'The search is soul-destroying': Young jobseekers on the struggle to find work Young people are bearing the brunt of the UK's weak labour market, according to new figures from the Office for National Statistics (ONS). Some 16.1% of people aged 16 to 24 are not able to find work, compared to a national unemployment figure of 5.1%. That does not include young people who are out of work but not looking for a job, due to ill health or who are still studying. Businesses, particularly in sectors that traditionally gave young people their first jobs, like retail and hospitality, say higher costs are leading them to cut staff or not take on new hires, which often hits young workers the hardest. But graduate-level roles are also proving harder to land.

Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of predictive performance, interpretability, and fairness. It is shown that explainable boosting machines, a recent interpretable model, perform nearly as well as the best black-box models. It is also shown how model sparsity, feature smoothing, and fairness mitigation can enhance transparency and fairness with only minor losses in performance. These findings suggest that interpretable profiling provides an accountable and trustworthy alternative to black-box models without compromising performance.

The extraction of information from semi-structured text, such as resumes, has long been a challenge due to the diverse formatting styles and subjective content organization. Conventional solutions rely on specialized logic tailored for specific use cases. However, we propose a revolutionary approach leveraging structured Graphs, Natural Language Processing (NLP), and Deep Learning. By abstracting intricate logic into Graph structures, we transform raw data into a comprehensive Knowledge Graph. This innovative framework enables precise information extraction and sophisticated querying. We systematically construct dictionaries assigning skill weights, paving the way for nuanced talent analysis. Our system not only benefits job recruiters and curriculum designers but also empowers job seekers with targeted query-based filtering and ranking capabilities.

Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations.

When Anisa* graduated from her second degree, she felt "fairly confident" that her postgraduate studies, double-major undergraduate degree and years spent balancing two volunteering roles and a part-time job would account for something in the job market. She applied to every entry-level or junior role she could find in her industry, tracking each application's outcome on a spreadsheet. Before she knew it, "applying for jobs became a full-time job": in total, she applied for, and was rejected from, 350 jobs before finally landing one 18 months later. And she believes that AI – in particular its use in screening applications – is a huge part of the reason. "The rise of third party, AI-run digital online forms are a huge pain point for so many jobseekers," Anisa says as she recalls uploading CVs alongside filling in digital forms.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. While there's a vast amount of data available for HR and talent analytics today, most organizations are still not reaping the benefits of their analytics investments. Gartner reports that just 21% of HR leaders use data to "shape talent acquisition and recruiting strategies, improve employee engagement and inform other business decisions." As the report notes, more data doesn't necessarily mean more action. However, Myinterview -- an Israel-based company that aims to enable hiring managers to leverage video for pre-screening candidates at scale -- says it has developed a platform that captures, plays and interprets videos generated from candidates who respond to pre-determined questions.

High-tech artificial intelligence (AI) is to be rolled out at a Kent Jobcentre as part of a six-month nationwide trial to marry jobseekers with local vacancies. The Government-backed match-making service will start in March at 20 selected sites across England and Scotland - with Maidstone the only place in Kent included. It forms part of the Government's Way to Work campaign which aims to get 500,000 people back into work. The technology will pose a series of questions to those seeking work to build up an online profile - this will then be fed into the software, described as'cutting-edge' by Government chiefs, to point jobseekers in the right direction to existing vacancies or towards a local skills'bootcamp'. It will not, however, be mandatory.

The U.S. market for robotics and artificial intelligence career openings is exploding based on early 2022 trends from job postings on Robots.Jobs, the marketplace specifically for robotics and AI companies looking for talent and for jobseekers looking for the latest industry opportunities. Feb. 2, 2022 - The U.S. market for robotics and artificial intelligence career openings is exploding based on early 2022 trends from job postings on Robots.Jobs, the marketplace specifically for robotics and AI companies looking for talent and for jobseekers looking for the latest industry opportunities. In the last 90 days, open positions on Robots.Jobs have increased by more than 500 percent. Newly featured job-posters include autonomous drone hardware and sensors company GreenSight and Intrinsic AI, making industrial robotics accessible and usable for businesses. "Robotics, IoT and AI careers are in high demand across almost all industries, including industrial, healthcare, biotech, logistics, consumer and more," said Ann P. Walsh, CEO & cofounder, Robots.Jobs.

RIYADH: When applying for a job these days, Saudis need to think not just about the person reading their CV and cover letter, but the artificial intelligence involved as well. The nature of employment in Saudi Arabia is in a state of rapid flux. While previous generations might have aspired to a career-long government position, younger jobseekers must be far more alert, nimble and ready for change. Two distinct trends are emerging in the Kingdom: an overall move to the private sector as the government encourages economic diversification, and a growing focus on technology-related jobs. A recent survey by the UK's Open University found that no less than eight of the top ten'jobs of the future' are in the realm of computer science: machine learning consultant (specialized in'data mining'), ethical hacker (testing cybersecurity systems), blockchain developer, AI developer, AI analytics engineer, data analyst, data protection officer and digital content strategist.

The advent of powerful prediction algorithms led to increased automation of high-stake decisions regarding the allocation of scarce resources such as government spending and welfare support. This automation bears the risk of perpetuating unwanted discrimination against vulnerable and historically disadvantaged groups. Research on algorithmic discrimination in computer science and other disciplines developed a plethora of fairness metrics to detect and correct discriminatory algorithms. Drawing on robust sociological and philosophical discourse on distributive justice, we identify the limitations and problematic implications of prominent fairness metrics. We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority. We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.