When Ty landed an introductory phone interview with a finance and banking company last month, they assumed it would be a quick chat with a recruiter. And when they got on the phone, Ty assumed the recruiter, who introduced herself as Jaime, was human. "The voice sounded similar to Siri," said Ty, who is 29 and lives in the DC metro area. Ty realized they weren't speaking to a living, breathing person. Their interviewer was an AI system, and one with a rather rude habit.

The use of ChatGPT among job applicants has grown in popularity amid their concerns about their own ability to create a resume that stands out in a competitive job market. In Japan, it is customary for students to begin job hunting long before graduation. The job-hunting process is arduous, and there is a stigma around failing to secure a job before graduation. One critical aspect of the application process is the completion of company-specific questionnaires known as "entry sheets" (ES), with students typically applying to several firms. These sheets require concise responses, typically within 150 to 400 characters, for each question.

Earlier this month, Lina Khan, chair of the US Federal Trade Commission (FTC), wrote an essay in The New York Times affirming the agency's commitment to regulating AI. But there was one AI application Khan didn't mention that the FTC urgently needs to regulate: automated hiring systems. These range in complexity from tools that merely parse resumes and rank them to systems that green-light candidates and trash applicants deemed unfit. Increasingly, working Americans are obligated to use them if they want to get hired. If you buy something using links in our stories, we may earn a commission.

Due to the increasing number of graduates, many applicants experience the situation about finding a job, and employers experience difficulty filtering job applicants, which might negatively impact their effectiveness. However, most job-hunting websites lack job recommendation and CV filtering or ranking functionality, which are not integrated into the system. Thus, a smart job hunter combined with the above functionality will be conducted in this project, which contains job recommendations, CV ranking and even a job dashboard for skills and job applicant functionality. Job recommendation and CV ranking starts from the automatic keyword extraction and end with the Job/CV ranking algorithm. Automatic keyword extraction is implemented by Job2Skill and the CV2Skill model based on Bert. Job2Skill consists of two components, text encoder and Gru-based layers, while CV2Skill is mainly based on Bert and fine-tunes the pre-trained model by the Resume- Entity dataset. Besides, to match skills from CV and job description and rank lists of jobs and candidates, job/CV ranking algorithms have been provided to compute the occurrence ratio of skill words based on TFIDF score and match ratio of the total skill numbers. Besides, some advanced features have been integrated into the website to improve user experiences, such as the calendar and sweetalert2 plugin. And some basic features to go through job application processes, such as job application tracking and interview arrangement.

Two-sided online matching platforms have been employed in various markets. However, agents' preferences in present market are usually implicit and unknown and must be learned from data. With the growing availability of side information involved in the decision process, modern online matching methodology demands the capability to track preference dynamics for agents based on their contextual information. This motivates us to consider a novel Contextual Online Matching Bandit prOblem (COMBO), which allows dynamic preferences in matching decisions. Existing works focus on multi-armed bandit with static preference, but this is insufficient: the two-sided preference changes as along as one-side's contextual information updates, resulting in non-static matching. In this paper, we propose a Centralized Contextual - Explore Then Commit (CC-ETC) algorithm to adapt to the COMBO. CC-ETC solves online matching with dynamic preference. In theory, we show that CC-ETC achieves a sublinear regret upper bound O(log(T)) and is a rate-optimal algorithm by proving a matching lower bound. In the experiments, we demonstrate that CC-ETC is robust to variant preference schemes, dimensions of contexts, reward noise levels, and contexts variation levels.

Because artificial intelligence (AI) has a wide range of uses in businesses, from streamlining job processes all the way to aggregating business data, more industry leaders are now using AI to improve company performance. A recent global survey on AI adoption and value revealed that AI adoption in at least one business function had increased to 56%, up from 50% in 2020. More importantly, the survey indicated that AI brought about positive economic returns. Companies involved in the study reported that earnings attributable to AI have increased to 27%, up from 22% in the previous survey. Lastly, the article shared that companies experienced significantly higher cost savings from AI than they did previously in every function.

New laws in New York City and California regulating the use of AI and personal data are set to go into effect on January 1, 2023. Despite the delay in enforcement, experts predict that it will still have a significant impact outside of those jurisdictions. By requiring a biased audit before any tool is used and notifying job applicants and employees prior to its use, Local Law 144 will regulate how businesses can use automated employment decision tools. The 2018 California Consumer Privacy Act, as amended by the California Privacy Rights Act, broadens the scope of the data privacy law to include operations between businesses as well as employees, job applicants, and independent contractors. Both laws' implementation dates, April 15, 2023, for the CCPA in California and July 1, 2023, for New York City, have been postponed, and no regulations have yet been released.

According to a confidential internal document viewed by Recode, Amazon has been working on an ... [ ] Automated Applicant Evaluation system that will determine which job applicants possess the most potential for success. Instead of humans reading your résumé, artificial intelligence technology is equipped to do the job. But can it do it well? Last week, Amazon offered buyouts to its recruiters and could look to replace them with artificial technology software. This is in addition to the projected thousands of people who will be let go from the giant online retailer.

Last week, Amazon extended buyout offers to hundreds of its recruiters as part of what is expected to be a months-long cycle of layoffs that has left corporate employees across the company angered and on edge. Now, Recode has viewed a confidential internal document that raises the question of whether a new artificial intelligence technology that the company began experimenting with last year will one day replace some of these employees. According to an October 2021 internal paper labeled as "Amazon confidential," the tech giant has been working for at least the last year to hand over some of its recruiters' tasks to an AI technology that aims to predict which job applicants across certain corporate and warehouse jobs will be successful in a given role and fast-track them to an interview -- without a human recruiter's involvement. The technology works in part by finding similarities between the resumes of current, well-performing Amazon employees and those of job applicants applying for similar jobs. The technology, known internally as Automated Applicant Evaluation, or AAE, was built by a group in Amazon's HR division known as the Artificial Intelligence Recruitment team and was first tested last year.

In recent years, there has been an increase in the usage of AI tools that are advertised as a solution to the lack of diversity in the workforce. These tools range from chatbots and CV scrapers to aid companies in hiring employees. Users of such tools claim that it eliminates gender and ethnic biases in hiring by utilizing algorithms that analyze job applicants through their speech patterns, expressions, and other aspects. However, researchers from Cambridge's Centre for Gender Studies contend that AI recruiting tools are superficial and equivalent to "automated pseudoscience" in a recent report published in Philosophy and Technology. They claim it is a risky instance of "technosolutionism" - the use of technology to address complex issues like discrimination without making the necessary investments or alterations to organizational culture.