Response to Data Science

• The application of machine learning to hiring and promoting employees promises to alleviate some of the biases created by current techniques that rely more on human intuition. What biases in the HR hiring and promotion process are the people analytics systems trying to eliminate, and how do they do it. What new biases might these algorithms introduce?


There are many significant biases in the HR hiring and promotion process that the people analytics systems trying to eliminate. One of them being appearance,Tall men get hired and promoted more frequently than short men, and make more money. Beautiful women get preferential treatment, too. one of the best example being survey done by Duke’s Fuqua School of Business where they found that there was no relationship  between how competent a CEO looked and the financial performance of his or her company.


"Now days hiring managers just don’t even want to interview anymore they just want to hire the people with the highest scores."


Companies want great Aptitude, skills, personal history, psychological stability, discretion and loyalty which can not be tested with just looking at one’s resume but machine learning helps them to go through this and choose the best.And algorithms helps them to do so perhaps the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.


Hiring managers  assess the way coders use language on social networks from LinkedIn to Twitter; companies have determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills, this will surely allow hiring companies as well as people finding jobs to connect and save time.


The application of machine learning to hiring and promoting employees is certainly alleviating some of the biases created by current techniques that rely more on human intuition.


“Consider knack”, a tiny start-up based in Silicon Valley came up with a great idea to hire people, they created games. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential.
Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, algorithm had identified the people whose ideas had panned out.
It offers a way for the hiring managers to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out and then they can devote much more careful attention to the 20 people out of 100 whose ideas have the most merit.
In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.


Company called Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors.When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose


The potential power of this data-rich approach is obvious. What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management. In theory, this approach enables companies to fast-track workers for promotion based on their statistical profiles; to assess managers more scientifically; even to match workers and supervisors who are likely to perform well together, based on the mix of their competencies and personalities.


But there is even a different side to it, there are some new biases that these algorithms might introduce We are leaving the evaluation on specific algorithms that are already set and choose specifically. Don’t you think ‘data signature’ of natural leaders play a role in promotion. These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.




• Considering that most of you will be interviewing for jobs soon, does this application of machine learning to human resources seem fair to you? What's your reaction?




It is pretty true that over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers. But this relationship is likely to loosen in the coming years and hiring using algorithms will take over future.  The use of machine learning to choose human resource is a definitely a good idea but, I think that sometimes it is also important for the hirer to understand one more than his brain, like one’s body language can tell a lot of things about one. So a mixture of both algorithms and human intuition will work best.