In an age where algorithms guide everything from search results to hiring decisions, it’s easy to assume they are neutral tools—mathematical and therefore objective. But the truth is more complicated. Algorithms reflect the data and values used to build them, which means they can inherit human biases, even when no person is consciously making the decision.
Consider facial recognition software, now used in airports, smartphones, and even policing. Several studies have shown that these systems perform less accurately when identifying people with darker skin tones. The reason isn’t malicious intent—it’s the data. If a system is trained mostly on images of light-skinned faces, it will struggle to recognize others with the same accuracy.
This is just one example of algorithmic bias—a phenomenon where automated systems replicate or even amplify inequalities present in society. In some cases, these biases are subtle, like a search engine suggesting different job ads based on gender. In others, they are more serious, such as predictive policing tools sending more patrols to historically over-policed neighborhoods, reinforcing a cycle of surveillance.
The danger lies not just in the flaws themselves, but in our tendency to trust machines more than humans. Algorithms are often seen as impartial, which makes their decisions harder to question. When a person makes a mistake, we ask why. When a machine makes one, we may not even notice.
Some experts argue that the solution is not to reject algorithms entirely but to build better ones—systems designed with fairness, transparency, and accountability in mind. This involves asking hard questions: Who is designing the algorithm? What data is it using? And who benefits—or suffers—from its outcomes?
As technology becomes more woven into decisions that affect lives—credit, education, employment, justice—these questions become more urgent. Algorithms don’t just reflect the world as it is. They help shape the world that will be.
Q1: Which sentence best summarizes the central claim of the passage?
Q2: What is the purpose of the author’s example about facial recognition technology?
Q3: How does the author develop the idea that algorithms can be dangerous even when unintentional?
Q4: What is the tone of the final paragraph?
Q5: What abstract concept is most explored in this passage?
Printable Comprehension Practice
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Q1: Which sentence best summarizes the central claim of the passage?
✅ Correct Answer: B
💡 Reasoning: This sentence captures the main idea: while algorithms seem impartial, they can actually inherit human biases, leading to unequal outcomes.
Q2: What is the purpose of the author’s example about facial recognition technology?
✅ Correct Answer: C
💡 Reasoning: The facial recognition example demonstrates how biased inputs can cause unfair results, even when there is no deliberate prejudice involved.
Q3: How does the author develop the idea that algorithms can be dangerous even when unintentional?
✅ Correct Answer: C
💡 Reasoning: The author emphasizes that people often don’t question algorithmic decisions, making their unseen biases more harmful.
Q4: What is the tone of the final paragraph?
✅ Correct Answer: B
💡 Reasoning: The final paragraph uses phrases like 'become more urgent' and 'shape the world that will be,' suggesting concern about future impacts.
Q5: What abstract concept is most explored in this passage?
✅ Correct Answer: C
💡 Reasoning: The passage’s deeper idea is that technology is not neutral—it reflects and sometimes magnifies the biases present in the world around it.