Uses and limitations of Artificial Intelligence in Human Resources

Artificial Intelligence (AI) is a wide term applied to machines performing intelligent task. Machine Learning (ML) is a part of artificial intelligence. It is probably the part that is currently evolving faster and it is indeed revolutionizing many s

The application of Artificial Intelligence in human resources has an enormous potential, but it faces some challenges that are becoming very evident.

ML takes advantage of huge amounts of data to notice things that humans are not able to detect because of the volume and complexity of the information to be analyzed. An example of AI, which is not ML, would be a computer programmed with chess rules. A computer can calculate alternative movements fast enough to play chess at a good level.

On the other hand, an example we would be using ML if we provide a computer with all lists in Spotify so that, with all that data, an algorithm can recommend songs to users adapted to their preferences. In this case, the analysis of the existing lists allows the machine to associate songs of the same style and give results that the human mind would have difficulty identifying due to the huge amount of information.

ML is surprising the world because, in the most favorable environments, machines can discover trends and correlations that could not be apprehended by the human mind. In environments of millions of data, the advantage of machines lies in their capacity and speed. In any case, ML is a specific application of AI that is far from the intelligence that science fiction books talked about where the intelligence of a machine seemed almost human.

A favorable environment for ML has a large amount of data that faithfully represent reality. We are not programming machines, we are teaching them with data. Machines learn from the correlations in the data, but do not detect the logic behind. A machine can detect a correlation that makes no sense, such as redheads speak better English, simply because they are learning from a sample in which the correlation exists.

To understand the application in the field of human resources, it is useful to explore the difference between supervised and unsupervised ML. The following article by Andrew Ng can help you  understand this distinction What Artificial Intelligence Can and Can’t Do Right Now. ML is called supervised, when inputs are mapped to outputs. For example, you have some photographs and the machine learns to recognize if there are human faces in them. In this context, the key is that you need many examples of photos, with and without faces, to be able to teach the machine. Once the machine learns, it can differentiate. This method is used to classify and study regressions.

ML is called unsupervised when the data is not mapped and it is the machine that looks for the patterns. This technique is used to identify groups and associations. For example, the machine is loaded with all the purchases that consumers make in a supermarket to identify buying patterns among users. In this type of ML, it may not be possible to detect the logic of the predictions that are being made.

Against this background
, the applications to human resources are multiple. A good example of supervised ML is CV screening based on certain predetermined parameters. There are many similar applications. The application of ML becomes more problematic in unsupervised or more sophisticated environments. For example, the theory says that machines evaluate CVs objectively, while humans can value some attributes and ignore others depending on their own background and preferences. However, if the data with which we are training the machines has bias, we will be introducing biases unconsciously, and potentially at a larger scale.

First of all, we must take into account that when talking about ​​people, data is sensitive, difficult to obtain and always subject to the data protection law applicable in each country. Secondly, in the field of ​​people management, you need to explain the decisions you make with transparency. Additionally, the impact of an error in anomalous cases (I mean a case that does not respond to the usual correlations) is not acceptable. Hiring a person is not the same as recommending a song.

In the case of Amazon, as reported in A Practical Approach to Detecting and Correcting Bias in AI Systems, they built their CV database with CVs submitted during the last 10 years, most of which were men. Without a more representative sample, the algorithm developed a bias against women. Unfortunately for ML, the selection of people is an extremely complicated issue. First, it could be said that human resources professionals also learn following a ML methodology. We learn and make predictions based on our past experience. For this reason, a rich and diverse experience often helps people to have less bias. Of course, years of work and having conducted many interviews favor a broader vision. The value of experience is fundamental in this field. Additionally, HR professionals must be open and have mental flexibility to detect new trends and adapt their criteria to avoid hiring for the future based only on skills that have been successful in the past. Josh Bersin's reflection on this topic is interesting in the article Hire Leaders for What They Can Do, Not What They Have Done (including an interesesting mention to the Peter principle).

In summary, the application of ML to the screening of candidates has four major drawbacks: 1) Difficulty of obtaining a sample without bias. 2) The risk of error in sensitive issues. 3) Intrinsically, ML is based on learning from the past, as it is trained with existing data, and presents difficulties in anticipating future needs. 4) Transparency and understanding of the criteria are necessary, so the use of unsupervised techniques is limited. 

The following article -The role of artificial intelligence and machine learning in hr- seems realistic defining the current situation of AI and ML in human resources. Taking the selection processes as an example, the objective will not be to replace recruiters, but to facilitate their task. The most common use in this area in the coming years seems to be the automation of repetitive tasks such as CV filtering, chatbots, candidate testing and application review. 

The above mentioned difficulties limit, for the moment, the applications of ML in the most sensitive and complex human resources activities. In any case, the potential of AI in this field is undoubtedly enormous.