In the hiring process, unconscious bias happens when you form an opinion about candidates based solely on first impressions. Or, when you prefer one candidate over another simply because the first one seems like someone you’d easily hang out with outside of work.

Even in the early hiring stages, a candidate’s resume picture, their name, or their hometown could influence your opinion more than you think. In short, unconscious bias influences your decision – whether positively or negatively – using criteria irrelevant to the job.

According to Deloitte's article on diversity and inclusion, it states, “A growing body of research indicates that diverse and inclusive teams outperform their peers. Companies with inclusive talent practices in hiring, promotion, development, leadership, and team management generate up to 30 percent higher revenue per employee and greater profitability than their competitors. Without a strong culture of inclusion and flexibility, the team-centric model comprising diverse individuals may not perform well.”

Inclusion in the workplace will continue to be a vital component in 2020 and beyond. In order for diversity programs and initiatives to be successful, organizations have to be inclusive. Diversity does not exist without inclusion. When employees feel included, they feel a sense of belonging that drives increased positive performance results and creates collaborative teams who are innovative and engaging. Employees that feel included are more likely to be positively engaged within the organization. Higher employee engagement drives higher levels of productivity, retention, and a company’s overall success.

What it does

Increasing Inclusion provides a unique solution to the manual recruitment process. It can help you through the interview system. Our goal is to bring an objective and diverse approach to a previously subjective recruitment process. We recognise the need for organisations to make data-backed hiring decisions and limit biases in their recruiting processes.

Our solution aims to solve the following problems in a unique and innovative manner:

Reduces bias in judgments: Ensuring that our judgments are not clouded by any bias is crucial when finding right candidate for a job. Using content-based and collaborative filtering methods, the proposed AI-based solution ensures that recommendations are free from any bias. We use resume screening for the initial filtering where only skills are considered as the criteria to be matched instead of gender, race, location, etc. The second set of filtering is done by a chatbot that analyses user responses using the Big 5 model and then content and collaborative filtering is used to suggest the right candidate to the users.


  • Resume Generator.
  • Job Recommendation System using hybrid filtering.
  • Skill-based filtering system.
  • AI ChatBot for skills and personality analysis.
  • Messenger for connecting applicants and recruiters.
  • A secure peer-to-peer video conferencing system for interviews

How we built it

We provide a solution where recruiters can post their job description with all the necessary details and advertise their organization and its job listings and the candidates can apply to them by uploading their resume consisting of skills, past work experiences and qualifications. And our system will recommend the best candidates to the organization with an efficient process of analysis and testing which will unfold in 3 phases.

Phase 1: Resume screening: A candidate can apply to a job of their liking, and on the basis of analyzing their resume, our system will check if the skills and experience possessed by the candidate is in line with the requirements of the organization and shortlist an intermediate pool of candidates.

Phase 2: Interview with Chatbot: Understandably this pool might also be too large to submit to the organization, hence the shortlisted candidates will then have to answer a few questions by interacting with our system’s chat bot. Using natural language processing, our system will measure the accuracy of the answers and analyze the candidates personality traits and assign them scores. On the basis of a threshold, a final pool of selected candidates will be given to the organization.

Phase 3: Online/Video Interview: The organization will be able to interact with these final candidates via a video call service that our system will provide and on the basis of their specific needs, the organization can recruit their picks.

Challenges we ran into

  • Designing the flow of the application
  • Integrating various parts of the application
  • Lack of prior research regarding recruitment automation
  • Security Challenges for protecting data

Accomplishments that we're proud of

Saves Precious Time - Reduces pre-screening efforts and need for in person interviews.

Fast and Relevant - It filters candidates based on hard as well as soft skills using hybrid filtering (both content as well as collaborative filtering)

Reduces Bias in Judgements - All candidates go through the same screening and AI is used to gauge their skills and personality

Helps find the right candidate - Candidates are shown jobs according to their skill-sets increasing the likelihood of companies finding the right candidate.

What's next for Increasing Inclusion

Converting the web-application to a native application as well is the future scope of our application. Having said that, our web-application is fully web-responsive and supports all browsers.

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