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The goal of Talent Acquisition leaders is to hire people who are the most qualified for a role, regardless of factors that have no influence on their ability to perform. But, while many companies believe their hiring practices are fair and free of bias, there is a strong chance that unintentional biases emerge throughout the process. This hinders their ability to create a diverse and high-performing team.
Clearly talent acquisition should primarily be focused upon the candidates’ skills, experience and potential. Yet without a robust profile which includes 50-100 data points detailing their skills, competencies, experience and aspirations you are limited to a brief resume or a LinkedIn profile. I think you’d agree, this is not a true representation of a candidate.
When your decision making process is constricted by limited data, you’re more likely to be impacted by unconscious bias. You are forced to make a judgment that’s not based upon empirical data.
Thankfully, there are some forward-thinking organisations that have evolved to remove most of the obvious unconscious bias factors such as race, ethnicity or gender from their hiring process. But still, the plethora of equally harmful biases still emerge – school bias, affinity bias, confirmation bias, physical appearance bias, past performance bias and my favourite – intuition bias based on a ‘gut instinct’.
The answer is to leverage AI technology that can build a profile across your total talent pool (employees, contingent, alumni and candidates). This not only contains the 50-100 data points detailing their skills, competencies, experience and aspirations but it is also constantly evolving with new skills being added as learning adds new data to the Knowledge Graph.
Importantly, there is no need to throw out the existing HR Technology stack as these tools (mostly) do their job very well. What is needed though is a new layer of technology – An AI Powered Team & Work management platform that uses a Machine Learning algorithm. This technology can be integrated into all facets of Talent Acquisition and Talent Management processes to finally remove just about all unconscious bias.
As you explore this exciting new technology there are a few new concepts to get your head around…
What is Machine Learning?
Machine Learning is a field of computer science that uses statistical techniques to enable a software algorithm to learn and improve with experience. Within the context of Talent Acquisition, a Machine Learning algorithm becomes the evolving engine that doesn’t require endless database fields of information to be entered by employees or candidates – instead automatically recommending new skills and competencies.
What is an ontology and why is it relevant?
An ontology is a formal naming and definition of the types, properties, and interrelationships of terms. Within the talent acquisition sector the types, properties and interrelationships of all skills, competencies and experiences form a relevant ontology.
At ProFinda we have invested 5+ years and millions of pounds building an ontology of 60,000+ terms used across the corporate world and how they interconnect. A simple example is the ontology will recognize that the skill ‘bitcoin’ is a ‘cryptocurrency’ and that both ‘Ripple’ and ‘Ethereum’ are interrelated as they are other cryptocurrencies. The Machine Learning algorithm then suggests all options to a user as a further skill.
So what is a Knowledge Graph?
A Knowledge Graph acquires and integrates information from the ontology and using the Machine Learning algorithm applies a reasoner to derive new knowledge for the user. The Knowledge Graph is the interrelation between work, roles, skills and outcomes and it becomes a self-evolving graph of knowledge.
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