Read the passage carefully
Algorithms are increasingly used to recommend, rank, classify, and predict. Their apparent precision can create an illusion of neutrality: a numerical score looks objective because it is expressed in numbers. Yet an algorithm is not an independent observer of reality. It is a procedure created from assumptions, data, and design choices.
Consider a system that predicts which applicants are likely to succeed in a course. If historical data reflect unequal access to preparation, the system may learn patterns that correlate with past advantage rather than genuine potential. Even if the mathematical model is internally consistent, its output may reproduce a bias present in the data.
This does not mean that algorithms are useless. Human decision-making is also vulnerable to inconsistency, prejudice, fatigue, and selective attention. A carefully designed algorithm can reveal patterns that a person would miss and can apply a stated rule consistently. The problem arises when numerical output is treated as a final judgment rather than as evidence to be interpreted.
Responsible use therefore requires more than improving model accuracy. It requires asking what the system is optimizing, whose interests are represented in the data, what errors are costly, and what opportunities exist for human review. Transparency is valuable, but transparency alone is insufficient if users cannot understand the implications of the output.
The mature position is neither 'trust the algorithm' nor 'reject the algorithm'. It is to treat computational prediction as one component of a larger decision process in which assumptions, evidence, uncertainty, and human consequences remain visible.