In the Pearson Test of English (PTE), the Summarize Written Text section is a core part of the Speaking & Writing module. This task requires students to summarize a passage into one sentence containing between 5 and 75 words. One of the frequently addressed topics in this section is the Ethical Dilemmas In Artificial Intelligence due to its relevance in modern-day discussions.
Below, we explore a sample question on this topic and provide sample answers for different PTE scoring bands.
Summarize Written Text: Sample Task on Ethical Dilemmas in AI
Task: You will have to summarize the following text in one sentence. Make sure your summary contains between 5 and 75 words.
Sample Text:
The increasing implementation of artificial intelligence (AI) in decision-making processes across various sectors, such as healthcare, law enforcement, and finance, raises significant ethical concerns. The primary dilemma revolves around who is accountable when AI systems make flawed decisions. As AI becomes more autonomous, the role of human oversight diminishes, potentially leading to unchecked biases within the systems. Additionally, the lack of transparency in AI’s decision-making processes hinders efforts to detect and rectify these biases. Despite the potential for automation to bring about positive efficiencies, the paramount ethical question remains: Are we prepared to live with the consequences of decisions made by machines without human intervention?
Instruction: Summarize the text in one sentence.
Sample Answers and Band-Level Analysis
Band 90 Answer
The widespread use of artificial intelligence in sectors like healthcare and finance has led to ethical concerns regarding accountability, the diminishing role of human oversight, and the transparency of AI’s decision-making processes, especially due to the risk of embedded biases.
- Content: Fully addresses the text by including key ideas (ethical concerns, diminishing human oversight, AI bias).
- Form: Single sentence, grammatically correct, within word limits (43 words).
- Grammar: Perfect grammar usage—no issues with articles, punctuation, or sentence structure.
- Vocabulary: Wide range of relevant vocabulary (accountability, transparency, oversight).
- Spelling: No spelling errors.
Band 75 Answer
The use of AI in decision-making raises ethical questions about accountability, bias, and the diminishing role of human oversight in critical sectors like healthcare, while the lack of transparency in these systems further complicates ethical dilemmas.
- Content: The summary captures most of the critical content, but lacks mention of the importance of human intervention.
- Form: Well-formed sentence, with proper punctuation and fluency (35 words).
- Grammar: Solid grammatical structure.
- Vocabulary: Accurate but slightly less varied compared to Band 90.
- Spelling: No issues detected.
Band 65 Answer
AI raises ethical concerns, especially about bias and the need for human oversight in decision-making systems like healthcare and finance.
- Content: Covers the main ethical concerns, but misses out on the idea of transparency in decision-making.
- Form: Fulfills the format requirement; phrase structurally coherent (23 words).
- Grammar: Good sentence construction but relatively simple.
- Vocabulary: Somewhat limited range of words.
- Spelling: Error-free.
Band 50 Answer
AI has ethical issues like bias and lack of human oversight.
- Content: Only partially captures content—the key ideas like accountability and sector-specific applications are missing.
- Form: Single sentence but too short (10 words).
- Grammar: Basic, no complex constructs.
- Vocabulary: Limited range, lacks variety.
- Spelling: No issues.
Vocabulary and Grammar Breakdown
To further improve your performance in the Summarize Written Text section, it’s important to build a strong vocabulary and understand grammar usage related to ethical discussions in artificial intelligence:
-
Accountability /əˌkaʊn.təˈbɪl.ɪ.ti/: Responsibility for the outcomes, particularly important in ethical discussions about AI.
Example: The lack of accountability in AI systems can lead to disastrous consequences. -
Autonomy /ɔːˈtɒn.ə.mi/: The capacity for a system to act independently without human intervention.
Example: Fully autonomous systems present challenges in assigning ethical responsibility. -
Bias /ˈbaɪ.əs/: A prejudice or inclination towards a particular outcome, which can result in unfair decisions.
Example: AI systems may inherit biases from their underlying data sets. -
Transparency /trænˈspær.ən.si/: Openness or clarity about how decisions are made by AI systems.
Example: The lack of transparency in AI algorithms often leads to ethical concerns. -
Oversight /ˈoʊ.vər.saɪt/: The act of overseeing or supervising processes, especially in preventing errors.
Example: Human oversight is critical in mitigating the flaws of AI-driven decision-making systems. -
Sector /ˈsɛk.tər/: An area of the economy or a segment of society affected by AI, such as healthcare, law enforcement, etc.
Example: The healthcare sector benefits greatly from AI, but faces ethical dilemmas regarding accountability. -
Efficiency /ɪˈfɪʃ.ən.si/: Achieving maximum productivity with minimal wasted effort or expense, yet can bring ethical concerns.
Example: AI can enhance process efficiency but sometimes at the cost of fairness. -
Ethical Quandary /ˈɛθɪkəl ˈkwɒn.dri/: A moral dilemma involving difficult choices about right and wrong.
Example: Ethical quandaries arise when AI decisions impact human lives without human judgment. -
Automation /ˌɔː.təˈmeɪ.ʃən/: The use of technology to perform tasks without human intervention.
Example: Increased automation in the workforce is raising questions about the ethical treatment of displaced workers. -
Intervention /ˌɪn.təˈven.ʃən/: Involvement in a process to prevent an undesirable outcome.
Example: Human intervention is crucial for rectifying biases that AI systems might develop.
Conclusion
The above sample summarizations and vocabulary breakdowns will help deepen your understanding of how to tackle Summarize Written Text tasks effectively. The topic of ethical dilemmas in artificial intelligence is not only important for exams but also highly relevant in today’s fast-evolving technological landscape. Continuously practicing and focusing on key elements like content, grammar, and vocabulary will elevate your test performance.
For more insights into these dilemmas, such as their implications for data ethics in artificial intelligence, be sure to explore more detailed resources online. Engage with this content, and remember that AI in decision-making will continue to be a focal point in real-world discussions as well as in standardized tests.
Would you like to see more examples? Feel free to leave your thoughts in the comment section below!
