The practices of human resources (HR) and recruitment have been drastically altered by artificial intelligence (AI). Even if computer systems don’t have human emotions or life experiences, they might nevertheless reinforce prejudices from their construction or training data. Additionally, they may misinterpret data, which could result in forecasts that are skewed. Manifestations of bias can be fatal.
According to research, certain AI-powered autonomous cars have trouble identifying pedestrians with dark skin, endangering their lives. Targeting corporate HR departments, this in-depth paper offers a thorough examination of the main obstacles, solutions, and industry best practices for minimizing bias in AI hiring and HR systems.
AI Hiring Bias: What is It?
An AI model that unfairly or incorrectly favors or disapproves of particular candidates is said to exhibit AI hiring bias. This prejudice may cause AI systems to reject competent applicants based on factors like gender or ethnicity that have no bearing whatsoever on their ability to execute the job. AI technologies are employed for several tasks, including optimizing job descriptions, matching candidates, processing resumes, and even performing AI-driven interviews.
If an AI hiring system is trained mostly on resumes from a certain university, for instance, it may unjustly give preference to applicants from that school. If you hire someone solely because they attended Hogwarts, you’re disregarding muggles who could perform the same or even better!
What Kinds of Bias Are There in AI Hiring Systems?
1. Algorithmic bias.
When mistakes in the algorithm of an AI model cause it to make unfair or incorrect decisions, this is known as algorithmic bias. Nowadays, with applications of machine learning (ML) and artificial intelligence (AI) encroaching into every part of our lives, it’s a common worry.
An algorithm that has been taught to identify high-performing individuals based on their prior achievements, for example, can unintentionally favor characteristics that are more prevalent in a particular population. This prejudice results from unfair algorithms, biased or incomplete input data, or exclusionary AI development procedures.
2. Training Data Bias:
AI systems base their judgments and predictions on past data. The algorithm will reinforce prejudices if the data used to train these systems shows biased human choices, favoring a specific gender, race, or age group. Programs that have been trained on data sets to identify patterns or reach conclusions are known as artificial intelligence (AI) models.
A biased dataset produces skewed results, low accuracy levels, and analytical errors since it does not fairly represent the use case of a model. The algorithm is trained to assume that all teachers are female, for instance, by utilizing training data that solely includes female teachers.
3. Predictive Bias.
When an AI system continuously overestimates or underestimates the performance of a specific group in the future, this bias arises. The discrepancy between the average of a model’s predictions and the average of the data’s ground-truth labels is known as prediction bias. A personality test, for instance, predicts performance, but it does so more accurately for people under 40 than for those over 40. This indicates the presence of prediction bias.
Examples of Bias in AI Hiring in the Real World
To add some spice, here are some actual instances of bias in AI hiring systems:
1. HireVue’s Tool for Facial Analysis:
Candidates were assessed by HireVue, a startup that provides AI-driven video interview analysis, using their tone of voice, word choice, and facial expressions. Nonetheless, detractors contended that the tool would strengthen preexisting prejudices against people with disabilities, gender, and ethnicity. Candidates were disadvantaged, for example, if they talked differently or were less expressive because of neurological or cultural differences. The facial analysis feature was phased off by HireVue in 2021 following an FTC complaint and substantial criticism.
2. LinkedIn’s AI Hiring Platform
Recruiters were once given candidate recommendations by LinkedIn using AI. Even when female candidates had comparable or superior skills, the algorithm was favoring male candidates for technical posts, according to internal tests. The biased historical employment practices, where men were more commonly hired for such posts, were unintentionally being learned by the AI. LinkedIn had to make systemic changes to guarantee more equitable recommendations.
3. AI Resume Screening by Google (Internal Testing)
When Google tried using AI to review resumes, it discovered that the algorithm was reproducing human biases identified in previous hiring data. For instance, it would give preference to candidates from historically favored schools or backgrounds, frequently at the expense of marginalized groups. Google chose to concentrate on bias prevention in upcoming projects rather than implementing the AI tool extensively as a result of these worries.
AI Hiring Bias’s Ethical and Legal Consequences
AI hiring bias has the potential to result in not only unfair employment judgments but also legal and ethical issues for your company.
1. Explainability and Transparency:
AI algorithms frequently function as “black boxes,” meaning that even its creators may not completely comprehend the decision-making process. It is challenging to recognize and address prejudices because of this lack of transparency. Recently, the Harvard-led Hire Aspirations Institute stressed that because users are unable to adequately examine or rectify opaque AI systems, prejudices are made worse.
2. Lawsuits for Discrimination and Regulatory Penalties
An applicant may file a lawsuit against your company for AI discrimination if they believe an AI system has treated them unfairly during the recruiting process. Your company may face expensive legal fights, fines, and damage to its reputation if it is proven to have engaged in discrimination or non-compliance. For instance, AI hiring tools may be subject to the General Data Protection Regulation (GDPR) of the European Union, which contains rules on automated decision-making.
3. Interaction between Humans and AI:
Human recruiters frequently depend too much on AI suggestions without carefully examining the outcomes. According to a World Economic Forum survey, recruiters blindly followed AI suggestions in 85% of AI-driven hiring decisions, without challenging their accuracy or fairness.
The Best Ways to Put Fair AI Recruiting Systems Into Practice
Organizations should adhere to a number of best practices in addition to the previously mentioned tactics in order to develop impartial and equitable AI hiring procedures.
1. Establishing a Diverse Development Team:
Ensuring diversity in the teams that develop and deploy AI systems is essential to lowering bias in these systems. Biases that homogeneous groups might overlook are more likely to be identified and addressed by diverse teams.
2. Working with External Auditors:
Employing outside auditors to examine AI systems for bias can offer a dispassionate evaluation of their impartiality. The Algorithmic Justice League is one of several groups that offers services for auditing AI systems and making suggestions for lessening bias.
3. Consider inclusivity when creating AI models:
In order for your AI team to detect various forms of prejudice, diversify it by including individuals from a range of genders, economic backgrounds, and races. Additionally, establish quantifiable objectives so that the AI models function similarly throughout the targeted use cases, such as for multiple age groups.
How Will AI Affect Hiring in the Future?
Future recruitment will probably see even more advanced techniques for spotting outstanding talent as AI technologies develop further. In the field of AI hiring, several trends are developing:
1. XAI, or explainable AI:
One interesting avenue is the creation of XAI models, which give recruiters insight into the decision-making process of AI tools. Explainable AI is commonly addressed in deep learning and is a fundamental part of the fairness, accountability, and transparency (FAT) machine learning paradigm. These tools are intended to assist HR managers in recognizing and more successfully overcome prejudices by offering insights into the standards that AI algorithms employ.
2. AI for Diversity and Inclusion:
Promoting diversity and inclusion in recruiting procedures will be a growing priority for future AI systems. Recruiters can create inclusive job descriptions and communications that appeal to a wide range of audiences by utilizing natural language processing. This will draw in a more diverse application pool.
3. Legal Frameworks for AI:
Organizations must be informed about compliance requirements as more nations and regions implement legal frameworks to control AI in hiring. The proposed AI Act from the European Union, for instance, would categorize AI systems according to their level of danger and place more stringent restrictions on high-risk systems, such as those used in hiring.
Final Thoughts
AI has the potential to completely change the employment process, but if it is not developed and applied properly, it also carries several serious concerns. Bias in AI hiring systems can result in unjust treatment, legal repercussions, and harm to one’s reputation. It can be caused by faulty algorithms, biased data, or a lack of transparency.
By creating diverse development teams, carrying out frequent audits, and embracing explainable AI, businesses can reduce these risks by placing a high priority on justice, inclusion, and accountability. Compliance and ethical responsibility will become unavoidable as legal frameworks change. AI will be used in hiring in the future to create inclusive, varied, and egalitarian workplaces in addition to increasing efficiency.
