7 Recruiting AI Terms Every Recruiter Needs To Know

Ji-A Min

Interest in artificial intelligence (AI) recruiting technology has exploded recently. From finance to sales departments, business leaders are asking how they can leverage AI technology to become more efficient, cost-effective, and competitive. HR is no exception.

To stay on top of this trend, here are seven recruiting AI terms that every recruiter needs to know.

1. Artificial intelligence

Artificial intelligence (AI) is a machine that can mimic human abilities such as learning, problem- solving, planning, perception, and the ability to move objects.

In a nutshell, AI requires large amounts of data as input to produce an output which is a solution to a problem. Core areas of AI include machine learning (e.g., Netflix recommendations), machine perception (e.g., Apple’s Siri), and robotics (e.g., self-driving cars).

How AI is used in recruiting: AI for recruiting is the application of artificial intelligence such as learning or problem-solving to the recruitment function. Recruiting AI technology is designed to automate some part of the recruiting workflow, especially repetitive, high-volume tasks. Applications of recruiting AI technology that currently exist include automated resume screening, recruiter chatbots, and digitized interviews.

2. Algorithm

An algorithm is a procedure or formula that takes inputs through a sequence of steps to produce an output in order to solve a problem.

How an algorithm is used in recruiting: The simplest form of an algorithm used in recruiting is a keyword or Boolean search. The problem here is identifying qualified candidates from a larger applicant pool, the inputs are your search terms, and the output is a shortlist of candidates who meet your search specifications. An example of how an algorithm is used in recruiting AI technology is intelligent resume screening. The problem here is the same: identifying qualified candidates from a larger applicant pool. Instead of using pre-selected search terms, this type of machine learning algorithm trains itself on prior employees to learn which resume data points (inputs) are correlated with successful employees to produce a shortlist of qualified candidates (output).

3. Machine learning

Machine learning is a type of computer program or algorithm with the ability to teach itself by analyzing data (inputs) and coming up with a solution (output). A machine-learning algorithm continues to learn from new data you input to increase the accuracy of the solution it comes up with.

How machine learning is used in recruiting: Machine-learning algorithms in recruiting AI technology is being used to automate resume screening to shortlist and grade candidates by learning from existing employees’ resumes. Machine-learning algorithms in recruiting software are also being used assess candidates’ personality and job fit through digitized interviews by learning from successful candidates’ facial expressions and word choices.

4. Natural language processing

Natural language processing is the ability of a computer program to understand human speech as it is spoken or written.

How natural language processing is used in recruiting: One way natural language processing is being used in recruitment automation technology is through AI chatbots that provide answers, feedback, and suggestions to candidates in real time. Based on candidates’ replies and feedback, the chatbot uses machine learning to teach itself to become more accurate in its answers when interacting with other candidates in the future.

5. People analytics

People analytics is the use of data and data analysis techniques to understand, improve, and optimize the people side of business.

People analytics links people data (inputs) with different types of business data using predictive algorithms to produce outcomes (outputs) aligned with company goals such as increased revenues and lowered costs.

How people analytics is used in recruiting: People analytics isn’t a recruiting AI term on its own, but it falls under the same umbrella of leveraging data and technology to optimize HR and recruiting processes.

6. Predictive analytics

Predictive analytics is a catch-all term for the application of a statistical equation or algorithm to a data set (inputs) to create a predictive model (output) that determines a numerical value of a future probability.

In many cases, the data set used contains multiple variables that are believed to be predictive of a particular outcome.

How predictive analytics is used in recruiting: Predictive analytics can be applied to candidates to predict which ones are likely to be successful employees. This predictive model can be created using resume data, pre-hire assessments, or interview scores. For a predictive model that uses resume data as its inputs, the variables could include education level, years of experience, skills, and personality traits. Predictive analytics can also be applied to employees to predict which one are likely to quit. This predictive model may use multiple variables such as commute distance, company tenure, employee engagement, and compensation.

7. Sentiment analysis

Sentiment analysis is the ability of a computer program to determine the subjective opinion, emotional state, or intended emotional effect of spoken or written word.

The basic form of sentiment analysis is classifying the polarity of a given text: positive, negative, or neutral. More advanced sentiment analysis classifies text into specific emotions such as “angry” and “happy.

How sentiment analysis is used in recruiting: Sentiment analysis is being used to identify potentially biased language in job descriptions. The program is fed input that words such as “aggressive” are perceived as masculine-sounding, whereas words such as “collaborative” are perceived as feminine-sounding. By analyzing the words used in a  job posting, the program can suggest alternative words to help solve problems such as use of words that may discourage female candidates from applying.

The takeaways

The dominant theme in recruiting right now is AI for recruiting. It’s clear that tech-enabled recruiting is here to stay. Give yourself a leg up by familiarizing yourself with the AI recruiting technology terms below:

  1. Artificial intelligence
  2. Algorithm
  3. Machine learning
  4. Natural language processing
  5. Predictive analytics
  6. Sentiment analysis

For more on this topic, see Can Technology Replace Human Interpreters?

Photo: jhuniorig Flickr via Compfight cc

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AI, Blockchain, And Cloud Fuel Banking’s Evolution

John Bertrand

Artificial intelligence (AI), blockchain, and cloud technologies are increasingly appearing on the horizon. This could be exactly what the banker ordered, given the legal mandates for open banking and General Data Protection Regulation for 2018. These three key technologies can fuel the financial services industry’s evolution into the digital age.

Artificial intelligence

AI is a collection of machine learning, natural language processing, and cognitive computing designed for scale. It is this scalability that is exciting, as it can create exponential growth and deliver today’s required personalized communications. For example, in July 2017 UK payments processed 21 million payments per business day. If 0.5% of the daily volume needed additional review, 105,000 items would need to be checked, often manually and with rules-based, tick-the-box solutions. AI would significantly increase productivity by matching payment behavior and pattern recognition, and simply asking the question, “does this look right?” AI-powered chatbots could help business users and consumers answer inquiries and enhance the customer experience.

Blockchain

Blockchain is also a mix of technologies that enables us to trust someone we do not know and protects us from cybercriminals. The block contains vital information about a party, and the chain is the sequence of third-party, verified events that have taken place over the history of the transaction. Blockchain is fully encrypted and can be permissioned for private and public groups. Given the manual, paper-based state of the supply chain, it is not surprising that we’re seeing many new proofs of concepts and pilots using blockchain.

Cloud

Cloud computing gives improved security, scale, and agility to respond to market demands and can decrease banks’ cost bases. The advances in cloud technologies permit software applications to move seamlessly between legacy, private cloud, and public cloud solutions. One such technology, containers, allows the applications to flow safely across the end-to-end processes regardless of the underlying technologies, much like how shipping containers transformed the inefficient, non-scalable 20th century transportation industry to the one today.

Finance’s digital evolution

These technologies are could be the savior of financial services industry. Financial services are rapidly becoming a technology-driven sector, evidenced by the increasing amount of money being spent in this area.

  • Financial services is now one of the largest buyers of software
  • IDC expects this figure to grow more than five percent over 2016’s spending
  • The forecast of $2.7 trillion in worldwide IT spending by 2020 is led by the financial services industry

Legacy banks and financial services firms can either build the technology themselves or work with fintechs to do so; either way it has to be done. Eminent evolutionary biologist Charles Darwin could have been discussing this new banking environment when he noted:

It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change. 

Potential impacts of financial services’ digital evolution include:

  • Low-cost centers using AI to increase straight-through processing (STP) to 100%, thus removing cost, increasing customer satisfaction, and reducing liabilities from errors
  • Administration of trade finance through blockchain to reduce costs and increase certainty of ownership at any point in time
  • Spare computer capacity created by using the cloud, enabling banks to meet peak-day requirements and increase cybersecurity

Security is now a bottom-line concern. See The Future of Cybersecurity: Trust as Competitive Advantage.

This article was originally published on Finextra.

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Robots Are Moving Into Our Human Resources Functions

Agnes Desplechin

“When we react it will be too late,” said Elon Musk, CEO of Tesla Motors (a pioneer in the connected car market), in July at the U.S. National Governors Association Summer Meeting. The businessman expressed his concern about the development of artificial intelligence and the delay in terms of regulation that would represent “a fundamental risk to the existence of human civilization.”

Today, the thought that humans can (in some activities) be substituted by robots no longer belongs solely to fictional works such as Frankenstein (1818) or current television shows Black Mirror and Westworld.

Concerns about potential failures caused by robots are very real and present today. Even before the most advanced prototypes of robots and the possibilities offered by artificial intelligence were considered, economist John Maynard Keynes prophesied the substitution of man by machines. In “Economic Possibilities for Our Grandchildren,” published in 1930, he questioned the effects of automation on jobs, well-being, and happiness, seeking to find solutions to the issue of “technological unemployment.”

A century later, the replacement of human workers by robots is anticipated across the job spectrum. According to Laurent Alexandre, technocrat, urological surgeon, and artificial intelligence (AI) advocate, all professions will, in the near future, be threatened by AI, which will soon be everywhere. Indeed, AI it is already in your pocket; Siri and Google Assistant are early chatbots, conversational robots that will replace salesmen, attorneys, journalists, and eventually, human resource assistants.

To better understand the stakes, we must understand what AI is. Consider a machine without AI, which makes decisions based on manually defined rules. When a machine facing a large data flow learns to analyze and make decisions, intelligence is born; this is machine learning. If you’re still confused about machine learning based on this description, let’s take the example of email that you define manually as “spam” within your mailbox. Once it learns the form, structure, sender, and other details that led you to mark a message as spam (i.e., the rules you defined, even subconsciously), the machine can make the decision that a message is spam. Unlike human intelligence, the machine can be caught off guard when there are exceptions.

How AI develops is of great interest in the context of HR functions. Some examples include using automated and intelligent filters for recruitment, using robots for interviews, or having chatbots act as human resource assistants in order to answer recurring questions from employees.

AI’s contribution is often measured in terms of time and cost savings, but it can also lead to more impartiality and efficiency. Even so, the human aspects and ethics must remain the core part of the HR role. As Elon Musk suggests, we must now ensure AI retains our standards, and, crucially for the HR profession, keeps the “human” in human resources.

Will intelligent machines and HR one day walk hand in hand? AI offers prospects that are very promising and prompt many questions that will shape the evolution of our profession.

AI’s ability to end bias hinges on teaching it to play fair and constantly questioning the results.
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Diving Deep Into Digital Experiences

Kai Goerlich

 

Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.


What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”


Download the executive brief Diving Deep Into Digital Experiences.


Read the full article Swimming in the Immersive Digital Experience.

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Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

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Jenny Dearborn: Soft Skills Will Be Essential for Future Careers

Jenny Dearborn

The Japanese culture has always shown a special reverence for its elderly. That’s why, in 1963, the government began a tradition of giving a silver dish, called a sakazuki, to each citizen who reached the age of 100 by Keiro no Hi (Respect for the Elders Day), which is celebrated on the third Monday of each September.

That first year, there were 153 recipients, according to The Japan Times. By 2016, the number had swelled to more than 65,000, and the dishes cost the already cash-strapped government more than US$2 million, Business Insider reports. Despite the country’s continued devotion to its seniors, the article continues, the government felt obliged to downgrade the finish of the dishes to silver plating to save money.

What tends to get lost in discussions about automation taking over jobs and Millennials taking over the workplace is the impact of increased longevity. In the future, people will need to be in the workforce much longer than they are today. Half of the people born in Japan today, for example, are predicted to live to 107, making their ancestors seem fragile, according to Lynda Gratton and Andrew Scott, professors at the London Business School and authors of The 100-Year Life: Living and Working in an Age of Longevity.

The End of the Three-Stage Career

Assuming that advances in healthcare continue, future generations in wealthier societies could be looking at careers lasting 65 or more years, rather than at the roughly 40 years for today’s 70-year-olds, write Gratton and Scott. The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

It will be replaced by a new model in which people continually learn new skills and shed old ones. Consider that today’s most in-demand occupations and specialties did not exist 10 years ago, according to The Future of Jobs, a report from the World Economic Forum.

And the pace of change is only going to accelerate. Sixty-five percent of children entering primary school today will ultimately end up working in jobs that don’t yet exist, the report notes.

Our current educational systems are not equipped to cope with this degree of change. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is outdated by the time students graduate, the report continues.

Skills That Transcend the Job Market

Instead of treating post-secondary education as a jumping-off point for a specific career path, we may see a switch to a shorter school career that focuses more on skills that transcend a constantly shifting job market. Today, some of these skills, such as complex problem solving and critical thinking, are taught mostly in the context of broader disciplines, such as math or the humanities.

Other competencies that will become critically important in the future are currently treated as if they come naturally or over time with maturity or experience. We receive little, if any, formal training, for example, in creativity and innovation, empathy, emotional intelligence, cross-cultural awareness, persuasion, active listening, and acceptance of change. (No wonder the self-help marketplace continues to thrive!)

The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

These skills, which today are heaped together under the dismissive “soft” rubric, are going to harden up to become indispensable. They will become more important, thanks to artificial intelligence and machine learning, which will usher in an era of infinite information, rendering the concept of an expert in most of today’s job disciplines a quaint relic. As our ability to know more than those around us decreases, our need to be able to collaborate well (with both humans and machines) will help define our success in the future.

Individuals and organizations alike will have to learn how to become more flexible and ready to give up set-in-stone ideas about how businesses and careers are supposed to operate. Given the rapid advances in knowledge and attendant skills that the future will bring, we must be willing to say, repeatedly, that whatever we’ve learned to that point doesn’t apply anymore.

Careers will become more like life itself: a series of unpredictable, fluid experiences rather than a tightly scripted narrative. We need to think about the way forward and be more willing to accept change at the individual and organizational levels.

Rethink Employee Training

One way that organizations can help employees manage this shift is by rethinking training. Today, overworked and overwhelmed employees devote just 1% of their workweek to learning, according to a study by consultancy Bersin by Deloitte. Meanwhile, top business leaders such as Bill Gates and Nike founder Phil Knight spend about five hours a week reading, thinking, and experimenting, according to an article in Inc. magazine.

If organizations are to avoid high turnover costs in a world where the need for new skills is shifting constantly, they must give employees more time for learning and make training courses more relevant to the future needs of organizations and individuals, not just to their current needs.

The amount of learning required will vary by role. That’s why at SAP we’re creating learning personas for specific roles in the company and determining how many hours will be required for each. We’re also dividing up training hours into distinct topics:

  • Law: 10%. This is training required by law, such as training to prevent sexual harassment in the workplace.

  • Company: 20%. Company training includes internal policies and systems.

  • Business: 30%. Employees learn skills required for their current roles in their business units.

  • Future: 40%. This is internal, external, and employee-driven training to close critical skill gaps for jobs of the future.

In the future, we will always need to learn, grow, read, seek out knowledge and truth, and better ourselves with new skills. With the support of employers and educators, we will transform our hardwired fear of change into excitement for change.

We must be able to say to ourselves, “I’m excited to learn something new that I never thought I could do or that never seemed possible before.” D!

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