If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Under the new law, companies must explain how the technology works and how the tools evaluate a candidate. Employers must obtain consent from applicants before using A.I. to assess their videos. The legislation also prohibits businesses from sharing submitted videos except with "persons whose expertise or technology" are required to screen applicants. Job applicants can ask to have submitted videos destroyed, and companies, including any individual with copies, must comply within 30 days.
Artificial Intelligence is the latest buzzword in the technology space. As expected, the automation has made things easier and smarter in various industries, ranging from healthcare, startups to even human resources. Unlike the implementation of Artificial Intelligence in other sectors, the HR space becomes trickier as it involves sensitive employee-employer relationship. Also, will employees trust machines to evaluate their performance or even play a role in recruiting them? Nonetheless, there are several advantages that AI brings to this segment.
Uncomfortable as it may be to admit, uncertainty lies at the heart of some of the most critical decisions we make in the workplace. A job applicant may enjoy stellar qualifications and a track record of success, but it's impossible to know with absolute certainty that her success will be replicated at your organization. There's no guarantee that major investments -- whether they're in marketing, talent acquisition, new technologies or another company -- will yield a high return on investment. New processes and workflows come with their own risks: Will employees adhere to them, and even if they do, will they improve organizational performance? How do we grapple with the many uncertainties we encounter daily?
The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end to end Ability aware Person Job Fit Neural Network model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word level semantic representation for both job requirements and job seekers' experiences based on Recurrent Neural Network. Along this line, four hierarchical ability aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large scale real world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines.
We consider how fair treatment in society for people with disabilities might be impacted by the rise in the use of artificial intelligence, and especially machine learning methods. We argue that fairness for people with disabilities is different to fairness for other protected attributes such as age, gender or race. One major difference is the extreme diversity of ways disabilities manifest, and people adapt. Secondly, disability information is highly sensitive and not always shared, precisely because of the potential for discrimination. Given these differences, we explore definitions of fairness and how well they work in the disability space. Finally, we suggest ways of approaching fairness for people with disabilities in AI applications.
According to CareerBuilder, over 67% of the applicants have a positive impression about organizations who keep them updated throughout the application process. However, it is a herculean task for recruiters to provide regular updates to every one of the aspirants participating in the recruitment process. In fact, they hardly have time to personally update the status of rejected applications while they're busy sourcing candidates. Almost 75% of the applicants never hear back from recruiters. This is an area in which chatbots can transform the applicant experience and take it to a new level.
Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks of quantitative ways of measuring talent competencies as well as the job's talent requirements. To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network which can effectively learn the joint representation of Person-Job fitness from historical job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fits a job, but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, the extensive experiments on a large-scale real-world dataset clearly validate the performance of PJFNN in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.
"So plum is a great data point but it's not the entire data set," said Karim Ben-Jaafar, president of Beanworks. "We look at every single resume and we don't let plum make the decision for us." Plum works by collecting data on people who have succeeded in roles through an online assessment system, and measuring applicants relative to one another on the broad traits determined to be shared by the success stories. A person completing the "Plum profile" may be assessed on traits like organizational skills, for example. Then they receive a score showing how they ranked on organizational skills relative to their peers.
In the corporate world, the only thing worse than a lack of information is an abundance of inaccurate or useless information. While the lack of data can prompt one to action, having access to numerous low-quality pieces of information can lull businesses into a false sense of security. Once disaster strikes, this unintegrated data proves to be just as "effective" as the non-existent data was. The key role in preventing this scenario is the organization's ability to ensure comprehensive data integration across its many departments. It is essential to make all of them work together in unison, as this can oil the cogs of your business machinery.