AI adds huge value – but only when applied strategically.
It’s important to know that AI isn’t a silver bullet, nor a one-size-fits-all solution. Undoubtedly, the growth in tech solutions will open up a world of opportunity and value. But the growing number of options means it’s imperative organizations think critically about the core principles of AI before investing.
When implemented properly, AI-led talent management and acquisition tools can:
- Improve data-based decision making (and quality of hire)
- Improve the candidate and employee experience
- Automate burdensome manual tasks
- Cut operational overheads
But AI also introduces complexity around bias and data security. Organizations that fail to perform thorough due diligence at the evaluation stage could find themselves facing serious financial, legal and reputational risk.
Understanding AI
Artificial Intelligence is any technique that lets machines mimic human behavior. It allows recruiters to do more of the same things, but also to do things better. AI can be split into 3 fundamental concepts: automation, machine learning and deep learning.
Automation
Automation is rule-based. It applies the same rules (“if this, then that”) to every input, allowing processes to be run faster and at scale, without the need for human intervention. For example, Chatbots. Rule-based chatbots can return candidates the right FAQ page.
Machine Learning
Machine Learning (ML) is more intelligent than automation. It’s not rule-based. It is the ability of an algorithm to learn how to perform a task from a dataset, and then keep learning to do it better over time. One example could be in job/candidate matching. Automation would only group candidates based on explicit, preprogrammed rules. ML would be able to infer additional similarities as the skills found in a job title changed over time, without algorithms needing to be re-written.
Deep Learning
Deep Learning goes even further. It is able to look at a data set and figure out what is most useful and interesting about it in order to solve a task. CV parsing might be one example. Deep Learning can look at unstructured data (like a CV) and identify the elements that suggest a candidate is a good fit for a role, without being told what they are.
Implementing AI
Avoiding Bias
The Industry Need
Using AI, models can be built to exclude bias in algorithms either explicitly (by removing identifiers like name, age and gender) or implicitly (by removing things like address, education or salary). AI can also be baked into candidate scoring systems to ensure consistent and equal weighting across data points, ruling out the possibility of over-reliance on a single factor. Job descriptions are proven to be rife with unconscious bias, but rules engines, analytics and manual processes aren’t sophisticated enough to read data and understand which wording will ensure the most diverse slate of candidates. AI is able to analyze and respond to the changing market at speed and understand adjacency of skills, making sure candidates with similar skills that may be unfamiliar with the recruiter are not overlooked.
The Beamery Solution
Beamery has designed our products and services to avoid unjust impacts on people based on protected characteristics, such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious beliefs. These characteristics, as well as known proxies for these characteristics, are never included as inputs for our training models. Our models, training, and validation data have been audited by a third party to confirm we are meeting the industry standards minimizing the risk of unintended bias. We also never use any facial recognition, voice recognition, emotion detection, or biometrics in our products or services.
Explainability
The Industry Need
The risk of bias in talent management is high, and can have serious downstream impacts not just for businesses, but for people. After all, hiring decisions determine the course of their career. The right AI can help talent teams provide transparency for candidates and employees about which data points have driven which decisions, both positive and negative. That transparency can also promote inclusion by suggesting career paths that may not otherwise have been obvious, especially for minorities who may not be as well-represented at the senior level, or within particular teams.
Explainability means knowing what features and data points led to a recommendation, and having an audit trail to interrogate and justify them. AI and its decision parameters need to be testable, auditable and documented to stand up to scrutiny. Explainable AI helps talent teams make fair, unbiased decisions. Indeed, it can lead to better decisions being made. Understanding how models are producing results means they can be taught, corrected and optimized.
The Beamery Solution
Beamery helps users understand why someone is predicted to be a good match for a role, using a few key explanation layers, which users can provide to candidates, that clarify the AI recommendation.
- Users can see each component’s weight (or influence) in the decision: our AI recommendations articulate what is the mix and weight of skills, seniority, proficiency and industry, so users can clearly see the main reason our AI considered a potential fit between talent and vacancies.
- Users can understand what skills impacted the recommendations the most, also helping them to understand what skills did or did not have an impact on the recommendation.
Security and Privacy
The Industry Need
Unsurprisingly where large volumes of data are concerned, security and privacy are paramount. With the introduction of GDPR, and repeated headlines about breaches and losses, data security is already front of mind for most organizations. They know that data needs to be stored safely, and that they need thorough and provable due diligence processes to stay out of trouble with regulators.
But AI brings in an additional level of risk. AI only works when it’s trained with large volumes of data. That means the onus is on businesses to think critically not only about how they store data, but how they use it. A core principle in the UK’s AI code, for example, is that “Artificial intelligence should not be used to diminish the data rights or privacy of individuals, families or communities”. Talent teams need to ensure they’re using data sensitively, appropriately and in ways that won’t introduce unconscious bias.
The Beamery Solution
When training our models, Beamery uses anonymized third-party datasets, which means the models are not explicitly aware of race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious beliefs. Additionally, when the models are validated against test data sets, non-personally-identifiable data is used. When Beamery matches candidates in the CRM, our models never see the PII of any candidate. All they see are skills, roles and experience. Additionally, any time Beamery’s external auditors make use of candidate demographic features that are obtained from outside of the internal processes, they are deleted following the audit process, and never retained. These data points are used only in assessing model performance for potential bias.