Academic Writing Sample Answers Practice 7 Test 01

Automated recruitment is increasingly used to rank applications and even assess candidates through recorded interviews. Supporters contend that it can make hiring faster and less prejudiced, whereas critics warn that systems trained on previous decisions may perpetuate discrimination. In my view, such technology can improve recruitment, but only when it supports transparent human decision-making rather than determining outcomes independently.

There are persuasive reasons for using algorithms at the initial stage. A large employer may receive thousands of applications for a single graduate programme, making careful manual review both costly and inconsistent. Software can apply the same job-related criteria to every candidate, identify relevant qualifications and remove information such as names or photographs that might trigger conscious or unconscious bias. It can also widen access by processing applications promptly and allowing interviews to be completed remotely. If the criteria are valid and regularly tested, automation may therefore give more applicants a genuine opportunity to be considered.

However, consistency is not synonymous with fairness. An algorithm learns from the objectives and data supplied to it, and historical hiring records often reflect unequal opportunities. For example, a model trained on the profiles of previously successful managers might downgrade applicants whose education, career breaks or vocabulary differ from those of the traditionally dominant group. Video-interview tools may create further problems if they interpret eye contact, facial movement or speech patterns as evidence of competence. Such features can disadvantage disabled candidates, non-native speakers or people from different cultural backgrounds without measuring their ability to perform the job. Because complex models are difficult to explain, rejected applicants may also be unable to identify or challenge an unfair decision.

I therefore believe that employers should use AI for limited tasks such as organising applications, anonymising personal details and flagging candidates for review. Final judgements should remain with trained recruiters, while independent audits should compare selection rates across demographic groups and investigate unexplained disparities. Applicants should also be told when automation is used and offered a human review or an accessible alternative interview format.

In conclusion, recruitment technology can reduce inconsistency and administrative delay, but it does not automatically eliminate prejudice. Its benefits outweigh its risks only when its criteria are relevant, its results are auditable and accountable people retain meaningful control.

Artificial intelligence is now involved in many recruitment processes, from filtering CVs to conducting first-round interviews. Some people see this as a way to reduce personal prejudice and improve efficiency, while others fear that biased historical information will simply be converted into automated decisions. I believe AI can be useful in recruitment, provided that employers monitor it closely and do not allow it to make final decisions alone.

The strongest argument for automation is that it can handle a very large number of applications quickly and consistently. Human recruiters may become tired, overlook suitable candidates or favour people who share their background. In contrast, a well-designed system can assess every application against the same list of relevant skills and qualifications. It can also hide names, ages and photographs during screening, reducing opportunities for discrimination. Employers save time and money, while applicants may receive decisions more quickly.

Nevertheless, an algorithm is only as fair as its training data and design. If a company has historically hired mainly from a narrow social group, a system trained to copy past choices may learn that the characteristics of this group indicate success. It might then reject candidates from different universities, neighbourhoods or career paths even when they could do the work well. Automated video interviews are also questionable when they judge voice, expressions or eye contact. These features may reflect disability, culture or nervousness rather than professional ability. Moreover, when the system is complicated, neither the recruiter nor the applicant may understand why a particular score was given.

For these reasons, AI should assist recruiters rather than replace them. It is suitable for administrative work and an initial comparison of clearly defined qualifications, but humans should review borderline cases and make the final selection. Companies should regularly test whether rejection rates are unusually high for any group and remove criteria that have no direct connection with job performance. Candidates should also have the right to request a human review.

In conclusion, automated recruitment can be faster and more consistent than purely manual hiring, yet it can also repeat discrimination on a larger scale. Careful auditing, relevant criteria and human responsibility are therefore essential if it is to make employment genuinely fairer.

Many companies use algorithms and artificial intelligence to check job applications or carry out early interviews. While some people believe this makes recruitment more efficient and less affected by human prejudice, others argue that the technology can copy unfair patterns from the past. In my opinion, AI is helpful for some parts of recruitment, but people should remain responsible for important hiring decisions.

One advantage of automated screening is its speed. Large organisations may receive hundreds or thousands of applications, and it takes employees a long time to read them all. A computer system can quickly find candidates who have the required qualifications or experience. It can also use the same basic standards for everyone, unlike a recruiter whose judgement may change because of tiredness or a personal preference. If personal details such as names and photographs are removed, the technology may prevent some forms of discrimination.

On the other hand, AI does not make decisions without human influence. People choose the information used to train it and decide which qualities it should value. If most employees hired successfully in the past came from one social group, the system may learn to prefer applicants with a similar background. For instance, it might give a lower score to someone who attended a different type of school or took time away from work to care for children. There are also risks in automated interviews. A system that judges eye contact or speaking style could unfairly disadvantage a nervous candidate, a person with a disability or someone from another culture.

I believe employers should use these tools mainly to organise applications and check clear requirements. A human recruiter should examine the results, interview shortlisted applicants and make the final decision. Companies should also test their systems regularly to see whether particular groups are being rejected more often for reasons unrelated to the job. Applicants need a simple way to ask for human review if they believe the process was unfair.

In conclusion, AI can save time and make some stages of recruitment more consistent. However, it can also repeat existing bias, so it should be carefully checked and used under human supervision rather than trusted completely.