Summarize Written Text in PTE: Data Ethics in Artificial Intelligence

Artificial Intelligence (AI) has become an integral form of technology that revolutionizes industries like healthcare, law enforcement, and finance. However, its increasing growth raises ethical concerns, most notably regarding data usage. These issues commonly appear in the PTE exam, especially in the Summarize Written Text (SWT) section, part of the Speaking & Writing test. In this article, you’ll find several practice prompts and high-quality model answers, helping you sharpen your skills and improve your understanding of Data Ethics In Artificial Intelligence.

Practice Summarize Written Text Prompt 1

Artificial intelligence (AI) is widely utilized across multiple sectors including healthcare, finance, and surveillance systems. While AI enhances productivity and decision-making processes, its usage also triggers critical ethical concerns mainly associated with data privacy, bias, and accountability. Personal data is often acquired without explicit user consent, making it vulnerable to misuse. Moreover, AI systems may inherit prejudices present in the datasets they are trained on, amplifying existing inequalities. Given that these technologies are handling sensitive tasks such as predictive policing and healthcare recommendations, strict regulatory frameworks need to be enforced to ensure ethical development and usage.

In one sentence, summarize the text.

Model Answers

High Score (Band 75-90):
Ethical concerns related to artificial intelligence revolve around data privacy, bias, and accountability, which are addressed by implementing stricter regulatory frameworks, particularly in sensitive areas like policing and healthcare.

Analysis:

• Content: Provides an accurate and concise summary.
• Form: Exactly one sentence.
• Grammar: Correct use of complex structures such as participial phrases.
• Vocabulary: Uses advanced vocabulary and technical terms such as “predictive policing” and “regulatory frameworks.”
• Spelling: No errors.

Mid Score (Band 50-65):
AI faces ethical problems like data privacy and bias, hence regulatory measures are needed in certain industries like policing and healthcare.

Analysis:

• Content: Main ideas are present but less detail is included.
• Form: One complete sentence.
• Grammar: Simple structure, no errors.
• Vocabulary: Adequate but less complex (“problems” instead of “concerns”).
• Spelling: No issues.

Low Score (Band 30-45):
Artificial intelligence is used in healthcare and policing but may have ethical problems.

Analysis:

• Content: Misses essential ideas such as bias, privacy, and the need for regulation.
• Form: One sentence but overly simplistic.
• Grammar: Basic, no errors.
• Vocabulary: Lacks specific terms like “accountability” or “regulatory frameworks.”
• Spelling: No issues.

Practice Summarize Written Text Prompt 2

Artificial intelligence has proven to be a valuable tool in many sectors, including personalized healthcare. By analyzing large volumes of patient data, AI systems can identify patterns and recommend treatment plans tailored to individuals. However, this development presents new ethical dilemmas, such as patient consent, data ownership, and the accuracy of AI-driven medical advice. Informed patient consent becomes questionable when AI systems operate in the background with vast codes of algorithms, many times overlooked by healthcare providers and individuals alike.

In one sentence, summarize the text.

Model Answers

High Score (Band 75-90):
Though artificial intelligence offers personalized healthcare solutions by analyzing extensive patient data, it brings ethical challenges related to patient consent, data ownership, and the accuracy of AI-generated medical advice.

Analysis:

• Content: Incorporates all key points.
• Form: Single sentence following proper format.
• Grammar: Well-structured, no errors.
• Vocabulary: Uses appropriate terminologies such as “personalized healthcare” and “informed consent.”
• Spelling: No issues.

Mid Score (Band 50-65):
AI benefits healthcare by recommending treatments but creates ethical issues such as patient consent and data accuracy.

Analysis:

• Content: Covers some critical aspects but lacks depth.
• Form: One correct sentence.
• Grammar: Fair, no errors.
• Vocabulary: Simpler phrases (“benefits” vs. “offers solutions”), but still on point.
• Spelling: No errors.

Low Score (Band 30-45):
AI in healthcare recommends personal treatments but could face ethical problems.

Analysis:

• Content: Lacks detail – only vague references to issues.
• Form: Correct sentence length but lacks complexity.
• Grammar: Simplistic structure.
• Vocabulary: Basic and lacks specificity.
• Spelling: Accurate.

Artificial intelligence in personalized healthcare with data ethics concerns

Vocabulary and Grammar

Here is a list of important vocabulary from the texts, along with their meanings and examples of usage:

1. Prejudice /ˈprɛdʒʊdɪs/ (noun): Bias or preconceived opinion not based on reason or actual experience.
   Example: AI systems may amplify societal prejudices found in historical data.

2. Consent /kənˈsɛnt/ (noun): Permission for something to happen or agreement to do something.
   Example: Patients must provide informed consent before their data is used.

3. Regulatory /ˈrɛɡjʊlət(ə)ri/ (adjective): Relating to or serving a process of regulation or control.
   Example: AI systems require regulatory oversight to ensure ethical use.

4. Accountability /əˌkaʊntəˈbɪlɪti/ (noun): The fact or condition of being responsible.
   Example: There is a need for increased accountability in AI decision-making.

5. Inherited /ɪnˈhɛrɪtɪd/ (adjective): Derived genetically or from predecessors.
   Example: AI systems might inherit biases from the data they are trained on.

6. Surveillance /sə(ː)ˈveɪləns/ (noun): Close observation, especially of a suspected person.
   Example: AI plays a key role in modern surveillance systems, raising privacy concerns.

7. Ethical /ˈɛθɪk(ə)l/ (adjective): Relating to moral principles.
   Example: AI poses serious ethical challenges around data usage and bias.

8. Predictive /prɪˈdɪktɪv/ (adjective): Relating to prediction or foreseeing future events.
   Example: Predictive modeling in AI is used in both policing and healthcare.

9. Algorithm /ˈælɡəˌrɪðəm/ (noun): A process or a set of rules to be followed in calculations or problem-solving operations.
   Example: AI operates using complex algorithms to analyze vast amounts of data.

10. Bias /ˈbaɪəs/ (noun): Inclination or prejudice for or against one person or group, especially in a way considered to be unfair.
    Example: Bias in AI systems can perpetuate inequality if not addressed properly.

Conclusion

Summarizing written text in the PTE exam often involves dealing with intricate topics like data ethics in artificial intelligence, especially as AI integrates deeper into areas like healthcare and policing. By practicing with varied examples like those provided above, test-takers can become proficient at identifying critical points and structuring them into an effective one-sentence summary. Keep practicing, ask questions, and remember to focus on clarity and accuracy.

For a deeper dive into AI’s impact, see our articles on Artificial intelligence and ethical dilemmas and AI’s role in personalized healthcare.