{"slug": "can-ai-get-you-a-better-job-is-it-ethical", "title": "Can AI Get You a Better Job? Is It Ethical?", "summary": "Using AI in a job search improves outcomes, according to MIT research tracking roughly 500,000 job seekers on a global freelance platform, which found that AI-assisted resume writing led to an 8 percent higher hiring rate, 7.8 percent more job offers, and 8.4 percent higher wages. However, the article warns of ethical concerns, including a 'self-preference bias' in large language models that favors AI-generated resumes up to 82 percent of the time, potentially penalizing equally qualified applicants without access to such tools.", "body_md": "######\n[Artificial Intelligence](/us/basics/artificial-intelligence)\n\n# Can AI Get You a Better Job? Is It Ethical?\n\n## Used properly, AI increases the probability of job success. But questions remain.\n\nPosted August 18, 2026\n[\nReviewed by Margaret Foley\n](/us/docs/editorial-process)\n\n### Key points\n\n- AI doesn't create job opportunities or secure better offers; it improves how you compete for positions.\n- You can use AI to research, rehearse, and negotiate, not just to polish a resume or cover letter.\n- Some AI tools can be used against you, so pair automation with judgment and be aware of how employers use AI.\n\nWill AI get you a better job? No, not by itself. Expecting AI to hand you a job is like expecting a calculator to put money in your bank account: It's simply not how the tool works. What AI can do is give you an edge in your job search, improving your odds of landing the job you want, provided you know how to use it and what to emphasize along the way. MIT tracked roughly 500,000 job seekers on a global freelance platform and found that those who used AI to help write their resumes were 8 percent more likely to be hired, received 7.8 percent more job offers, and earned 8.4 percent higher wages than applicants who got no such help (Wiles et al., 2025). The numbers aren't debatable. Using AI in your job search is not a nice-to-have; it's essential for success.\n\n## Presenting your credentials\n\nMost resumes are written for humans but are first read by applicant tracking systems. Jobscan's annual audit of Fortune 500 [career](https://www.psychologytoday.com/us/basics/career) pages found that 489 of the 500 companies in 2025 used a screening system, a figure that has held between 97 percent and 99 percent every year since 2018 (Purcell, 2025). A resume that reads well but scans poorly will likely be rejected and never get the chance to impress anyone. These systems now go beyond keyword matching, evaluating resumes holistically, including how your self-portrayal matches [company culture](https://www.psychologytoday.com/us/basics/workplace-dynamics). LLMs synthesize content, infer intent, and make contextual judgments based on subliminal messages (Mao et al., 2023). AI benefits the screening process because it can quickly translate real experience into the language that applicant tracking systems recognize and reward.\n\nLike people, AI tends to favor outputs that are familiar. Computer science research has identified the \"*self-preference bias*,\" which is the tendency of large language models to favor their own generated content. This means screening systems favor resumes drafted with AI assistance over ones written by hand, choosing the AI-polished version up to 82 percent of the time (Xu et al., 2025). While holistic evaluation makes intuitive sense for finding the right hire, it magnifies the bias risk if an LLM systematically favors resumes that reflect its own generative style, reinforcing its own assumptions over time and depleting fairness from the assessment process. As Xu and colleagues noted, \"this bias rewards access to specific generative technologies and penalizes those without it, even when applicants are otherwise equally qualified.\" Polishing your resume with AI means that when an LLM is the evaluator, you are 23 percent to 60 percent more likely to be short-listed than equally qualified applicants submitting human-written resumes.\n\n## Identifying opportunities\n\nYou cannot get a job offer if you can't find the job. Identification gets tricky with non-standard job titles, which vary among companies since there's no standard taxonomy. If your goal is to manage people as an HR generalist, you might need to search HR Manager, People Operations Manager, Talent Acquisition Partner, Employee Experience Lead, or Chief People Officer, among others. Miss one and you might miss the perfect job. AI can search for what you didn't know to look for: roles that match your skills but not your job title, postings buried deep in listings, or openings that fit a lateral move you hadn't considered. Unlike you, AI doesn't get [bored](https://www.psychologytoday.com/us/basics/boredom), which is more than can be said for most of us by the second or third page of job listings.\n\n## Acing the interview\n\nMock interviewing is one of the best strategies for acing an interview. Unlike other role-play strategies, AI lets you precisely specify the [personality](https://www.psychologytoday.com/us/basics/personality), background knowledge, and attitude of the interviewer. You can structure the interview to test your knowledge, your cultural fit, or any other critical competency. Drop the company's strategic plan into the response criteria and test how your own beliefs align with the organization's. Or let the AI interviewer ask specific technical questions that help you calibrate knowledge gaps. An AI interviewer won't get tired of your answer, won't ask the awkward follow-up a friend is too polite to raise, and won't mock your ridiculous responses at a future gathering. A recent field experiment on AI-assisted recruitment found that candidates who went through a structured AI interview passed their final human interview at a 20-percentage-point higher rate than candidates screened by resume alone (Aka & Palikot, 2025). Why not increase your odds with practice only AI can provide?\n\n## Talking to decision makers\n\nThis is where AI gives you a decisive edge, performing grunt work in seconds that might take you days. Ask it to pull historical salary ranges for your role and region, compare benefit structures across offers, or draft the counteroffer email you keep rewriting in your head at 2 a.m. AI won't negotiate for you at the table, but it will prepare you by boosting your [confidence](https://www.psychologytoday.com/us/basics/confidence) and negotiation swagger, ensuring you walk into the final interview with hard data instead of a clueless grin. AI-supported candidates perform better in later human interviews and earn more offers than unassisted candidates (Aka & Palikot, 2025). Employers are using AI to find you. You should be using it to outmaneuver them.\n\n## One final note\n\nAI hiring tools are not neutral, and job seekers should be aware of negative AI bias. One study tested three LLMs on more than 3 million resume-to-job comparisons and found the models favored white-associated names 85 percent of the time over Black-associated names, favored male names over female names by a wide margin, and never once favored a Black male name over a white male name in any comparison (Wilson & Caliskan, 2024). If a machine is judging you before a person does, it's worth remembering the machine has its own blind spots, none of which you can fix by trying harder. Newer models have endeavored to eliminate stereotypes, but bias mitigation remains an ongoing problem, since removing biased patterns without removing important assessment information is challenging.\n\nUltimately, AI provides low-hanging career support. While it won't get you a job, it will sharpen your resume, widen your search, coach your delivery, and arm your negotiation with data, not opinion. Just remember that the math AI uses is not always fair calculation, and the smartest job seekers use AI tools without relying on them for the entire job search process.\n\nReferences\n\nAka, A., & Palikot, E. (2025). Better together: Quantifying the benefits of AI-assisted recruitment (arXiv:2507.08029). Stanford University.\n\nMao, K., Dou, Z., Mo, F., Hou, J., Chen, H., & Qian, H. (2023, December). Large language models know your contextual search intent: A prompting framework for conversational search. In *Findings of the Association for Computational Linguistics: EMNLP 2023* (pp. 1211-1225).\n\nPurcell, K. (2025). 2025 applicant tracking system (ATS) usage report: Key shifts and strategies for job seekers. [https://www.jobscan.co/blog/fortune-500-use-applicant-tracking-systems/](https://www.jobscan.co/blog/fortune-500-use-applicant-tracking-systems/)\n\nWiles, E., Munyikwa, Z., & Horton, J. (2025). Algorithmic writing assistance on jobseekers’ resumes increases hires. *Management Science*, *71 *(12), 10144-10164.\n\nWilson, K., & Caliskan, A. (2024, October). Gender, race, and intersectional bias in resume screening via language model retrieval. In *Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society* (7) 1, 1578-1590.\n\nXu, J., Li, G., & Jiang, J. Y. (2025). AI self-preferencing in algorithmic hiring: Empirical evidence and insights. *arXiv preprint arXiv:2509.00462*.", "url": "https://wpnews.pro/news/can-ai-get-you-a-better-job-is-it-ethical", "canonical_source": "https://www.psychologytoday.com/us/blog/motivate/202608/can-ai-get-you-a-better-job-is-it-ethical", "published_at": "2026-08-18 18:02:44+00:00", "updated_at": "2026-08-18 18:42:52.694954+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-ethics"], "entities": ["MIT", "Jobscan", "Fortune 500", "Xu et al.", "Wiles et al.", "Purcell", "Mao et al."], "alternates": {"html": "https://wpnews.pro/news/can-ai-get-you-a-better-job-is-it-ethical", "markdown": "https://wpnews.pro/news/can-ai-get-you-a-better-job-is-it-ethical.md", "text": "https://wpnews.pro/news/can-ai-get-you-a-better-job-is-it-ethical.txt", "jsonld": "https://wpnews.pro/news/can-ai-get-you-a-better-job-is-it-ethical.jsonld"}}