How Machine Learning is taking over recruitment, and how it’s not

Machine Learning’s promise is to find the perfect candidate and assess them without your interference, but what is it exactly and how does it really help you?

Machine Learning is one of today’s most promising technologies. Especially in the last 10 years the interest in Machine Learning has been rapidly increasing. But what many people don’t know, is that Machine Learning already came about in 1952 when scientists began experimenting with programming machines to remember their own previous actions.

What is Machine Learning (ML)?

Machine learning (ML) is an application of artificial intelligence (AI) that has the ability to improve itself from experience automatically without being explicitly programmed. It does so by learning from data — text, numbers or for example photos.

Examples of Machine Learning that most of us have experienced in real life are:

• Recommendation engines (like Netflix and YouTube suggestions)
• Image analysis (like Google Photos’ face recognition)
• Chatbots (like customer service chatbots)
• Semi self-driving cars (like some of Tesla’s cars)
• Medical diagnostics (like medical image recognition and other medical information analysis)

Machine Learning is a subset of Artificial Intelligence

‍There are three main types of machine learning algorithms that have their own way of learning:

Supervised learning: Models that are trained with labelled data sets on which the model learns. The input data can be for example pictures of cats and other things. The pictures are labelled by humans, some are cats and some are not cats. The machine learns to recognize patterns in pictures of cats and over time is able to recognize them on its own. Supervised machine learning is the most common type used today.

Handsketsh by Serafeim Loukas

‍Unsupervised learning: Models that learn with unlabelled training data. The unsupervised program looks for patterns in the unlabelled data and can find patterns or trends that people aren’t explicitly looking for. Unsupervised learning is widely used to uncover groups within data (referred to as clustering) and to predict rules that describe data (referred to as association). An example is a Machine Learning model that starts to see patterns in certain types of customers of a webshop and categorizes them accordingly.

Handsketsh by Serafeim Loukas

‍Reinforcement learning: Models that have been given a set of guidelines and, through trial and error, learn to take actions with the best possible outcome. In case the outcome of the machine’s action is desirable it is positively reinforced with rewards, if actions lead to undesirable outcomes it is sanctioned. An example is an autonomous vehicle that is told when it makes the right decisions and when it doesn’t.

Handsketsh by Serafeim Loukas

Deep Learning is a subset of Machine Learning in which the intelligence of the system is made up of neural networks with many different layers. Deep Learning can process extensive amounts of data and determine the weight of connections in the network. In face recognition for example, some layers might be responsible for recognizing the shape of the nose and other layers look at the overall composition of the individual features of the face and if that picture adds up to being a face.

How does Machine Learning work?

Machine Learning looks for patterns in data to inform better decisions in the future. In order to do that the model needs initial input.

So called training data is fed into the model that serves as a base of examples. Training data can be for example a lot of pictures or loads of text, depending on the desired function of the model.

The developers of the Machine Learning algorithm choose a type of learning to use: supervised, unsupervised, reinforcement learning or a combination.

The model trains itself to find patterns and the developer can tweak the model over time by changing parameters.

The steps of developing a Machine Learning model are as follows:

1. A problem is defined and a goal is set for the model to achieve.
2. The development team chooses their algorithm.
3. Training data is gathered: sources of data are identified (for example a cat image database), scripts are written to extract the data (getting the cat images in your own database), data is verified (are these quality pictures of actual cats), cleaned (get rid of mouse pictures) and in case of supervised learning the data is labelled (image = cat).
4. The model is built and trained. In a supervised model the team feeds data to the model and tweaks its parameters. In an unsupervised model the team doesn’t interfere with how the model learns and observes the outcomes that the model provides.
5. The model is connected to an application so the intelligence of the model can be accessed by end users, this can be anything like an analytics dashboard, search engine or a webshop.
6. The final step is model validation: the model is verified based on the output of the model and the user experience in the application. If outcomes are not desirable or if they are degrading over time, the model has to be retrained.

A great way to get a picture of how Machine Learning works is to check out this interactive website:

Visual explanation of Machine Learning by R2D3

R2D3 website explaining Machine Learning in a step by step approach

Use cases of Machine Learning in recruitment

There are loads of use cases in recruitment for the application of Machine Learning.

The recruitment process exists for a big part on collecting information: finding profiles, screening and scoring them, assessing candidates etc.

Every step in the recruitment cycle can be potentially a very data driven one, that’s why you see use cases all across the cycle.

Candidate sourcing and screening

One of the most well known but at the same time challenging uses of Machine Learning in recruitment is in the sourcing and screening process.

Candidate profiles (or CV’s) and in some cases social media activity are analysed to get a better understanding of what background, skills and interest candidates have.

This information is then matched to the job requirements of the hiring company.

The opportunity for using Machine Learning in this process is the speed and volume at which Machine Learning algorithms can analyse data and the statistical accuracy at which they can match profile and activity information to job and company information.

A big part of the sourcing and screening is doing checks based on requirements. The challenge is that talent data is very hard to standardize because candidates in different industries have different ways of articulating and structuring their information and the same accounts for how hiring companies draft their job descriptions and requirements.

Examples of companies in this field are HeroHunt.ai, Ceipal and Beamery.

Candidate assessment

Candidate assessment is used to assess a candidate’s competency. In most cases an assessment is focussed on skills but there are also assessment tools that focus on personality and intelligence.

In some cases it makes sense to support candidate assessments with Machin…

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