyến nghị:

• Passage 1 (Easy): 15-17 phút – Nội dung tương đối đơn giản, từ vựng cơ bản, thông tin rõ ràng
• Passage 2 (Medium): 18-20 phút – Yêu cầu hiểu sâu hơn, có paraphrase và suy luận
• Passage 3 (Hard): 23-25 phút – Nội dung học thuật, từ vựng chuyên ngành, cấu trúc câu phức tạp

Lưu ý quan trọng: Dành 2-3 phút cuối để chuyển đáp án vào Answer Sheet vì không có thời gian bổ sung sau khi hết giờ.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Câu hỏi trắc nghiệm nhiều lựa chọn
2. True/False/Not Given – Xác định thông tin đúng, sai hoặc không được nhắc đến
3. Matching Information – Ghép thông tin với đoạn văn tương ứng
4. Sentence Completion – Hoàn thành câu với từ trong bài
5. Matching Headings – Ghép tiêu đề với đoạn văn
6. Summary Completion – Hoàn thành đoạn tóm tắt
7. Short-answer Questions – Trả lời ngắn với giới hạn từ

2. IELTS Reading Practice Test

PASSAGE 1 – The Dawn of Ethical AI: Understanding the Basics

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Artificial Intelligence has become an integral part of modern life, influencing everything from the recommendations we see on social media to the diagnostic tools used in healthcare. As AI systems become more sophisticated and widespread, questions about their ethical development have moved from theoretical discussions to practical concerns that affect millions of people daily.

The concept of ethical AI development refers to the creation of artificial intelligence systems that are fair, transparent, and beneficial to humanity. This means ensuring that AI technologies do not discriminate against certain groups, that their decision-making processes can be understood and questioned, and that they are designed with human welfare as a primary consideration. However, achieving these goals is far more challenging than it might initially appear.

One of the fundamental challenges in ethical AI development is the issue of bias. AI systems learn from data, and if that data reflects existing societal prejudices, the AI will inevitably learn and perpetuate those biases. For example, if a recruitment algorithm is trained on historical hiring data from a company that has traditionally hired more men than women for technical positions, the AI might learn to favor male candidates over equally qualified female applicants. This creates a vicious cycle where existing inequalities are reinforced rather than addressed.

Transparency represents another significant hurdle. Many modern AI systems, particularly those based on deep learning, operate as “black boxes.” Even their creators cannot always explain exactly how they arrive at specific decisions. This lack of interpretability becomes particularly problematic in high-stakes situations such as medical diagnoses, loan applications, or criminal justice decisions. If a patient is denied treatment or a defendant receives a harsh sentence based on an AI recommendation, both the affected individuals and society at large have a right to understand the reasoning behind these decisions.

The challenge of accountability is closely related to transparency. When an AI system makes a mistake or causes harm, determining who should be held responsible is not always straightforward. Is it the programmers who wrote the code, the company that deployed the system, the datasets providers who supplied the training data, or the users who implemented the technology? This ambiguity can create situations where no one takes responsibility for AI-related problems, allowing harmful practices to continue unchecked.

Privacy concerns add another layer of complexity to ethical AI development. Many AI systems require vast amounts of personal data to function effectively. However, collecting, storing, and analyzing this data raises serious questions about individual privacy rights. The Cambridge Analytica scandal, where personal data from millions of Facebook users was harvested without consent for political advertising, demonstrated how easily AI-powered systems can violate privacy at an unprecedented scale. Balancing the data requirements of effective AI with the fundamental right to privacy remains an ongoing challenge.

Furthermore, there is the question of consent and autonomy. As AI systems become more pervasive, people increasingly interact with them without being fully aware of their presence or understanding how their data is being used. Smart home devices, for instance, constantly collect information about our daily routines, conversations, and preferences. While users technically consent to this data collection when they agree to terms of service, these agreements are often lengthy, complex, and rarely read or understood. This raises questions about whether such consent is truly informed or meaningful.

The rapid pace of AI development also creates challenges for regulatory frameworks. Technology evolves much faster than laws and regulations can be created and implemented. By the time policymakers understand a particular AI application well enough to regulate it effectively, the technology may have already advanced to new forms that raise different ethical questions. This regulatory lag means that many AI systems are deployed and widely used before adequate safeguards are in place.

Despite these challenges, there is growing recognition of the need for ethical AI development. Many technology companies have established ethics boards, researchers are developing new methods for detecting and mitigating bias, and governments are beginning to implement AI-specific regulations. Organizations like the Partnership on AI bring together companies, researchers, and civil society groups to establish best practices for responsible AI development. These efforts represent important steps toward ensuring that AI technology serves humanity’s best interests rather than undermining fundamental values and rights.

Questions 1-13

Questions 1-5: Multiple Choice

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

1. According to the passage, ethical AI development primarily focuses on:
   A) Making AI systems more powerful
   B) Creating fair and transparent AI systems
   C) Reducing the cost of AI technology
   D) Increasing the speed of AI processing

2. The recruitment algorithm example demonstrates:
   A) How AI can improve hiring processes
   B) The benefits of using historical data
   C) How biases can be perpetuated by AI
   D) The superiority of AI over human judgment

3. The term “black box” in the passage refers to:
   A) The physical appearance of AI systems
   B) AI systems whose decision-making is unclear
   C) Dangerous AI technologies
   D) Outdated AI methods

4. The Cambridge Analytica scandal is mentioned to illustrate:
   A) The benefits of data collection
   B) Successful AI applications
   C) Privacy violations through AI systems
   D) Effective political campaigns

5. According to the passage, regulatory lag occurs because:
   A) Policymakers lack interest in AI
   B) Technology develops faster than regulations
   C) Companies refuse to cooperate with regulations
   D) AI systems are too simple to regulate

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.

6. Deep learning systems can always explain their decision-making processes clearly.
7. The passage states that determining accountability for AI mistakes is straightforward.
8. Smart home devices are mentioned as examples of AI systems that collect personal data.
9. The Partnership on AI was established by the United Nations.

Questions 10-13: Sentence Completion

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

10. AI systems that discriminate might create a __ where inequalities continue.
11. Many users do not fully understand __ when agreeing to data collection by AI devices.
12. The rapid development of AI creates challenges for __ that try to regulate the technology.
13. Technology companies are establishing __ to address ethical concerns in AI development.

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PASSAGE 2 – The Technical and Social Dimensions of Ethical AI

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The pursuit of ethical artificial intelligence development represents a multifaceted challenge that extends far beyond mere technical adjustments to algorithms. Tương tự như Challenges of balancing economic growth and environmental protection, vấn đề phát triển AI có đạo đức đòi hỏi sự cân bằng tinh tế giữa nhiều yếu tố phức tạp. It encompasses fundamental questions about human values, societal structures, and the very nature of decision-making in an increasingly automated world. As AI systems become more deeply embedded in critical infrastructure and social institutions, the stakes of ensuring their ethical operation grow exponentially.

Algorithmic fairness stands as one of the most technically demanding aspects of ethical AI development. The challenge lies not simply in eliminating bias, but in defining what fairness means in different contexts. Consider a predictive policing system designed to help law enforcement allocate resources. Should the system treat all neighborhoods equally, directing the same level of attention to each? Or should it account for historical crime rates, potentially directing more resources to areas with higher past crime levels? The former approach might seem fairer on the surface, but it could leave high-crime areas underserved. The latter approach, however, risks creating a self-fulfilling prophecy where increased police presence in certain neighborhoods leads to more arrests, which the algorithm then interprets as validation of its predictions, perpetuating a cycle of disproportionate enforcement.

This dilemma illustrates a crucial point: fairness is not a single, objectively definable concept but rather a spectrum of potentially conflicting interpretations. Computer scientists have identified over twenty different mathematical definitions of algorithmic fairness, and research has shown that some of these definitions are mathematically incompatible – achieving one form of fairness necessarily precludes achieving another. This means that developers must make explicit value judgments about which conception of fairness to prioritize, and these decisions have profound implications for how AI systems affect different groups in society.

The opacity of advanced AI systems presents another substantial obstacle to ethical development. Modern machine learning models, particularly those based on neural networks with millions or billions of parameters, operate in ways that defy straightforward explanation. These systems identify complex patterns in data through processes that do not map neatly onto human logical reasoning. While researchers have developed various “explainable AI” techniques aimed at making these systems more interpretable, these methods have significant limitations. They often provide post-hoc rationalisations rather than true explanations of the model’s internal logic, and they can sometimes be misleading, suggesting simple causal relationships where the actual computations are far more convoluted.

This opacity becomes particularly problematic in adversarial contexts, where actors might deliberately attempt to exploit AI systems. Researchers have demonstrated that seemingly imperceptible changes to input data can cause AI systems to make drastically different predictions – a phenomenon known as “adversarial attacks.” An image recognition system might correctly identify a stop sign in normal conditions but misclassify it as a speed limit sign when strategically placed stickers are added. These vulnerabilities highlight how difficult it is to ensure that AI systems will behave safely and ethically across all possible scenarios, especially when their decision-making processes remain fundamentally opaque.

The economic dimensions of ethical AI development introduce yet another set of complications. Developing, training, and deploying sophisticated AI systems requires substantial computational resources and expertise, creating significant barriers to entry. This concentration of AI capability among a small number of well-resourced organizations – primarily large technology companies and wealthy nations – raises concerns about power imbalances and the democratisation of AI benefits. If only a few entities control advanced AI technology, they effectively control the values and priorities embedded in these systems, with limited accountability to the broader public.

Moreover, the competitive pressures in AI development can create perverse incentives that work against ethical considerations. Companies racing to bring products to market may shortchange ethical reviews or testing procedures. Researchers competing for publications and citations may focus on achieving state-of-the-art performance on benchmark tasks rather than addressing real-world ethical implications. Military and intelligence agencies developing AI for national security purposes may prioritise effectiveness over transparency or public accountability. These institutional pressures can systematically bias AI development away from ethical considerations, even when individual developers and researchers are genuinely concerned about ethical issues.

The cross-cultural dimension of ethical AI adds further complexity. Ethical principles and values vary significantly across different cultures and societies. Concepts of privacy, for example, differ markedly between individualistic Western societies and more collectivist Eastern cultures. Similarly, attitudes toward authority, personal autonomy, and the appropriate role of technology in human life vary substantially across cultural contexts. As AI systems are deployed globally, questions arise about whose values should be encoded in these systems. Should there be universal ethical standards for AI, or should systems be tailored to align with local cultural norms? Either approach presents challenges: universal standards may inadvertently impose one culture’s values on others, while culturally adaptive systems might perpetuate practices that some view as ethically problematic.

Looking forward, addressing these challenges will require sustained collaboration across multiple disciplines. Ethicists, social scientists, legal scholars, and policymakers must work alongside computer scientists and engineers to develop comprehensive frameworks for ethical AI development. This interdisciplinary approach must extend beyond academia to include stakeholder engagement with affected communities, ensuring that diverse voices and perspectives shape the development of AI technologies. Only through such holistic efforts can we hope to create AI systems that are not merely technically impressive but also genuinely aligned with human values and societal wellbeing.

Biểu đồ minh họa các thách thức kỹ thuật và xã hội trong phát triển trí tuệ nhân tạo có đạo đức

Questions 14-26

Questions 14-18: Yes/No/Not Given

Write YES if the statement agrees with the claims of the writer, NO if the statement contradicts the claims of the writer, or NOT GIVEN if it is impossible to say what the writer thinks about this.

14. The writer believes that eliminating bias from AI is the primary challenge in ethical AI development.
15. According to the writer, some mathematical definitions of fairness cannot coexist.
16. The writer suggests that explainable AI techniques provide complete understanding of AI decisions.
17. The writer implies that competitive pressures can negatively affect ethical AI development.
18. The writer thinks Western ethical standards should be universally applied to 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 impossibility of achieving perfect fairness
ii. Cultural differences in AI ethics
iii. Economic barriers to ethical AI access
iv. Security vulnerabilities in opaque systems
v. The paradox of predictive policing
vi. Multiple interpretations of fairness principles
vii. Competitive market forces affecting ethics
viii. The limitations of transparency efforts

19. Paragraph B (starting with “Algorithmic fairness stands…”)
20. Paragraph C (starting with “This dilemma illustrates…”)
21. Paragraph D (starting with “The opacity of advanced AI…”)
22. Paragraph E (starting with “This opacity becomes…”)
23. Paragraph F (starting with “The economic dimensions…”)

Questions 24-26: Summary Completion

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

Ethical AI development faces numerous interconnected challenges. While researchers have identified over twenty different 24. __ of algorithmic fairness, some are mathematically incompatible. The problem of AI opacity is worsened by 25. __, where minor input changes cause major prediction errors. Additionally, 26. __** in AI development may cause companies to prioritize speed over ethical considerations.

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PASSAGE 3 – Toward Comprehensive Frameworks for Ethical AI Governance

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The nascent field of AI ethics finds itself at a critical juncture, grappling with challenges that are simultaneously epistemological, technological, institutional, and normative. Trong bối cảnh này, việc tìm hiểu How is blockchain technology influencing global economic development? có thể cung cấp những góc nhìn bổ ích về cách các công nghệ mới ảnh hưởng đến cấu trúc xã hội và kinh tế toàn cầu. As artificial intelligence systems transcend their origins as specialized tools to become ubiquitous infrastructure underpinning critical societal functions, the imperative for robust ethical frameworks becomes increasingly acute. However, the development of such frameworks is hampered by fundamental uncertainties about the nature of intelligence itself, the trajectory of technological advancement, and the appropriate locus of governance authority in an interconnected global system.

Epistemic challenges constitute a particularly insidious barrier to ethical AI development. The very metrics by which we evaluate AI systems often embody unstated assumptions about human cognition and values. Consider the concept of “superintelligence” – artificial intelligence that surpasses human cognitive abilities across all domains. The prevailing discourse around superintelligence often assumes that intelligence is a unidimensional trait that can be meaningfully compared across radically different substrates (biological versus silicon-based). This assumption, however, rests on contentious philosophical grounds. Human intelligence is profoundly embodied, shaped by evolutionary pressures, social contexts, and phenomenological experiences that AI systems do not share. The question of whether artificial systems can truly possess “intelligence” analogous to human cognition, rather than merely exhibiting functionally equivalent behaviours, remains philosophically contested. This conceptual ambiguity permeates discussions of AI ethics, creating foundational uncertainties about what ethical obligations we owe to AI systems and what harms they might inflict.

The temporal dimension of AI ethics introduces additional complexities that are insufficiently addressed in current frameworks. AI systems develop and change over time through continuous learning from new data, potentially drifting from their original specifications and ethical alignments. A system that operates ethically when initially deployed might gradually develop problematic behaviours as it encounters edge cases, adversarial inputs, or distribution shifts in its operational environment. This phenomenon, termed “concept drift” in machine learning literature, creates a moving target for ethical oversight. Traditional regulatory paradigms, which typically involve pre-deployment certification followed by post-deployment monitoring, are ill-suited to technologies that fundamentally transform themselves during operation. The challenge is compounded by the fact that these changes often occur gradually and imperceptibly, making it difficult to identify the precise moment when a system transitions from acceptable to problematic behaviour.

Institutional structures for AI governance face the daunting task of coordinating across multiple levels of jurisdiction and expertise. AI development spans private corporations, academic institutions, government agencies, and international organizations, each operating under different incentive structures, accountability mechanisms, and regulatory regimes. A patchwork of regulations – data protection laws like Europe’s GDPR, sector-specific rules for medical or financial AI, voluntary industry standards, and academic ethical guidelines – creates a complex and sometimes contradictory landscape that developers must navigate. This fragmentation not only imposes compliance burdens but also creates opportunities for regulatory arbitrage, where organizations can exploit jurisdictional differences to avoid stringent ethical requirements. The transnational nature of AI development further complicates governance, as systems developed in one country with lax regulations may be deployed globally, affecting populations in jurisdictions with more rigorous standards.

The challenge of ex ante versus ex post regulation exemplifies the temporal misalignment between AI development and governance. Proactive regulation attempts to anticipate and prevent potential harms before they occur, but this requires prescient understanding of technologies whose trajectories are inherently uncertain. Reactive regulation responds to observed harms but may come too late to prevent significant damage and may inadvertently stifle beneficial innovation through overly cautious restrictions. Striking the appropriate balance requires regulatory mechanisms that are both adaptive and anticipatory – capable of responding to emerging risks while avoiding both the Scylla of under-regulation and the Charybdis of innovation-hampering over-regulation. Some scholars have proposed “regulatory sandboxes” where AI systems can be tested under controlled conditions with temporary exemptions from certain regulations, allowing iterative refinement of both technology and rules. However, such approaches carry risks of regulatory capture, where industry interests unduly influence the evolution of standards.

Value alignment – ensuring that AI systems’ objectives and behaviours cohere with human values – presents what may be the most philosophically profound challenge in AI ethics. The problem extends beyond merely encoding ethical rules into AI systems. Human values are often implicit, context-dependent, and mutually contradictory. They evolve over time and vary across individuals and cultures. Moreover, many important values resist explicit formalization. How does one encode concepts like human dignity, fairness, or respect into mathematical objectives that an AI system can optimize? The inverse reinforcement learning approach attempts to infer human values from observed behaviour, but this method assumes that human behaviour reliably reflects human values – an assumption that is empirically questionable given phenomena like akrasia (acting against one’s better judgment) and the influence of cognitive biases.

The “value loading problem” is further exacerbated by normative uncertainty – disagreement about what values should guide AI behaviour. Different ethical frameworks yield different prescriptions: utilitarian approaches might favour outcomes that maximize aggregate welfare even at the cost of individual rights, while deontological frameworks prioritise adherence to moral rules regardless of consequences. Virtue ethics focuses on cultivating desirable character traits rather than following rules or optimizing outcomes. Pluralistic societies contain adherents of all these frameworks and more, raising the question of whose ethics should be embedded in AI systems. Democratic processes might seem to offer a solution, but majoritarian decision-making can perpetuate the marginalization of minority perspectives. Furthermore, some ethical principles – such as basic human rights – are often considered to transcend democratic approval, existing as inviolable constraints even if a majority disagrees.

Technological affordances themselves shape ethical possibilities in ways that demand careful consideration. The architectures we choose for AI systems – centralized versus distributed, opaque versus interpretable, general-purpose versus domain-specific – carry normative implications. Centralized systems may offer greater coherence and control but concentrate power and create single points of failure. Interpretable systems may be accountable but potentially less performant than opaque alternatives. These trade-offs mean that ethical AI development requires not merely constraining technological choices through external regulation but reconceiving the very design space of AI systems to foreground ethical considerations from the outset. This approach, sometimes called “values in design” or “ethics by design**,” requires deep integration of ethical reasoning into technical development rather than treating ethics as an external constraint.

The path forward necessitates a paradigmatic shift in how we approach AI development – from a primarily techno-centric model focused on capability advancement to a sociotechnical framework that situates AI systems within their broader social, political, and ethical contexts. This requires cultivating interdisciplinary expertise, developing adaptive governance mechanisms, fostering inclusive stakeholder participation, and maintaining epistemic humility about the limits of our ability to predict and control complex technological systems. The challenges are formidable, but the stakes – nothing less than the trajectory of technologically mediated human civilization – could hardly be higher.

Sơ đồ khung quản trị và giám sát phát triển trí tuệ nhân tạo có đạo đức toàn diện

Questions 27-40

Questions 27-31: Multiple Choice

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

27. According to the passage, the concept of superintelligence is problematic because:
    A) It is too expensive to develop
    B) It assumes intelligence is a single measurable trait
    C) It has already been achieved
    D) It contradicts evolutionary theory

28. The term “concept drift” refers to:
    A) Changes in AI systems over time during operation
    B) The initial design phase of AI systems
    C) Disagreements among AI developers
    D) Cultural differences in AI understanding

29. Regulatory arbitrage in AI development occurs when:
    A) Organizations negotiate better regulations
    B) Governments create unified global standards
    C) Organizations exploit differences between jurisdictions
    D) AI systems regulate themselves

30. The “value loading problem” is challenging because:
    A) Human values are consistent across cultures
    B) Values are often implicit and contradictory
    C) AI systems reject human values
    D) Values can be easily encoded mathematically

31. The passage suggests that ethical AI development requires:
    A) Focusing solely on technical improvements
    B) Abandoning AI development entirely
    C) Integrating ethics from the design stage
    D) Prioritizing performance over accountability

Questions 32-36: Matching Features

Match each concept (32-36) with the correct description (A-H).

Concepts:
32. Epistemic challenges
33. Regulatory sandboxes
34. Inverse reinforcement learning
35. Value alignment
36. Ethics by design

Descriptions:
A) Testing environments with temporary regulatory exemptions
B) Ensuring AI objectives match human values
C) Fundamental uncertainties about intelligence and cognition
D) Democratic decision-making about AI
E) Inferring human values from observed behaviour
F) Integrating ethical considerations into technical development
G) Post-deployment monitoring systems
H) Centralized AI governance structures

Questions 37-40: Short-answer Questions

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

37. What philosophical approach focuses on maximizing overall welfare?
38. What type of framework does the passage suggest is needed instead of a techno-centric model?
39. What term describes acting against one’s better judgment?
40. What quality does the passage say is necessary regarding our ability to predict technological systems?

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. B
6. FALSE
7. FALSE
8. TRUE
9. NOT GIVEN
10. vicious cycle
11. terms of service
12. regulatory frameworks
13. ethics boards

PASSAGE 2: Questions 14-26

14. NO
15. YES
16. NO
17. YES
18. NOT GIVEN
19. v
20. vi
21. viii
22. iv
23. iii
24. mathematical definitions
25. adversarial attacks
26. competitive pressures

PASSAGE 3: Questions 27-40

27. B
28. A
29. C
30. B
31. C
32. C
33. A
34. E
35. B
36. F
37. utilitarian approaches
38. sociotechnical framework
39. akrasia
40. epistemic humility

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: ethical AI development, primarily focuses
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Bài đọc nêu rõ “The concept of ethical AI development refers to the creation of artificial intelligence systems that are fair, transparent, and beneficial to humanity.” Đây chính là paraphrase của đáp án B “Creating fair and transparent AI systems.”

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: recruitment algorithm example, demonstrates
• Vị trí trong bài: Đoạn 3, dòng 4-7
• Giải thích: Ví dụ về thuật toán tuyển dụng được đưa ra để minh họa cách AI có thể học và “perpetuate those biases” (duy trì những thành kiến). Điều này tương ứng với đáp án C.

Câu 3: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: black box, refers to
• Vị trí trong bài: Đoạn 4, dòng 2-4
• Giải thích: Thuật ngữ “black boxes” được giải thích là các hệ thống mà “Even their creators cannot always explain exactly how they arrive at specific decisions,” nghĩa là quy trình ra quyết định không rõ ràng.

Câu 6: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Deep learning systems, explain, decision-making processes, clearly
• Vị trí trong bài: Đoạn 4, dòng 2-4
• Giải thích: Bài viết nói rõ rằng ngay cả người tạo ra các hệ thống deep learning cũng không thể giải thích cách chúng đưa ra quyết định, mâu thuẫn trực tiếp với câu phát biểu.

Câu 8: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Smart home devices, collect personal data
• Vị trí trong bài: Đoạn 7, dòng 3-5
• Giải thích: Bài viết đề cập rõ “Smart home devices… constantly collect information about our daily routines, conversations, and preferences.”

Câu 10: vicious cycle

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI, discriminate, inequalities continue
• Vị trí trong bài: Đoạn 3, dòng cuối
• Giải thích: Bài viết sử dụng cụm từ chính xác “vicious cycle” để mô tả tình huống mà sự bất bình đẳng được củng cố thay vì được giải quyết.

Câu 12: regulatory frameworks

• Dạng câu hỏi: Sentence Completion
• Từ khóa: rapid development, challenges, regulate
• Vị trí trong bài: Đoạn 8, dòng 1-2
• Giải thích: “The rapid pace of AI development also creates challenges for regulatory frameworks” khớp chính xác với câu hỏi.

Passage 2 – Giải Thích

Câu 14: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: eliminating bias, primary challenge
• Vị trí trong bài: Đoạn 2, dòng 1-2
• Giải thích: Tác giả nói “The challenge lies not simply in eliminating bias, but in defining what fairness means” – cho thấy đây không phải là thách thức chính duy nhất, mâu thuẫn với quan điểm trong câu hỏi.

Câu 15: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: mathematical definitions, fairness, cannot coexist
• Vị trí trong bài: Đoạn 3, dòng 4-6
• Giải thích: Bài viết nêu rõ “some of these definitions are mathematically incompatible – achieving one form of fairness necessarily precludes achieving another.”

Câu 16: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: explainable AI, complete understanding
• Vị trí trong bài: Đoạn 4, dòng 6-9
• Giải thích: Tác giả chỉ ra rằng các kỹ thuật explainable AI có “significant limitations” và cung cấp “post-hoc rationalisations rather than true explanations.”

Câu 19: v (The paradox of predictive policing)

• Dạng câu hỏi: Matching Headings
• Vị trí: Đoạn B
• Giải thích: Đoạn này thảo luận về hệ thống dự đoán tội phạm và nghịch lý về cách xác định công bằng trong việc phân bổ nguồn lực cảnh sát.

Câu 20: vi (Multiple interpretations of fairness principles)

• Dạng câu hỏi: Matching Headings
• Vị trí: Đoạn C
• Giải thích: Đoạn này tập trung vào ý tưởng rằng công bằng không phải là một khái niệm duy nhất mà có hơn 20 định nghĩa toán học khác nhau.

Câu 24: mathematical definitions

• Dạng câu hỏi: Summary Completion
• Vị trí trong bài: Đoạn 3, dòng 3-4
• Giải thích: “Computer scientists have identified over twenty different mathematical definitions of algorithmic fairness” là cụm từ chính xác cần điền.

Câu 25: adversarial attacks

• Dạng câu hỏi: Summary Completion
• Vị trí trong bài: Đoạn 5, dòng 3-5
• Giải thích: Bài viết mô tả “adversarial attacks” là hiện tượng mà những thay đổi nhỏ trong dữ liệu đầu vào gây ra những dự đoán hoàn toàn khác biệt.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: superintelligence, problematic
• Vị trí trong bài: Đoạn 2, dòng 4-8
• Giải thích: Bài viết chỉ ra rằng khái niệm superintelligence có vấn đề vì “assumes that intelligence is a unidimensional trait that can be meaningfully compared” – giả định trí tuệ là một đặc điểm đơn chiều có thể đo lường.

Câu 28: A

• Dạng câu hỏi: Multiple Choice
• Từ khóa: concept drift, refers to
• Vị trí trong bài: Đoạn 3, dòng 2-5
• Giải thích: “Concept drift” được định nghĩa là hiện tượng hệ thống AI “develop and change over time through continuous learning,” tức thay đổi trong quá trình hoạt động.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: regulatory arbitrage, occurs when
• Vị trí trong bài: Đoạn 4, dòng 7-9
• Giải thích: Bài viết giải thích regulatory arbitrage là khi “organizations can exploit jurisdictional differences to avoid stringent ethical requirements.”

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: value loading problem, challenging
• Vị trí trong bài: Đoạn 7, dòng 1-4
• Giải thích: Vấn đề này khó khăn vì “Human values are often implicit, context-dependent, and mutually contradictory” – ngầm ẩn và mâu thuẫn lẫn nhau.

Câu 32: C (Epistemic challenges)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 2, toàn bộ
• Giải thích: Epistemic challenges được mô tả là những bất định cơ bản về bản chất của trí tuệ và nhận thức.

Câu 33: A (Regulatory sandboxes)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 5, dòng cuối
• Giải thích: Regulatory sandboxes được định nghĩa là môi trường thử nghiệm “with temporary exemptions from certain regulations.”

Câu 37: utilitarian approaches

• Dạng câu hỏi: Short-answer Questions
• Vị trí trong bài: Đoạn 7, dòng 5-6
• Giải thích: Bài viết nêu rõ “utilitarian approaches might favour outcomes that maximize aggregate welfare.”

Câu 40: epistemic humility

• Dạng câu hỏi: Short-answer Questions
• Vị trí trong bài: Đoạn cuối, dòng 2-4
• Giải thích: Tác giả nhấn mạnh cần “maintaining epistemic humility about the limits of our ability to predict and control complex technological systems.”

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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
integral	adj	/ˈɪntɪɡrəl/	không thể thiếu, thiết yếu	AI has become an integral part of modern life	integral part, integral component
sophisticated	adj	/səˈfɪstɪkeɪtɪd/	phức tạp, tinh vi	As AI systems become more sophisticated	sophisticated technology, sophisticated algorithm
discriminate	v	/dɪˈskrɪmɪneɪt/	phân biệt đối xử	AI technologies do not discriminate	discriminate against, discriminate between
perpetuate	v	/pəˈpetʃueɪt/	duy trì, kéo dài	The AI will perpetuate those biases	perpetuate inequality, perpetuate stereotypes
transparency	n	/trænsˈpærənsi/	tính minh bạch	Transparency represents another significant hurdle	lack of transparency, ensure transparency
accountability	n	/əˌkaʊntəˈbɪləti/	trách nhiệm giải trình	The challenge of accountability is closely related	hold accountability, ensure accountability
black box	n	/blæk bɒks/	hộp đen (không rõ cách hoạt động)	Many modern AI systems operate as black boxes	black box system, black box approach
vicious cycle	n	/ˈvɪʃəs ˈsaɪkl/	vòng luẩn quẩn	This creates a vicious cycle	break the vicious cycle, trapped in vicious cycle
consent	n	/kənˈsent/	sự đồng ý	The question of consent and autonomy	informed consent, give consent
pervasive	adj	/pəˈveɪsɪv/	lan tràn, phổ biến	As AI systems become more pervasive	pervasive technology, pervasive influence
regulatory lag	n	/ˈreɡjələtəri læɡ/	sự chậm trễ trong quy định	This regulatory lag means safeguards are not in place	address regulatory lag, overcome regulatory lag
mitigate	v	/ˈmɪtɪɡeɪt/	giảm nhẹ, làm dịu	Developing new methods for mitigating bias	mitigate risk, mitigate impact

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
multifaceted	adj	/ˌmʌltiˈfæsɪtɪd/	nhiều khía cạnh	The pursuit of ethical AI represents a multifaceted challenge	multifaceted problem, multifaceted approach
algorithmic	adj	/ˌælɡəˈrɪðmɪk/	thuộc về thuật toán	Algorithmic fairness stands as one of the most demanding aspects	algorithmic bias, algorithmic decision-making
self-fulfilling prophecy	n	/self fʊlˈfɪlɪŋ ˈprɒfəsi/	lời tiên tri tự ứng nghiệm	This risks creating a self-fulfilling prophecy	become a self-fulfilling prophecy
disproportionate	adj	/ˌdɪsprəˈpɔːʃənət/	không cân xứng	Perpetuating a cycle of disproportionate enforcement	disproportionate impact, disproportionate effect
incompatible	adj	/ˌɪnkəmˈpætəbl/	không tương thích	Some definitions are mathematically incompatible	incompatible with, fundamentally incompatible
opacity	n	/əʊˈpæsəti/	tính mờ đục, không rõ ràng	The opacity of advanced AI systems	opacity of algorithms, reduce opacity
adversarial	adj	/ˌædvəˈseəriəl/	thù địch, đối kháng	In adversarial contexts, actors might exploit AI	adversarial attack, adversarial example
imperceptible	adj	/ˌɪmpəˈseptəbl/	không thể nhận thấy	Seemingly imperceptible changes to input data	imperceptible difference, almost imperceptible
democratisation	n	/dɪˌmɒkrətaɪˈzeɪʃn/	dân chủ hóa	Concerns about democratisation of AI benefits	democratisation of technology, promote democratisation
perverse incentive	n	/pəˈvɜːs ɪnˈsentɪv/	động lực lệch lạc	Competitive pressures create perverse incentives	create perverse incentives, avoid perverse incentives
benchmark	n	/ˈbentʃmɑːk/	điểm chuẩn, tiêu chuẩn	Achieving state-of-the-art performance on benchmark tasks	industry benchmark, set a benchmark
collectivist	adj	/kəˈlektɪvɪst/	theo chủ nghĩa tập thể	More collectivist Eastern cultures	collectivist culture, collectivist society
inadvertently	adv	/ˌɪnədˈvɜːtəntli/	vô tình, không chủ ý	Universal standards may inadvertently impose values	inadvertently create, inadvertently cause
interdisciplinary	adj	/ˌɪntədɪsəˈplɪnəri/	liên ngành	This interdisciplinary approach must extend beyond academia	interdisciplinary research, interdisciplinary collaboration
holistic	adj	/həʊˈlɪstɪk/	toàn diện	Only through such holistic efforts can we succeed	holistic approach, holistic view

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
nascent	adj	/ˈnæsnt/	mới hình thành	The nascent field of AI ethics	nascent industry, nascent technology
juncture	n	/ˈdʒʌŋktʃə(r)/	thời điểm quan trọng	At a critical juncture	at this juncture, critical juncture
epistemological	adj	/ɪˌpɪstɪməˈlɒdʒɪkl/	thuộc về nhận thức luận	Challenges that are epistemological	epistemological question, epistemological foundation
ubiquitous	adj	/juːˈbɪkwɪtəs/	có mặt khắp nơi	To become ubiquitous infrastructure	ubiquitous technology, ubiquitous presence
insidious	adj	/ɪnˈsɪdiəs/	ngấm ngầm, âm hiểm	Epistemic challenges constitute a particularly insidious barrier	insidious effect, insidious threat
superintelligence	n	/ˌsuːpərɪnˈtelɪdʒəns/	siêu trí tuệ	The concept of superintelligence	artificial superintelligence, achieve superintelligence
substrate	n	/ˈsʌbstreɪt/	chất nền	Different substrates (biological versus silicon-based)	biological substrate, neural substrate
phenomenological	adj	/fɪˌnɒmɪnəˈlɒdʒɪkl/	thuộc về hiện tượng học	Phenomenological experiences that AI systems do not share	phenomenological approach, phenomenological experience
concept drift	n	/ˈkɒnsept drɪft/	trôi dạt khái niệm	This phenomenon, termed concept drift	detect concept drift, address concept drift
patchwork	n	/ˈpætʃwɜːk/	sự vá víu, ráp nối	A patchwork of regulations	patchwork of laws, regulatory patchwork
regulatory arbitrage	n	/ˈreɡjələtəri ˈɑːbɪtrɑːʒ/	lách luật quy định	Creates opportunities for regulatory arbitrage	engage in regulatory arbitrage, prevent regulatory arbitrage
ex ante	adj/adv	/eks ˈænti/	trước khi xảy ra	The challenge of ex ante versus ex post regulation	ex ante regulation, ex ante assessment
prescient	adj	/ˈpresiənt/	có tầm nhìn xa	This requires prescient understanding	prescient analysis, prescient prediction
Scylla and Charybdis	n	/ˈsɪlə ənd kəˈrɪbdɪs/	tiến thoái lưỡng nan	Between the Scylla of under-regulation and the Charybdis	navigate between Scylla and Charybdis
akrasia	n	/əˈkreɪziə/	hành động trái với phán đoán tốt	Given phenomena like akrasia	suffer from akrasia, concept of akrasia
deontological	adj	/ˌdiːɒntəˈlɒdʒɪkl/	thuộc về đạo đức luận nghĩa vụ	Deontological frameworks prioritise adherence to moral rules	deontological ethics, deontological approach
inviolable	adj	/ɪnˈvaɪələbl/	không thể vi phạm	Existing as inviolable constraints	inviolable rights, inviolable principles
affordance	n	/əˈfɔːdns/	khả năng cung cấp	Technological affordances themselves shape ethical possibilities	design affordances, technological affordances
epistemic humility	n	/ɪˈpɪstɪmɪk hjuːˈmɪləti/	khiêm tốn nhận thức	Maintaining epistemic humility about our limits	practice epistemic humility, cultivate epistemic humility

Bảng từ vựng quan trọng theo chủ đề phát triển AI có đạo đức cho IELTS Reading

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Kết bài

Chủ đề “What are the challenges of ensuring ethical AI development?” không chỉ là một đề tài nóng hổi trong thế giới công nghệ mà còn là nội dung thường xuyên xuất hiện trong bài thi IELTS Reading. Qua ba passages với độ khó tăng dần từ Easy đến Hard, bạn đã được trải nghiệm một bài thi hoàn chỉnh với 40 câu hỏi đa dạng dạng, từ Multiple Choice, True/False/Not Given, đến Matching Headings và Summary Completion.

Passage 1 giới thiệu những khái niệm cơ bản về phát triển AI có đạo đức như bias, transparency, và accountability. Passage 2 đi sâu vào các khía cạnh kỹ thuật và xã hội phức tạp hơn như algorithmic fairness và adversarial attacks. Passage 3 khám phá những thách thức triết học sâu sắc về superintelligence, value alignment, và regulatory frameworks.

Đáp án chi tiết kèm giải thích cụ thể giúp bạn hiểu rõ tại sao một đáp án đúng và cách paraphrase được sử dụng giữa câu hỏi và passage. Bảng từ vựng với hơn 40 từ quan trọng, bao gồm phiên âm, nghĩa tiếng Việt, ví dụ và collocations, sẽ giúp bạn nâng cao vốn từ vựng học thuật đáng kể.

Hãy sử dụng đề thi này như một công cụ luyện tập thực chiến. Làm bài trong điều kiện thi thật (60 phút không gián đoạn), sau đó đối chiếu đáp án và đọc kỹ phần giải thích để hiểu sâu hơn. Học từ vựng theo ngữ cảnh và thực hành paraphrasing – đây là kỹ năng then chốt để đạt band điểm cao trong IELTS Reading.