Summarize Written Text: Ethical Concerns in AI-Driven Decision-Making

The Summarize Written Text section is a crucial component of the Speaking & Writing exam in the Pearson Test of English (PTE). This part assesses your ability to grasp the core idea of a passage and summarize it into a single sentence. An increasingly common topic in this section relates to ethical issues in AI-driven decision-making, given its relevance in modern technology. Below, we will provide you with sample questions alongside answers, aiming to offer a practical approach for your PTE preparation.

Sample PTE Summarize Written Text Task 1: Ethical Concerns in AI-Driven Decision-Making

Artificial Intelligence (AI) has transformed various industries by accelerating the decision-making process. However, it presents ethical dilemmas, particularly in sectors like healthcare and criminal justice, where AI systems make critical decisions. For instance, in the domain of healthcare, AI has enhanced diagnostic capabilities by analyzing medical data with impressive speed but also makes opaque decisions, often without sufficient justification. Similarly, in the legal system, AI algorithms are used to predict recidivism, which raises concerns about fairness and bias, as there is an inherent risk of perpetuating racial or social biases embedded in the training data. These examples highlight the importance of ethical scrutiny surrounding AI’s decision-making capabilities and the need to integrate transparency and accountability into its applications to avoid deep-seated concerns about bias and inequality.

Your task: Summarize the text in one sentence as accurately as possible.

Answer 1: Band 90

While AI has improved efficiency in industries like healthcare and criminal justice, the lack of transparency and potential biases inherent in its decision-making processes raise significant ethical concerns, necessitating careful scrutiny and the promotion of accountability.

Analysis:

• Content: The core idea of the original passage is fully captured.
• Form: Correct word count (under 75 words).
• Grammar: No grammatical errors.
• Vocabulary: Appropriate use of complex vocabulary (e.g., “perpetuating,” “inherent,” “scrutiny”).
• Spelling: No errors.

Answer 2: Band 65

AI has made processes in healthcare and criminal justice more efficient, but it has ethical issues due to decisions being biased or opaque.

Analysis:

• Content: Key facts are mentioned, but the summary misses out on elements like accountability and transparency, which are strongly emphasized in the original text.
• Form: Within word limit.
• Grammar: Sentence is grammatically correct, but slightly simplified in structure.
• Vocabulary: Simplified vocabulary, with words like “biased” and “opaque” used correctly but lacking the depth of vocabulary seen in higher bands.
• Spelling: No errors.

Answer 3: Band 50

AI systems help in healthcare and court, though, they might be unclear.

Analysis:

• Content: Vague and incomplete, missing the detailed ethical dilemmas, such as fairness and bias.
• Form: Within word count but quite brief.
• Grammar: Some awkward phrasing in “help in healthcare and court.”
• Vocabulary: Very basic vocabulary, resulting in a lack of precision.
• Spelling: No errors, but the response doesn’t convey the complexity of the original passage.

Important Vocabulary in Summarize Written Text

Here are 10 important words and phrases from the task, which may appear difficult or unfamiliar:

1. Opaque /əˈpeɪk/ – Hard to understand or see through; not transparent.

   • Example: The opaque nature of AI decisions makes it challenging to evaluate them for fairness.

2. Perpetuating /pərˈpɛtʃuˌeɪtɪŋ/ – To cause something to continue indefinitely.

   • Example: AI systems perpetuating racial biases have raised significant ethical concerns.

3. Recidivism /rɪˈsɪdəˌvɪzəm/ – The tendency of a convicted criminal to reoffend.

   • Example: AI predictions regarding recidivism have come under scrutiny for being racially biased.

4. Scrutiny /ˈskruːtɪni/ – Critical observation or examination.

   • Example: The ethical use of AI should always be subject to scrutiny.

5. Bias /ˈbaɪəs/ – Prejudice in favor of or against one thing, person, or group.

   • Example: The data used to train AI models can introduce bias into the decision-making process.

6. Accountability /əˌkaʊn.təˈbɪl.ə.ti/ – The fact or condition of being accountable or responsible.

   • Example: There should be greater accountability for AI decisions that impact people’s lives.

7. Ethical /ˈɛθɪkəl/ – Related to moral principles or the rules of conduct.

   • Example: Ethical concerns must be at the forefront of AI development, especially in critical fields like law and medicine.

8. Transparency /trænsˈpærənsi/ – The practice of operating in a way that is easy to see through and understand.

   • Example: Transparency in AI decision-making processes is crucial to combat hidden biases.

9. Inequality /ˌɪnɪˈkwɒləti/ – The quality of being unequal or uneven.

   • Example: AI systems may exacerbate inequality if programmed without accounting for diverse data sources.

10. Diagnostic /ˌdaɪəgˈnɒstɪk/ – Related to diagnosis, especially in medical contexts.

    • Example: AI’s diagnostic capabilities have been revolutionary in processing health data.

Conclusion

Ethical Concerns In AI-driven Decision-making are a critical topic in the PTE Summarize Written Text section due to their practical relevance and real-world implications. The refined summaries provided above exemplify the type of answer needed to achieve desirable scores in this section. As this topic continues to evolve, expect it to remain widely used in PTE exams, making it essential for test-takers to familiarize themselves thoroughly. For additional high-quality examples and practice tasks relating to Automation in healthcare and efficiency and related topics, keep exploring the PTE Online platform.