AILET 2026 English Question Paper (OUT) with Solutions: Download PDF

AILET 2026 English Answer Key PDF – Download Link

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AILET 2026 English Overall Analysis

The AILET exam has clearly moved towards a passage-based question pattern, a shift that was strongly evident in the 2024 paper. Most of the passages were easy to moderate in difficulty. In fact, one or two passages were very easy, written in a simple, narrative style, making them comfortable to attempt for students who maintain regular reading habits.

That said, a few passages proved to be tricky. These combined English comprehension with elements of logical reasoning, creating potential pitfalls—particularly for students who are weaker in General Knowledge or analytical reading. Overall, the paper placed greater emphasis on reasoning abilities, especially general reasoning and inference-based questions. Alongside this, vocabulary-based reasoning and static, fact-based questions also appeared quite frequently, reinforcing the exam’s focus on analytical understanding rather than rote learning.

AILET 2026 English Section Analysis

The analysis will be updated here once the AILET 2026 exam is over.

ALIET 2026 English Questions with Solutions

Passage

1. Read the given passage and answer the following questions by choosing the most appropriate response.

The integration of Artificial Intelligence |A| 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 fairmess, bias, and the potential erosion of civil liberties. One of the most controversial applications is predictive policing, which uses historical crime data to forecast 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 binses. 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 Al-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 colour and women, increasing the risk of false accusations and wrongful detentions. The lack of transparency in how these systems operate further complicates the ethical landscape. 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 diffeult to challenge wrongful predictions or decisions in a court of law. As Al continues to evolve, lawmakers and civil rights advocates are calling for stronger regulations to ensure that these technologies are used responsibly. Proposals include 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. While At 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.

1. What is the central concern raised in the passage regarding AI in law enforcement?

a) Replacing human officers

b) High operational costs

c) Reinforcing bias and reducing fairness

d) inability to anulyze real-time data

Correct Answer: (c) Reinforcing bias and reducing fairness

Explanation:

The passage repeatedly highlights concerns about:

• Fairness

• Bias

• Erosion of civil liberties

• Reinforcement of existing societal and historical biases

Predictive policing and facial recognition are criticized mainly because they may perpetuate injustice, especially against marginalized communities. Hence, bias and fairness are the core concerns—not cost, officers, or real-time data.

2. Which of the following wrould best justify the use of predictive policing, denpite the ethical concerns discussed in the passage?

a) It will allow law enforcement to increase arrest quotas.

b) It helps deploy police more efficiently in high-risk areas,

c) It can eliminate the need for community engagement.

d) It will replace the need for police training programs.

Correct Answer: (b) It helps deploy police more efficiently in high-risk areas.

Explanation:

The passage clearly states:

“Proponents argue that this allows police to allocate resources more efficiently and reduce crime rates.”

This is the only positive justification acknowledged, even while ethical concerns remain.

Other options either exaggerate or contradict the passage.

3. Based on the passage, how does facial recognition technology potentially lend to injustice?

a) By exhibiting higher exour rates for apectfle demogriphic groups

b) By decreasing reliance on human judgment in policing

c) By increasing costs and limiting deployment in critical areas

d) By generating occasional misidentifications scress all populations equally

Correct Answer: (a) By exhibiting higher error rates for specific demographic groups

Explanation:

The passage says:

“Studies have shown that FRT is less accurate in identifying people of colour and women.”

This results in:

• False accusations

• Wrongful detentions

Hence, injustice arises due to unequal error rates, not equal mistakes or reduced human judgment.

4. Why does the lock of transnarency in AI algorithms pose a challenge within judicial proceedings?

a) It complicates efforts to scrutinise and contest algorithm-driven outcomes

b) It leads to greater reliance on community survelliance

c) It reatricts the profensional development of law enforcement personnel

d) It discourages investment in emerging AI technologies for policing

Correct Answer: (a) It complicates efforts to scrutinize and contest algorithm-driven outcomes

Explanation:

The passage explains:

• AI systems are often proprietary

• Even officers may not understand how decisions are made

• This “Black Box” nature makes it difficult to challenge decisions in court

Thus, lack of transparency directly affects judicial scrutiny.

5. The word "opaquenees" in the paragraph refers to:

a) Clear and understandable legal processes

b) Lack of visibility or understanding

c) Openness and transparency in aystems

d) Restricted access due to security levels

The word ‘opaqueness’ in the paragraph refers to:

Correct Answer: (b) Lack of visibility or understanding

Explanation:

“Opaqueness” is used in the context of:

• Algorithms being hard to understand

• Decision-making processes not being visible

This aligns exactly with a lack of clarity or transparency, not openness or security restrictions.