Science for Diversity

Diversity and inclusion have become a goal that many organizations strive to achieve. Knockri was founded to address the issue of diversity in hiring and remove barriers for organizations. We enable you to implement candidate selection practices that predict performance and improve diversity.
Opening the black box

Knockri’s Machine Learning and Ethical Science

Knockri’s approach to machine learning (ML) focuses on analyzing just the interview transcript of an interviewee’s responses. It does not analyze or detect any non-verbal or emotional cues as part of its analysis. Race, gender, age, ethnicity, accent, appearance, or sexual preference do not contribute towards an interviewee’s score.

Knockri uses sophisticated Natural Language Processing (NLP) models that only analyze the relevant behavioural content of each interviewee’s transcript, and how it relates to performance on-the-job.

Common Dangers When Applying ML for Hiring and Talent Management:

Instead, Knockri’s NLP models take a different approach that is grounded in scientific theory, which are transparent and enable purely objective scoring.

Solving the Diversity Dilemma

Selection procedures are flawed when it comes to finding a balance between test validity and candidate diversity, demonstrating a diversity dilemma. 
Cognitive ability assessments predict job performance but hinder diversity. Personality assessments can increase diversity but have a lesser relationship with job performance.

Eliminate Bias Without Compromising on Performance

Structured Behavioural Interviews Are Advantageous

These assessments predict performance equal to or better than cognitive assessments, but do not result in substantial subgroup differences.

Here are the most widely used digital selection procedures along with the expected subgroup differences for each

Fair Selection Procedures That Are Accessible to All

Removing Human Bias in the Hiring Process

Unstructured interview formats lead to bias and subjective forms of scoring. Here are some of the most common biases in interviews:

Halo effect:

The tendency to use overall impressions to guide scoring rather than individual responses. This can result in similar ratings across different competencies that are measured.


The tendency to provide only high ratings across candidates. This reduces variability in scores making it more difficult to distinguish between candidates.


The tendency to remember only the first and last piece of information during an interview or the first and last candidates interviewed.

First Impressions:

The tendency for first impressions to influence an interviewer’s subsequent judgments of a candidate in the rating process.

Similar-to-me effect:

The tendency to favor candidates that display similar background, demographic, or attitudinal similarities.

Interviewer Training Efforts Are Limited

Many training efforts have been introduced to improve interview rating accuracy, including rater-error training, frame-of-reference training, and behavioral observation training to mitigate bias and improve rating accuracy. But these often don't mitigate interviewer bias
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