Ethical considerations for AI implementation in human resources
As the use of AI in human resources continues to grow, HR leaders must remain alert to ethical considerations. HR processes impact people’s livelihood, such as who gets hired, who receives a promotion, and what career path the company suggests an employee follow. These things are important and demand fair decision-making.
No doubt exists that AI systems possess the potential to assist HR teams in many ways. Talent acquisition tasks such as composing job descriptions, sourcing candidates, and screening resumes become less daunting. Time saved allows focusing on other responsibilities.
However, remember that AI is artificial intelligence. The success of its output depends on the quality of its input. Thus, HR professionals must stay alert to the information provided by AI tools because humans are ultimately accountable for results.
Let’s look at some ethical concerns about AI use in human resources.
Bias and fairness
People desire a level playing field. They want employers to make decisions based on individual merits and potential.
Companies likewise benefit from selecting the most qualified person to fill an opening or receive a promotion. Their talents contribute to its success.
Through the years, many organizations have tried to keep human bias from affecting their decision-making processes. Actions such as removing names from resumes while screening lower the odds of potential biases based on gender or ethnicity.
In theory, AI should be capable of superior neutrality. Remember, though, that AI learns from what information it receives.
AI models and machine learning algorithms trained on historical data can inherit and amplify existing biases. This problem spells trouble for specific demographics, leading to unfair outcomes in hiring, promotions, and performance evaluations.
Here’s a real-life example:
About 10 years ago, Amazon attempted to create an AI tool to assist in hiring for technical jobs. The company discovered its AI application downgraded resumes that included the word “women’s” (as in “women’s college” or “captain of the women’s soccer team”).
The problem stemmed from gender bias in the data on which AI was trained. As reported by the ACLU, “The existing pool of Amazon software engineers is overwhelmingly male, and the new software was fed data about those engineers’ resumes.
If you ask the software to discover other resumes that look like the resumes in a ‘training’ data set, reproducing the demographics of the existing workforce is virtually guaranteed.”
As this example demonstrates, the choice of data to train AI algorithms is crucial. Biased or incomplete data sets can produce skewed results.
Ethical use of AI demands careful consideration of the information provided for training. Companies must seek diverse and representative data to input, and HR leaders must regularly monitor outcomes to identify potential problems.
Privacy and data security
HR departments traditionally have dealt with a variety of private employee information. HR professionals entrusted with access hold a responsibility to those they serve to protect confidentiality and inform them on how data is used.
Gathering extensive employee data through AI systems raises significant privacy concerns. Responsible handling of this data is essential to avoid violations.
Take data protection seriously. Areas to focus on include:
- Encryption
- Access control
- Regular security audits
- Breaches
- Transparency
- Informed consent
Transparency and explainability
The quick rise of AI in recent years yields widespread mistrust. Many people do not fully understand AI and fear its effects. ”AI anxiety” has become a popular topic of discussion.
The difficulty in understanding how AI reaches decisions creates skepticism and concerns about unfair treatment. Building trust requires employers to be open and honest about using AI. They must provide employees with clear, understandable explanations for AI-powered decisions. They also need to ensure workers are under diligent human oversight.
Human interaction and judgment
Replacing human judgment entirely with AI in critical HR processes can lead to neglecting nuances and context.
Consider, for example, a job candidate with a work history gap. AI might downgrade the resume due to the lack of progression. A human reviewer can analyze the situation – perhaps the applicant took time off to raise a child, care for an aging parent, or go back to school – and rectify the unfair penalization.
Or, what about this potential risk? Consider what could happen with automated performance assessments. Algorithms that only measure output could overlook important aspects of employee contribution, such as effort and collaboration. Adding input from the person’s supervisor paints a more complete picture.
Or, think about employee development. AI-driven career path recommendations might not consider individual aspirations and needs. Algorithms that only recommend paths based on past performance remove preferences, ambitions, and other factors that decide which route to pursue.
Maintaining human involvement in decision-making is essential to ensure ethical considerations get addressed. The involvement of “real people” also allows for factors beyond metrics, such as impressions and gut feelings, to enter the equation.
Best practices for ethical AI
AI advancements occur regularly and will expand enormously over the coming years. Wise companies develop solid ethical standards now and add to them as ethical challenges arise in the future.
- Develop diverse datasets
Mitigate bias by ensuring training data represents a wide range of demographics.
- Regularly monitor and audit
Continuously evaluate AI systems for potential bias, privacy concerns, etc. Adjust as needed. Seek expert third-party assistance for complex issues.
- Seek transparency in communication
Inform employees about how AI is being used in HR processes. They deserve to know, and sharing this information builds trust.
- Prioritize ethical guidelines and training
Establish clear ethical guidelines for AI development. Stay up to date on industry standards and policies regarding AI ethics. Train HR professionals on responsible AI practices.
- Remember the importance of human oversight
Lastly, avoid the temptation to “blame the tech” for undesirable outcomes. Company leaders, HR professionals, developers, and vendors play roles in AI usage and effectiveness. All stakeholders bear responsibility.
Treat AI as a companion to human decision-making, not the ultimate authority. Humans must remain at the helm.
More resources:
AI recruitment: Benefits, drawbacks and best practices 
AI job descriptions: How to write them faster and avoid bias
Understanding the AI impact on employee workplace expectations
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