The integration of Artificial Intelligence (AI) into law enforcement practices has sparked a global debate on ethics, privacy, and accountability. From facial recognition systems in public spaces to predictive policing algorithms, AI is transforming how crimes are detected and prevented. However, the benefits of efficiency and data-driven decision-making are accompanied by concerns about fairness, bias, and the potential erosion of civil liberties. One of the most controversial applications is predictive policing, which uses historical crime data to forecast the times and places where crimes are likely to occur or who might commit them. Proponents argue that this allows police to allocate resources more efficiently and reduce crime rates. However, critics warn that such systems may reinforce existing societal biases. If past data reflect biased policing practices, such as over-policing in marginalized communities, then the algorithm may perpetuate these injustices by disproportionately targeting the same areas or groups. Facial Recognition Technology (FRT) is another AI-driven tool that has gained traction. While it has proven useful in identifying suspects, it raises serious concerns regarding surveillance and individual privacy. Studies have also shown that FRT is less accurate in identifying people of color and women, increasing the risk of false accusations and wrongful detentions.
The lack of transparency in how these systems operate further complicates the field of law and practice. Many AI tools used in law enforcement are developed by private companies that treat their algorithms as proprietary, meaning that even law enforcement officers may not fully understand how decisions are made by these AI tools. This is often referred to as the “Black Box” problem. This opaqueness undermines accountability, making it difficult to challenge wrongful predictions or decisions in a court of law. As AI continues to evolve, lawmakers and civil rights advocates are calling for stronger regulations, awareness that these technologies are used responsibly. Proposals include the mandatory auditing of algorithms, public disclosure of data sources, and legal safeguards to protect against discrimination. Without such measures, the unchecked use of AI could lead to a justice system that prioritizes efficiency over equity, ultimately compromising democratic values and rule of law. AI holds the promise of revolutionizing law enforcement, its application must be guided by ethical frameworks that prioritize human rights, transparency, and fairness. Otherwise, technology intended to protect society may end up harming the very individuals it seeks to serve.
What is the central concern raised in the passage regarding AI in law enforcement?
Show Hint
- Replacing human officers
- High operational costs
- Reinforcing bias and reducing fairness
- Inability to analyze real-time data
The Correct Option is C
Solution and Explanation
- Step 2: Understanding the focus of the passage — The main issue raised in the passage is the risk of AI systems perpetuating existing biases, particularly against marginalized communities, and the potential erosion of fairness.
- Step 3: Analyzing the options — Option (c) directly addresses the concern of reinforcing bias and reducing fairness, which is the central theme of the passage.
- Step 4: Verifying other options — Options (a) and (b)are not discussed in the passage, and while real-time data analysis is mentioned, the core issue is about fairness and bias rather than technical limitations in data analysis.
- Step 5: Conclusion — The central concern is the reinforcement of bias and the reduction of fairness in AI-driven law enforcement practices.
Which of the following would best justify the use of predictive policing, despite the ethical concerns discussed in the passage?
Show Hint
- It will allow law enforcement to increase arrest quotas.
- It helps deploy police more efficiently in high-risk areas.
- It can eliminate the need for community engagement.
- It will replace the need for police training programs.
The Correct Option is B
Solution and Explanation
- Step 2: Analyzing the options — The passage supports the idea of using predictive policing for efficiency in high-risk areas, not for increasing arrest quotas or eliminating community engagement.
- Step 3: Conclusion — Option (b)directly aligns with the benefits discussed in the passage for predictive policing.
Based on the passage, how does facial recognition technology potentially lead to injustice?
Show Hint
- By exhibiting higher error rates for specific demographic groups
- By decreasing reliance on human judgment in policing
- By increasing costs and limiting deployment in critical areas
- By generating occasional misidentifications across all populations equally
The Correct Option is A
Solution and Explanation
- Step 2: Analyzing the options — Option (a) is supported by the passage, as the technology has higher error rates for specific demographic groups, leading to potential injustice.
- Step 3: Conclusion — Option (a) is the correct answer.
Why does the lack of transparency in AI algorithms pose a challenge within judicial proceedings?
Show Hint
- It complicates efforts to scrutinize and contest algorithm-driven outcomes
- It leads to greater reliance on community surveillance
- It restricts the professional development of law enforcement personnel
- It discourages investment in emerging AI technologies for policing
The Correct Option is A
Solution and Explanation
- Step 2: Analyzing the options — Option (a) is directly supported by the passage as it highlights the difficulty in contesting algorithm-driven decisions.
- Step 3: Conclusion — Option (a) is the correct answer.
The word "opaqueness" in the paragraph refers to:
Show Hint
- Clear and understandable legal processes
- Lack of visibility or understanding
- Openness and transparency in systems
- Restricted access due to security levels
The Correct Option is B
Solution and Explanation
- Step 2: Analyzing the options — Option (b)correctly defines "opaqueness" as a lack of visibility or understanding.
- Step 3: Conclusion — Option (b)is the correct answer.
What measure is most essential to prevent AI systems from reinforcing existing social biases?
Show Hint
- Training AI systems on large datasets without reviewing for fairness
- Allowing AI tools to evolve independently without human oversight
- Reviewing training data for historical bias and ensuring algorithmic accountability
- Prioritizing efficiency and rapid deployment over fairness and oversight
The Correct Option is C
Solution and Explanation
- Step 2: Analyzing the options — Option (c) is the only one that directly addresses the need to review and ensure fairness in AI systems.
- Step 3: Conclusion — Option (c) is the correct answer.
A government proposes a new AI-driven sentencing tool that assigns prison terms based on statistical models trained on past sentencing data. The tool is designed to ensure consistency and eliminate human error. Civil liberties groups oppose the tool, arguing it may encode past judicial biases. Which of the following objections is most consistent with the concerns raised in the passage?
Show Hint
- AI systems should not be used in criminal justice unless they are cheaper than traditional methods.
- Sentencing tools can never make mistakes if trained on real court data.
- Without transparency and bias audits, AI may reinforce systemic injustices embedded in the data.
- Automated sentencing ensures faster trials and should replace human judges entirely.
The Correct Option is C
Solution and Explanation
- Step 2: Analyzing the options — Option (c) directly addresses the concern of systemic injustices and the need for transparency and bias audits, which is emphasized in the passage.
- Step 3: Conclusion — Option (c) is the correct answer.
Which of the following assumptions, if true, would undermine concerns raised in the paragraph?
Show Hint
- AI models incorporate bias-correction mechanisms that adjust for historical disparities in sentencing patterns.
- The training data used by the AI model reflect systemic inequalities and disproportionate sentencing against certain communities.
- The AI system operates as a closed algorithm, with no transparency regarding how sentencing decisions are derived.
- Studies from other jurisdictions show that similar AI systems have not amplified racial and gender biases in sentencing outcomes.
The Correct Option is A
Solution and Explanation
- Step 2: Analyzing the options — Option (a) directly addresses a mechanism that could correct for the historical biases in sentencing, which would mitigate the concerns raised in the passage.
- Step 3: Conclusion — Option (a) is the correct answer.
Top Questions on Reading Comprehension
- Shown below are letters ‘e’ taken from four popular logos, arranged in a sequence. Which of the options is the correct sequence?
- UCEED - 2026
- English Language Comprehension
- Reading Comprehension
- Shown below are four options, each showing consecutive frames representing the movement of a pendulum of an old-style wall clock for an animation movie. Which is the correct option?
- UCEED - 2026
- English Language Comprehension
- Reading Comprehension
- Acrucial moderating factor in how people experience comparisons is self-esteem. Individuals with high self-esteem are more likely to interpret upward comparison as informative rather than threatening. They are more resilient in the face of others’ success and more likely to believe they can reach their own goals. In contrast, people with low self-esteem are more prone to interpret comparison as judgment, reinforcing negative self-views and triggering feelings of inadequacy.
This dynamic creates a self-reinforcing loop. People with high self-esteem are less vulnerable to upward comparison, which intensifies those doubts. Those with a secure sense of self are more likely to use comparison as a learning tool. The same external stimulus—a colleague’s achievements, a friend’s popularity—can have radically different effects depending on internal stability.
Self-esteem also influences how people choose their comparison targets. Research has found that individuals often engage in “selective comparison,” seeking out those who confirm their existing beliefs about themselves. This can become a subtle form of self-sabotage. Someone who feels unworthy may subconsciously seek out targets that reinforce that sense, perpetuating a narrative of inferiority. One of the most promising antidotes to social comparison is temporal comparison—evaluating oneself not against others, but against one’s own past. This method has been shown to increase motivation and satisfaction, especially when individuals can see concrete progress. Temporal comparison activates the same reward circuits as social comparison but avoids the threat systems associated with social ranking. It also reinforces agency: individuals focus on what they can control and improve rather than what others control. In therapeutic and coaching settings, temporal comparison is used to help clients build self-efficacy and track growth over time.
Moreover, people who focus on self-improvement rather than social dominance are less likely to fall into cycles of envy or self-pity. They can still use others as inspiration, but they do so without attaching their self-worth to the outcome. This is not to say they never compare—but that they focus on learning, rather than judgment. The most skilful approach to comparison may lie in eliminating it, but in reframing it as feedback. When we interpret comparison as information rather than a verdict, we can ask, “What can I learn from this?” This shift turns comparison into a growth tool. Teachers mentor their students, not as rivals. Psychologists emphasize that the key variable here is mindset.
A fixed mindset sees comparison as a threat. If someone else is better, it means we are worse. A growth mindset sees comparison as a map. If someone else has reached a certain level, it means the path exists. This reframing is not just a cognitive trick. It changes the emotional tone of comparison, making it more likely to inspire than to wound. Reframing also requires emotional regulation—the ability to notice an initial pang of envy or shame without reacting impulsively. With practice, individuals can learn to pause, reflect, and reinterpret their emotional responses. Over time, this builds resilience and self-trust, allowing comparison to become a learning tool rather than a cage.- XAT - 2026
- Verbal and Logical Ability
- Reading Comprehension
When people who are talking don’t share the same culture, knowledge, values, and assumptions, mutual understanding can be especially difficult. Such understanding is possible through the negotiation of meaning. To negotiate meaning with someone, you have to become aware of and respect both the differences in your backgrounds and when these differences are important. You need enough diversity of cultural and personal experience to be aware that divergent world views exist and what they might be like. You also need the flexibility in world view, and a generous tolerance for mistakes, as well as a talent for finding the right metaphor to communicate the relevant parts of unshared experiences or to highlight the shared experiences while demphasizing the others. Metaphorical imagination is a crucial skill in creating rapport and in communicating the nature of unshared experience. This skill consists, in large measure, of the ability to bend your world view and adjust the way you categorize your experiences. Problems of mutual understanding are not exotic; they arise in all extended conversations where understanding is important.
When it really counts, meaning is almost never communicated according to the CONDUIT metaphor, that is, where one person transmits a fixed, clear proposition to another by means of expressions in a common language, where both parties have all the relevant common knowledge, assumptions, values, etc. When the chips are down, meaning is negotiated: you slowly figure out what you have in common, what it is safe to talk about, how you can communicate unshared experience or create a shared vision. With enough flexibility in bending your world view and with luck and charity, you may achieve some mutual understanding.
Communication theories based on the CONDUIT metaphor turn from the pathetic to the evil when they are applied indiscriminately on a large scale, say, in government surveillance or computerized files. There, what is most crucial for real understanding is almost never included, and it is assumed that the words in the file have meaning in themselves—disembodied, objective, understandable meaning. When a society lives by the CONDUITmetaphor on a large scale, misunderstanding, persecution, and much worse are the likely products.- XAT - 2026
- Verbal and Logical Ability
- Reading Comprehension
Later, I realized that reviewing the history of nuclear physics served another purpose as well: It gave the lie to the naive belief that the physicists could have come together when nuclear fission was discovered (in Nazi Germany!) and agreed to keep the discovery a secret, thereby sparing humanity such a burden. No. Given the development of nuclear physics up to 1938, development that physicists throughout the world pursued in all innocence of any intention of finding the engine of a new weapon of mass destruction—only one of them, the remarkable Hungarian physicist Leo Szilard, took that possibility seriously—the discovery of nuclear fission was inevitable. To stop it, you would have had to stop physics. If German scientists hadn’t made the discovery when they did, French, American, Russian, Italian, or Danish scientists would have done so, almost certainly within days or weeks. They were all working at the same cutting edge, trying to understand the strange results of a simple experiment bombarding uranium with neutrons. Here was no Faustian bargain, as movie directors and other naifs still find it intellectually challenging to imagine. Here was no evil machinery that the noble scientists might hide from the problems and the generals. To the contrary, there was a high insight into how the world works, an energetic reaction, older than the earth, that science had finally devised the instruments and arrangements to coart forth. “Make it seem inevitable,” Louis Pasteur used to advise his students when they prepared to write up their discoveries. But it was. To wish that it might have been ignored or suppressed is barbarous. “Knowledge,” Niels Bohr once noted, “is itself the basis for civilization.” You cannot have the one without the other; the one depends upon the other. Nor can you have only benevolent knowledge; the scientific method doesn’t filter for benevolence. Knowledge has consequences, not always intended, not always comfortable, but always welcome. The earth revolves around the sun, not the sun around the earth. “It is a profound and necessary truth,” Robert Oppenheimer would say, “that the deep things in science are not found because they are useful; they are found because it was possible to find them.”
...Bohr proposed once that the goal of science is not universal truth. Rather, he argued, the modest but relentless goal of science is “the gradual removal of prejudices.” The discovery that the earth revolves around the sun has gradually removed the prejudice that the earth is the center of the universe. The discovery of microbes is gradually removing the prejudice that disease is a punishment from God. The discovery of evolution is gradually removing the prejudice that Homo sapiens is a separate and special creation.- XAT - 2026
- Verbal and Logical Ability
- Reading Comprehension
Questions Asked in AILET exam
- Author : Book = Composer : ?
- Complete the series: A2, C6, F12, J20, ?
- AILET BALLB (Hons.) - 2026
- Number Series
- Complete the series: B2, D4, G7, K11, ?
- AILET BALLB (Hons.) - 2026
- Number Series
- Keyboard : Typist = Paintbrush : ?
- Doctor : Hospital = ?