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Ethical AI – Fairness and Accuracy

Ethical A.I. powers Diversity & Inclusion

AI ethics is a set of values, principles, and techniques that employ widely accepted standards of right and wrong to guide moral conduct in developing and using AI technologies.

Why Ethical A.I is crucial for D&I across Talent Acquisition

At EVA.ai, we ensure that training sets are diverse, demographically unbiased and that our algorithms mitigate human prejudice and expand diversity and socioeconomic inclusion better than humans ever could.

Our Talent algorithms are optimised for Fairness and Accuracy

EVA deploys dynamic & personalised algorithms in your platform. These algorithms are machine-learning-based candidate recommendation predictors. The predictive scores they generate optimise your search for the ideal candidate by balancing as far as possible accuracy and fairness when it sorts your long lists of candidates. To do so, two main mechanisms are taking place:

  • Random Inclusion:

    By generating Diversity & Inclusion compliant long-lists that randomly add candidates who have close-enough correlations to the user’s request by measuring these variables against the data in EVA’s algorithmic recommendation module

  • Anonymisation: 

Basic personal information such as name, age, race, native language and gender can trigger a reviewer’s unconscious bias. As you don’t need this information to assess whether a candidate is great for the job, EVA can remove data from the User Interface

Engineered without Bias

EVA’s ML algorithms do not include data that is inherently biased – such as age, gender, location or nationality

The set of input variables is strictly controlled. There is limited scope for bias by excluding inherently biased data from the Machine Learning model’s data sets.

For example, personal data and age, gender or even postcodes are never used. The latter resolve to 50m radius and introduce bias between more or less affluent locations

EVA evaluates every recruiter and hiring manager decision (e.g. add, reject, interview, discard) and candidate decision (e.g. reject recommended job) into a continuous loop of insight that informs your recruitment processes.

The HR 4.0 Way

EVA’s predictive model is based on candidates’ successes and drop-outs across the recruitment funnel from Longlist to Shortlist (Screen) in the interview process and hiring.

In other terms, EVA tests candidate data against your recruitment history and pipelines to identify a list of the most relevant candidates for any given requirements. Decision data against candidate progressions are used to teach EVA’s predictive algorithms within the system.

Engraining Ethical D&I within a sustainable hiring culture

This ensures teams build invaluable and sustainable hiring practices. EVA learns, tracks, and retains the best-practice hiring patterns and insights. Over time, EVA will gain an understanding of your job(s) market context.

Whether it recognises the difference between a step up and sideways step in a career or anticipating candidate movements for truly proactive recruitment such as predicting who is likely to move within specific departments, job families or career stages.


EVA – conversational AI & predictive ML, operating within a modular HR Tech Platform, that automates processes and personalises experiences.

EVA team helps organisations achieve both growth and sustainability with HR 4.0 – the fourth industrial revolution applied to Talent Acquisition & Talent Management.

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