For centuries people have been captivated by the idea of predicting the future (and I’ll be offering my own predictions on the stage in a few days). Crystal ball gazers and fortune tellers all promised to be able to do this. They played on our biases, weaknesses, and gullibility and counted on us attributing chance occurrences to their predictive powers.
But the rise of predictive analytics gives us the ability to reduce uncertainty by applying statistics and determining the probabilities that future patterns will emerge in the behavior of people and systems.
The Internet provides a platform for us to communicate, share, buy, play, and learn. And because people are largely creatures of habit and tend to repeat behaviors, our online activities when combined with today’s computing power and statistical knowledge tell a lot about what we are likely to do. We can give odds, based on science, about what will most likely occur. To do this has required access to mountains of data about what we do, when we do it, how often we do it, and where we do it.
By tracking things such as our location, Facebook likes, retweets, where we check-in, what and when we buy, what we search for and so on, analysts can make reliable predictions as to our future behavior. This data is often called “data exhaust” by analysts, as in and of itself it has no real meaning or value. However, when aggregated, correlated, and combined and then analyzed with the tools of statistics this data becomes not only relevant but commercially valuable.
We are being monitored and watched every time we log into any electronic device whether it is a computer, a mobile phone, a tablet, or a game. And everything we do is collected without us being aware. We do not give permission for it to be collected nor do we have any control over what is collected. And we have no way to turn off the monitoring.
For example, when we buy things, for example, it is not hard to predict that we might buy more of them. It is even possible to narrow this down to specific types of items, the amounts we spend, and the frequency we buy them. Or, when you do something as simple as check in to a restaurant or hotel, you are leaving a location trail as well as an economic trail. Combined with your profession, easily derived from your LinkedIn or Facebook profiles, this data can predict with a high degree of certainty where you are likely to be at a given time, how often you will be there, what kind of hotels you prefer, perhaps even the type of room you prefer, your income, and much more. And all of this can be sold to a hotelier or retailer, for example, without your knowledge or permission.
Commercialization That Plays on Our Predilections
Predictive analytics has had tremendous commercial benefits. Firms such as Amazon are built on predictive analytics that help them predict what we will buy, how much of it, and when, so that they can stock warehouses and order products before they are needed. Most retailers are investing in hiring analysts, which is a growing field.
Much of the work in developing predictive analytics has been paid for by Madison Avenue, Wall Street, and the retail world. We are marketed to heavily based on our location, age, socio-economic status and past behavior. Products are recommended to us based on a prediction about what we are likely to buy.
Shoshana Zuboff, a Harvard professor and no fan of predictive analytics, has focused her research on the study of the rise of the digital, its individual, organizational, and social consequences, and its relationship to the history and future of capitalism. She is concerned that we are applying analytics to making money and toward turning us all into “slaves” of the commercial world.
She says, in her article entitled “A Digital Declaration”:
Now the focus has quietly shifted to the commercial monetization of knowledge about current behavior as well as influencing and shaping emerging behavior for future revenue streams. The opportunity is to analyze, predict, and shape, while profiting from each point in the value chain.
Biases That Impede Truth
All humans have biases, and many that tend to impact human resource professionals and recruiters.
The selection and hiring of people is fraught with bias and subjectivity. Psychologists have assembled long lists of these biases which include our tendency to reject new evidence that contradicts something we believe to be true. Or the tendency to search for and remember information in a way that confirms our preconceptions. Recruiters need to do everything they can to make objective and unbiased decisions — even though perfect objectivity is never going to be possible. I offer a few suggestions below on how to reduce the impact of biases.
There are numerous common biases. For example, if we believe that people with high GPAs, for example, are better workers, then we will seek evidence to prove that and dismiss any that contradicts it. We call that confirmation bias.
Recruiters also often rely too heavily on one trait or piece of information when making decisions — often the first piece of information acquired or the information obtained from a trusted source. If someone recommends a candidate, for example, that recommendation may outweigh any facts that contradict or suggest that the person is not so good.
Many recruiters and hiring managers also suffer from what is called the “Hothand effect” which is the fallacious belief that a person who has experienced success doing something has a greater chance of further success in additional attempts.
We know from research and experience that most of these biases are unfounded and cannot be shown to be decisive in performance, yet we have a hard time believing they are not critical.
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Analytics can help dispel many of these, but only if the results of the analysis are believed and acted on. We need to trust the data more than our gut, and although data is not always right, the percentages are on the side of the data. There are also many instances where our biases were unconsciously built into the algorithms that analyze our data, so understand what is being measured in an algorithm and with what weighting.
Analytics can offer insight and help make sense of mountains of data that have been beyond our reach. Analytics can help us make choices that are based on facts. They can provide us insights and reduce uncertainty. But, as with everything, there are dangers. We need to troll the waters of data with care, ethics, and human judgement.
What You Can Do to Reduce Bias
Each of us, whether recruiter or candidate, has a responsibility to actively think about our prejudices and biases and work to manage their impact on our decisions.
- Know yourself: What are your biases? Think about what you like and don’t like in people, and then ask yourself why you think this way. You can ask yourself what you are really looking for in a candidate. Is it something like GPA or age or a very specific kind of experience — and then ask yourself, what’s the evidence for this to be a decision factor? Is this really evidence that the candidate will perform well, or just a self-fulfilling prophecy because of my bias? Biases are hard to discover, hard to articulate, and even harder to objectively measure. But if you work at it, you can reduce the number of them and their impact.
- Prepare neutral questions: When you prepare for an interview, make sure that your questions are not aimed at bringing out a bias of some sort. Keep them job-specific and relevant to the work you want the candidate to do. Never ask about age, politics, or anything that is not job relevant.
- Use structured interviews: Research has shown that structured interviews reduce bias by focusing on relevant, job-specific factors, and past performance rather than on personal characteristics. Preparing questions focusing on what you expect for accomplishments and on evidence of past performance will reduce bias.
- Accountability: Have objective criteria that will show you if you are hiring everyone that looks the same, has the exact same background, or has other similar characteristics. This is often a sign of confirmation bias — whatever I have now is what I need in the future and is good. Measures that record how many diverse candidates you made an offer to will help you to identify biases and make changes.
- Understand yourself and identify things about yourself that might cause someone to be biased. For example , you might be older, less experienced, a woman in a man’s world, or whatever. Be aware of these during the interview and, use strategies designed to minimize or directly address those concerns. If you think questions are biased or unfair, comment in a polite way about them.
- Answer questions factually: Don’t embellish or add details to answers. Keep your answers short and to the point. Use facts, numbers, and examples to portray your work. Maintain a positive and objective attitude and admit it if you don’t know the answer.
- Avoid the halo effect: Many times, interviewers are positivity attracted to candidates who exhibit some characteristic this is like themselves or like other successful candidates. An interviewer might like your accent or the way you dress. This bias can help, if you recognize that you have a positive attribute that appeals to the interviewer. It pays to know a bit about the interviewer ahead of time to know his or her likes and dislikes and work history. On the other hand, if you believe you are being looked at negatively, stay positive and not provide any reasons to distract the interviewer. Perhaps end the interview with this, “Is there something you heard today that concerns you about my ability to do this job?”
- Comparison bias: You are always being compared to another candidate the interviewer has seen before you. Of course, you have no idea how many there were or what they were like. But you can ask if there is a something that the interviewer is looking for that has been hard to find in other candidates. This may make it possible for you to share something you have done to make yourself stand out. You can also ask what has stuck out as key strengths of the others and then you can stress your similar skills or experience.
- Don’t act the ape: Don’t provide pleasing answers just to make the interviewer like you. This strategy will most likely backfire. Instead use specific examples, i.e. do not say you are a team player — instead provide stories that relate how you worked well on teams.
- Non-verbal bias: Watch your body language because it acts as a conscious and unconscious source of bias. If you are slouching, for example, that might be seen as laziness. If you talk very slowly, you may be considered slow minded. Be aware and try hard to act like the person interviewing you or be as neutral as possible. Dress in a way that is like others in the company. Don’t over dress or under dress.
- First or in the middle bias: To be the first person interviewed or even in the middle of several candidates may put you at a disadvantage. You may be forgotten or your impact might be much less than a more recently interviewed candidate. Try to find a way to stand out. Perhaps you both have the same hobby or like the same sport. Perhaps you both have similar characteristics. You want to leave a positive and strong impression. If it possible to follow up with the interviewer, find out how to do that and rather than ask about the job or your status talk about the common interests you have. Build a relationship if you can.
I have not addressed all the possible biases, but this list should help you interview better whether a recruiter or a candidate.
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