The IELTS Reading section is a crucial component of the test, assessing your ability to comprehend complex texts and extract relevant information. Today, we’ll focus on a topic that has gained significant attention in recent years: “Ethical Issues In AI-enhanced Law Enforcement.” This subject has appeared in various forms in past IELTS exams and, given its relevance in our increasingly technology-driven world, it’s likely to resurface in future tests.
Based on our analysis of previous IELTS Reading passages, topics related to artificial intelligence and ethics have shown a steady increase in frequency over the past decade. The intersection of AI and law enforcement, in particular, has become a hot-button issue, making it a prime candidate for future IELTS Reading texts.
Let’s dive into a practice passage on this topic, followed by questions and a detailed analysis to help you sharpen your IELTS Reading skills.
Practice Reading Passage
AI in Policing: Balancing Innovation and Ethics
Artificial Intelligence (AI) is revolutionizing various sectors, and law enforcement is no exception. Police departments worldwide are increasingly adopting AI-enhanced tools to improve efficiency and effectiveness in crime prevention and investigation. However, this technological advancement brings with it a host of ethical concerns that demand careful consideration.
One of the primary applications of AI in policing is predictive policing, which uses algorithms to analyze vast amounts of data to forecast potential crime hotspots. While this approach has shown promise in reducing crime rates in some areas, critics argue that it may perpetuate existing biases and lead to over-policing in certain communities, particularly those that are already marginalized.
Facial recognition technology is another contentious AI application in law enforcement. Its ability to quickly identify individuals from surveillance footage or photographs has proven valuable in solving crimes and locating missing persons. However, concerns about privacy infringement and the potential for misidentification have led some jurisdictions to ban or severely restrict its use.
AI-powered decision-making tools are also being employed to assist in bail and sentencing recommendations. Proponents argue that these systems can provide more consistent and objective assessments than human judges. However, there are worries that these algorithms may inadvertently discriminate against certain demographic groups due to biases in the historical data used to train them.
The use of AI in monitoring online activities for potential threats raises additional ethical questions. While it can help identify and prevent cyber crimes and terrorist activities, there are concerns about the infringement of civil liberties and the potential for abuse of such surveillance powers.
Transparency and accountability are crucial issues when it comes to AI in law enforcement. The complex nature of AI algorithms often makes it difficult for the public and even the officers using these tools to understand how decisions are made. This “black box” problem can undermine trust in the justice system and make it challenging to contest AI-assisted decisions.
As AI continues to evolve and integrate into law enforcement practices, it is essential to establish robust ethical guidelines and oversight mechanisms. This includes ensuring diverse representation in the development of AI systems, regular audits for bias, and clear policies on data collection and use.
The potential benefits of AI in enhancing public safety are significant, but they must be balanced against the need to protect individual rights and maintain public trust. As society grapples with these complex issues, the path forward will require ongoing dialogue between technologists, law enforcement agencies, policymakers, and the communities they serve.
AI in law enforcement
Questions
True/False/Not Given
- Predictive policing has been proven to reduce crime rates in all areas where it has been implemented.
- Some jurisdictions have completely banned the use of facial recognition technology in law enforcement.
- AI-powered decision-making tools for bail and sentencing are universally accepted as more objective than human judges.
- The use of AI in monitoring online activities is considered ethical by all experts in the field.
- The complexity of AI algorithms can make it difficult for the public to understand how decisions are made.
Multiple Choice
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According to the passage, which of the following is NOT mentioned as an application of AI in law enforcement?
A) Predictive policing
B) Facial recognition
C) Traffic management
D) Online activity monitoring -
The main concern with AI-powered decision-making tools in bail and sentencing is:
A) They are too expensive to implement
B) They may perpetuate biases against certain groups
C) They are not as accurate as human judges
D) They are too slow in processing information
Matching Headings
Match the following headings to the correct paragraphs in the passage. There are more headings than paragraphs, so you will not use all of them.
- Paragraph 2
- Paragraph 4
- Paragraph 6
Headings:
A) The challenge of algorithmic transparency
B) Predictive policing: A double-edged sword
C) AI’s role in cybercrime prevention
D) Balancing efficiency and fairness in judicial processes
E) The future of AI in law enforcement
F) Privacy concerns in facial recognition technology
Short Answer Questions
Answer the following questions using NO MORE THAN THREE WORDS from the passage for each answer.
- What type of communities are critics concerned may be disproportionately affected by predictive policing?
- In addition to solving crimes, what other positive use of facial recognition technology is mentioned?
- What term is used to describe the difficulty in understanding how AI algorithms make decisions?
Answer Key and Explanations
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False – The passage states that predictive policing “has shown promise in reducing crime rates in some areas,” not all areas.
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True – The passage mentions that “some jurisdictions to ban or severely restrict its use.”
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False – The passage states that “Proponents argue” this point, but also mentions concerns about potential discrimination.
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Not Given – The passage does not state that all experts consider this ethical; it mentions both benefits and concerns.
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True – The passage directly states this in the paragraph about transparency and accountability.
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C – Traffic management is not mentioned in the passage as an application of AI in law enforcement.
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B – The passage states that “there are worries that these algorithms may inadvertently discriminate against certain demographic groups.”
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B – This paragraph discusses predictive policing and its potential benefits and drawbacks.
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D – This paragraph talks about AI-powered decision-making tools in bail and sentencing, discussing the balance between efficiency and potential discrimination.
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A – This paragraph discusses the “black box” problem and the challenge of understanding how AI makes decisions.
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Marginalized communities
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Locating missing persons
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Black box
Common Mistakes to Avoid
- Overgeneralization: Be cautious about applying a statement made about some cases to all cases.
- Confusing arguments for facts: Pay attention to phrases like “critics argue” or “proponents claim,” which indicate opinions rather than established facts.
- Overlooking qualifiers: Words like “some,” “may,” and “can” are important in determining the exact meaning of a statement.
- Making assumptions: Stick to the information given in the passage and avoid drawing conclusions based on your own knowledge or opinions.
Key Vocabulary
- Ethical (adjective): /ˈeθɪkəl/ – relating to moral principles or the branch of knowledge dealing with these
- Artificial Intelligence (AI) (noun): /ˌɑːtɪˈfɪʃəl ɪnˈtelɪdʒəns/ – the theory and development of computer systems able to perform tasks normally requiring human intelligence
- Perpetuate (verb): /pəˈpetʃueɪt/ – make (something) continue indefinitely
- Contentious (adjective): /kənˈtenʃəs/ – causing or likely to cause an argument; controversial
- Infringement (noun): /ɪnˈfrɪndʒmənt/ – the action of breaking the terms of a law, agreement, etc.; violation
- Inadvertently (adverb): /ˌɪnədˈvɜːtəntli/ – without intention; accidentally
- Transparency (noun): /trænsˈpærənsi/ – the condition of being transparent or easily understood
Grammar Focus
Complex sentences with multiple clauses are common in IELTS Reading passages. For example:
“While this approach has shown promise in reducing crime rates in some areas, critics argue that it may perpetuate existing biases and lead to over-policing in certain communities, particularly those that are already marginalized.”
This sentence structure allows for the presentation of contrasting ideas:
- Main clause: “critics argue”
- Subordinate clause introduced by “while”: presents a contrasting idea
- That-clause: explains what critics argue
- Participle phrase: adds additional information
Practice identifying and breaking down such complex sentences to improve your comprehension of academic texts.
Tips for Success
- Time management is crucial. Spend about 20 minutes on each passage in the IELTS Reading test.
- Skim the passage quickly first to get a general idea of its content and structure.
- Read the questions carefully before looking for answers in the text.
- Use the headings and first sentences of paragraphs to guide you to the relevant information.
- Pay attention to transition words and phrases that indicate contrasts, examples, or conclusions.
- Practice regularly with a variety of texts on different topics to improve your reading speed and comprehension.
- Develop your vocabulary, especially in areas related to technology, ethics, and social issues, as these are common themes in IELTS Reading passages.
Remember, success in IELTS Reading comes from a combination of strong vocabulary, efficient reading strategies, and plenty of practice. Keep working on these skills, and you’ll see improvement in your performance. Good luck with your IELTS preparation!
