Did you know Amazon had been building a recruiting machine-learning-based engine since 2014? It was to take care of reviewing applicants’ resumes with the aim of intelligently automatizing the search for top talent. However, in 2018, Amazon made headlines for the wrong reasons. Their AI recruiting tool showed a bias against women in technical roles. Now, it seems like the tech giant is harboring dreams of bringing this bot back, at the cost of laying off hundreds of its recruiters. This begs the question: Is AI technology inherently bad?


A snapshot look at Amazon’s failed AI recruitment tool

Automation has been key to Amazon’s e-commerce dominance, be it inside their warehouses or driving pricing decisions. The company’s experimental hiring tool used AI to give job candidates scores ranging from one to five stars - like shoppers rate products on Amazon.

By 2015, Amazon realized its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way. This is because it was built on data accumulated from CVs submitted to the firm over a 10-year period. It never occurred that the applicant pool then was a reflection of a male-dominated tech industry and leaves a mostly male applicant pool to learn from. In effect, Amazon's system penalized resumes that included the word "women's", as in 'women's chess club captain' for example. Amazon edited the program to make it neutral to the term but it became clear that the system could not be relied upon. 

We’d like to say (assuming it didn’t have a serious impact on the company’s recruiting over the last few years) that Amazon deserves some credit for realizing its tool had a problem, trying to fix it, and eventually moving on. 

Related post: Digital interviews raise concerns.


Which biases can affect job interviews?

Artificial Intelligence (AI) systems have the potential to revolutionize various industries, including recruitment. However, it is crucial to recognize and address the issue of bias that can find its way into these systems. Understanding the different types of bias in AI is key to demystifying this complex issue. Below we look at 4 types of biases in AI:

Data bias

Data bias occurs when the training data used to develop an AI system is unrepresentative or contains inherent biases. If the training data predominantly represents a specific demographic or perpetuates societal prejudices, the AI system may replicate those biases in its decision-making processes.

Algorithmic bias

Algorithmic bias refers to biases that arise from the algorithms used by AI systems to analyze and make predictions. These biases can manifest due to flawed logic, incomplete data, or the system unintentionally learning biased patterns from the training data.

Gender bias

Gender bias can manifest in AI recruitment systems when historical imbalances in the representation of genders within certain professions are encoded in the training data. Let’s use Amazon as an example; where the data mostly consists of male candidates being hired for specific roles, the AI system may inadvertently favor male applicants in its recommendations or selections.

Racial bias

Racial bias in AI recruitment systems occurs when the training data reflects historical disparities and prejudices related to race or ethnicity. If the data used to train the system is biased against certain racial or ethnic groups, it can lead to discriminatory outcomes, perpetuating existing inequalities.


The impact of bias on your recruitment processes

Reinforces inequality

Biased AI systems can reinforce existing inequalities by perpetuating discriminatory practices. If the AI system consistently favors certain demographics, it can perpetuate underrepresentation and hinder efforts to create diverse and inclusive work environments.

Underrepresentation and discrimination

Bias in recruitment AI systems can lead to the underrepresentation of marginalized groups in various professions. Candidates from such groups may be unfairly excluded or face additional barriers due to biased algorithms, resulting in discriminatory outcomes and missed opportunities for both candidates and employers.

Understanding the types of bias that can emerge in AI systems is essential for identifying and mitigating these issues. Recognizing the potential biases that exist in recruitment AI systems allows for the development of more fair and inclusive technologies that promote equal opportunities for all candidates. In the next section, we will explore how Tengai leads the way in addressing bias and ensuring fair recruitment practices.

Related post: What is ethical AI in recruitment?


The importance of an inclusive recruitment process

Fair recruitment processes are important in fostering a diverse, inclusive, and equitable workforce. When organizations prioritize fair recruitment, they ensure that all candidates, regardless of their background, have an equal opportunity to showcase their skills and qualifications. Allowing the best candidates to emerge based on their abilities rather than factors like gender, race, or socioeconomic status is something recruiters and businesses must prioritize. 

Having fair recruitment practices also helps organizations tap into a broader talent pool, so they can harness a diverse range of perspectives, experiences, and talents. By embracing fairness in recruitment, organizations are better able to foster innovation, creativity, and productivity, ultimately leading to better business outcomes. On a deeper level, having inclusive recruiting processes aligns with ethical principles, societal expectations, and legal requirements, demonstrating an organization's commitment to equality and social responsibility.

So, you can see how inclusivity in recruitment can be a win-win for both the applicants and the business but how do you make sure you’re implementing it into your recruitment process?


Best practices for implementing fair recruitment AI

Implementing recruitment AI requires careful consideration and adherence to best practices that prioritize fairness, diversity, and inclusivity. By following these practices, organizations can harness the power of AI to enhance their recruitment processes while ensuring equal opportunities for all candidates. Establishing clear goals and objectives, ensuring diverse and representative training data, regularly auditing and monitoring AI systems, involving diverse stakeholders in the development process, and providing ongoing training for recruiters are key components of effective implementation. These best practices serve as a guide to mitigate biases, promote transparency, and foster an inclusive recruitment environment. By adopting these principles, organizations can leverage recruitment AI to build diverse and high-performing teams, contributing to a fair and equitable workplace.

Establish clear goals and objectives

When implementing fair recruitment AI, it is essential to establish clear goals and objectives that align with diversity, inclusion, and equal opportunity. These goals should be defined and communicated across the organization, guiding the development and implementation of the AI system. Clear objectives help ensure that fairness is prioritized throughout the recruitment process.

Ensure diverse and representative training data

To mitigate biases in AI systems, it is crucial to use diverse and representative training data. This data should encompass a wide range of demographics, backgrounds, and experiences. By incorporating a variety of perspectives in the training data, organizations can reduce the risk of perpetuating biases and enable the AI system to make fair and inclusive decisions.

Regularly auditing and monitoring AI systems

To maintain fairness, AI systems used in recruitment should undergo regular audits and monitoring. Organizations should assess the performance and impact of the system, ensuring that it aligns with fairness goals. Regular evaluations help identify any biases that may arise over time, enabling organizations to address them promptly and make necessary adjustments.

Involve diverse stakeholders in the development process

To foster fairness and inclusivity, it is crucial to involve diverse stakeholders in the development process of AI recruitment systems. This includes individuals from different backgrounds, perspectives, and roles within the organization. By including a diverse group of stakeholders, organizations can benefit from various insights, challenge biases, and ensure that the system reflects a wide range of perspectives.

Provide ongoing training and education for recruiters

Equipping recruiters with the knowledge and skills to understand and mitigate bias is essential. Organizations should provide ongoing training and education on topics such as unconscious bias, diversity and inclusion, and ethical AI practices. This empowers recruiters to make informed and fair decisions, enabling them to effectively leverage AI systems while ensuring equal opportunities for all candidates.


Tengai's approach to fair AI recruitment

At Tengai, we recognize that our software can impact individuals and society. We take this responsibility seriously. Because without the right safeguards, AI could cause harm and exacerbate existing inequalities. We offer an unbiased and objective approach to hiring, focusing on fair and inclusive practices. Tengai combines cutting-edge technology with human-centric design to create a recruitment solution that minimizes bias and promotes equal opportunities for all candidates.

Here are a few ways we accomplish this:

Ethical principles that guide Tengai's development

We built Tengai on a foundation of strong ethical principles. We are committed to values such as fairness, non-discrimination, and equal opportunity for all. Everything we do all the way to the development process of Tengai is guided by these principles, ensuring that the platform is designed and implemented in a manner that aligns with ethical standards and respects the rights and dignity of all individuals involved.

Tengai's commitment to transparency and accountability

Transparency and accountability are core tenets of Tengai's operations. We commit Tengai to provide visibility into its processes and algorithms, ensuring that stakeholders have a clear understanding of how decisions are made. By promoting transparency, Tengai aims to foster trust and enable organizations to assess and validate the fairness of recruitment outcomes.

Mitigating data bias in Tengai's algorithms

One critical aspect of responsible AI development is, therefore, the focus on identifying and mitigating bias. As our society continues to evolve with rapid innovation in emerging technologies, in particular AI, we will continue to evolve and further develop our principles to ensure that we hold ourselves to the highest standards. Our platform employs robust data collection techniques, ensuring that the training data used to develop our algorithms is diverse, representative, and free from discriminatory biases. Tengai's data scientists and developers employ rigorous methods to identify and eliminate biases, continually refining and improving the platform to minimize the risk of biased outcomes.

Tengai's unbiased interview methodology

Tengai adopts an unbiased interview methodology that focuses on relevant competencies and skills rather than personal characteristics or demographic information. Tengai's interviews are standardized and structured, ensuring consistency and objectivity throughout the process. By removing subjective biases, Tengai enables fair evaluation of candidates, helping organizations make informed and unbiased hiring decisions.

Tengai's efforts to address diversity and inclusion

Tengai is dedicated to addressing diversity and inclusion challenges in the recruitment process. The platform actively promotes the consideration of a diverse range of candidates and emphasizes the importance of equal representation. Tengai collaborates with organizations to identify and eliminate bias, fostering an inclusive hiring environment where candidates from all backgrounds can thrive. Through its inclusive practices, Tengai contributes to building diverse and equitable workplaces.

It’s our commitment to ethical principles, transparency, unbiased algorithms, and inclusive practices that help set us apart as a leader in fair recruitment. By leveraging technology and human-centered approaches, we’re paving the way for organizations to embrace fairness and foster diverse, inclusive, and high-performing teams. 

Read more about Tengai’s ethical AI principles.


Mitigate bias in your recruitment process with Tengai

Our mission is not just to avoid bias in employment decisions, but that our AI is used to actively promote diversity and aid in the achievement of equal opportunity for everyone regardless of gender, ethnicity, age, or disability status. While our goal is to amplify, augment, empower, and enhance human performance, we recognize that there already are existing power imbalances in society that need to be considered. 

Effectively use the Tengai Avatar designed to conduct objective job interviews. The Tengai Avatar is the interface between our recruitment platform, Tengai Recruiting Hub, and our candidate platform Tengai Talent Hub. Making it possible for recruiters to screen candidates in a more efficient and unbiased way. At the same time, job seekers feel in control over their data and can see how Tengai has interpreted their personalities.

To find out more about how Tengai can help you improve your candidate interview experience, please get in touch.

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