Artificial intelligence (AI) has become increasingly popular in how organizations hire. AI can help streamline resume screening and interviews, making recruiting more efficient for both employers and candidates. However, as with any new technology, it’s important to be mindful of its use and be aware of the potential challenges that can arise while using AI to hire your next employee.
Between candidate bias to legal issues, AI has a few pitfalls you need to be aware of as you incorporate more AI into your hiring process. All you need to do is watch out for them and manage the potential risks to truly get the results you want.
7 Pitfalls of AI and Hiring
Here are seven challenges with AI you must watch out for to mitigate the risks.
Pitfall #1: Over-reliance on AI
While AI is a great tool to enhance the hiring process, an over-reliance on it could be problematic. This is because AI systems may focus too much on keywords and specific skills, and end up overlooking other candidates who may be a good cultural fit, and have valuable soft skills or transferable skills simply because they didn’t “fit” into the specific algorithm.
Reminder: AI should be used to enhance or complement the process rather than replace human judgment. AI could be good for initial screening, but deeper hiring decisions should involve human input to make a clear decision and assess the qualities that AI systems might miss such as emotional intelligence, creativity and culture fit.
Pitfall #2: Lack of transparency
AI systems have algorithms to make decisions, which means the decision-making process is not transparent to the users. This is a challenge when employees and candidates want to know why they were not selected to move forward or why they were not selected for the next round. With AI making key decisions that can impact careers, the “why” should be known to improve the candidate experience and enable them to understand why the decisions were made so that they might adjust their job search.
Reminder: Invest in AI models that can provide insights into how decisions are made to be able to give clear explanations of why certain candidates were chosen or not chosen. It’s important to give feedback to people who want it and ensure that decisions are being made with ethical practices aligned with the company’s values.
Pitfall #3: Bias in algorithms
AI systems are usually trained using data patterns – which means that if the data has historical biases such as preferences for certain demographics like age or gender, AI will perpetuate those biases. This could potentially lead to discrimination and exclusion of qualified candidates from marginalized groups.
Reminder: Organizations should train their AI systems on diversity and ensure all groups are represented. Make sure to audit AI systems regularly to detect and correct biases when caught.
Pitfall #4: Privacy and security concerns
Because AI relies heavily on data to make decisions, this necessitates access to personal information about candidates (resumes, background, education) which means ensuring the privacy of candidates is the top priority and cannot be ignored.
Reminder: Throughout the hiring process, companies need to protect the information shared by candidates. Extra caution must be taken to secure this personal data, ensuring total compliance with data privacy regulations. Additionally, companies should also be transparent with how candidate data will be used and delete this data when it is no longer needed.
Pitfall #5: Compatibility and job description issues
If job descriptions are not specifically optimized for AI algorithms, it might not recognize the important details that are required from candidates. This could lead to a mismatch of candidates chosen or the exclusion of qualified candidates.
Reminder: Avoid writing vague job descriptions and make sure to use industry-standard terminology that can be easily understood by AI systems. Employers should also use AI tools that can optimize job descriptions that are compatible with both human and machine reading.
Pitfall #6: Legal and ethical risks
As mentioned, biases and other ethical issues may arise – unintentionally – leading to legal considerations and challenges for the company. As AI is being explored, there are dilemmas regarding privacy, fairness and transparency in the process.
Reminder: All organizations should be well informed of the implications of using AI and make sure that they are compliant with all rules, regulations and labor laws to prevent any mishaps in the future. Additionally, the AI systems must be updated and follow the guidelines set often to further prevent these issues.
Pitfall #7: AI training and maintenance
For the AI systems to be as accurate and effective as possible, ongoing training and maintenance must be scheduled to prevent these systems from becoming outdated. If these processes are not updated, it can lead to poor decisions, potential discrimination and other issues.
Reminder: Continuously monitor and audit AI systems and regularly update the algorithms for the systems to be as current as possible. Conduct the appropriate tests to ensure that the systems are up to speed and adjust as necessary.