Imagine you’ve done the necessary groundwork for your dream job. You feel confident for the role as your qualifications are perfect, but instead of a recruiter going through your document, AI does—and you don’t get a callback. Why is that? The AI drew from past data and patterns that in the the past used to favor certain applicants over others, and your profile unfortunately didn’t fit the norm.
It’s not a dystopian future; this is happening as we speak. Hiring is fast-paced, and AI is transforming everything around it, from improving performance evaluations to even predicting employee turnover. But alas, there lies a problem. AI learning from information containing historical databases is not fair. Spoiler: It usually does. If AI reflects and even amplifies those biases, it simply results in employees being discriminated against.
Now, the question is: How do you ensure AI makes unbiased, fair decisions? In this blog, we will discuss how to ensure human resource AI doesn’t turn out to be discriminatory but remains ethical. We strive for no-bias policies and true helpfulness.
Why Ethical AI in HR Matters
AI enhances HR productivity, but if these efficiencies are achieved at the expense of fairness, it may cause great harm. These decisions impact a person’s career progression and income, hence if AI systems are unsympathetic in their actions, be it in hiring new employees, promotions, or even appraisals, the consequences can be dire.
It is equally well known that AI is not without prejudice.
- Amazon’s AI Hiring System (2018): An AI program designed by Amazon to help sift through resumes was halted because it was consistently rating women’s applications as less favorable. How? The AI was trained on resumes from past hires, most of whom were male.
- LinkedIn’s Job Recommender AI (2022): A study revealed that LinkedIn’s AI was biased towards recommending higher-paying jobs to men as opposed to women with equal qualifications.
- Facial Recognition Bias in Hiring: Video interviewing tools have been developed using AI that unconsciously discriminate against candidates with particular facial features, accents, and speech patterns that are less common.
These examples illustrate the need for ethics in AI management with regard to HR. In the absence of those principles, AI risks perpetuating, rather than remedying, HR’s problems.
So, what are the biggest challenges in ensuring AI makes fair and unbiased decisions?
Key Ethical Challenges in AI-Driven HR— and How to Fix Them
Integrating artificial intelligence with human resources increases efficiency, insights, and automation of repetitive tasks such as hiring and promotions. Yet, AI has some major problems including ethical concerns as it uses data that is often biased. If left unchecked, AI systems can reinforce racism, and sexism, make partial decisions, invade privacy, and evaluate employees in a biased manner.
Let’s break down these key ethical concerns and explore how HR teams can address them.
Bias in AI Training Data: The Past Shouldn’t Shape the Future
AI has to make historical data-based decisions and therefore, the problem stems from historical data that has biases and unfair stereotypes, like discrimination. AI doesn’t have any intention to discriminate, however, when it is trained with biased data, it will intentionally use discriminatory practices.
Consider, for example, an organization that has had a long-standing practice of prioritizing men to fill its senior leadership roles. If such an AI system is trained, it would amplify the correlation of leadership attributes by assuming that men outperform women by a long margin and are, therefore, more useful. If this happens, any woman candidate who is equally qualified to a male candidate would not get the opportunity which can be termed as discrimination.
And the example is racial or educational discrimination. If previous hiring patterns suggest a bias towards specific universities, AI might unduly neglect candidates from other universities even if they possess the requisite skills.
How Do We Fix It?
- Use diverse and representative datasets: AI systems must be developed based on data covering a wide range of demographics, experiences, and backgrounds. HR departments should partner with data specialists to achieve this.
- Conduct regular bias audits: Companies have to track the outcomes of AI-assisted recruitment over a certain period to see who has been hired, promoted, or rejected. They also need to look for emerging patterns of bias for any necessary corrective action.
- Apply bias correction technique: AI could be designed to actively counter biases, such as ensuring that data represent underrepresented groups by changing the data’s weight proportions.
AI should promote greater workforce diversity rather than perpetuating historical disparities. This, however, needs proper supervision.
The “Black Box” Problem: AI Decisions Should Not Be a Mystery
AI is perceived as a neutral device, which does not mean it is transparent. Most AI models, particularly deep learning models, function as “black boxes.” They utilize data to make decisions but do not provide any rationale to support the conclusions they arrive at.
A situation like this is an HR nightmare. Picture a candidate being filtered out by an AI screening system, only for them to later ask the question—Why? HR lacks enough information to provide an adequate response to that, which is ethically concerning. What factors did the AI take into account – were they pertinent? Did unconscious bias creep in? The unfortunate truth is that there is no answer to that because of a lack of clarity.
Not providing an explanation impacts much more than hiring – performance reviews, assessments, and even promotions are affected as well. The more concerning part is if a leadership role is offered and the recommendation provided is “the system suggested it,’’ that’s a huge red flag.
How Do We Fix It?
- Use Explainable AI (XAI): Unlike others, these systems provide an explanation of decision-making and record which factors played the greatest role.
- Require human oversight: AI shouldn’t have full autonomy when it comes to making decisions about a position’s hiring or promotions. An AI suggestion must always be subject to review and can be ignored by a human resources professional.
- Establish AI accountability policies: Businesses should specify under which conditions, if any, AI system claims can be contested. There has to be an appeal window for AI-powered applicant tracking systems that deny candidates’ applications for a particular role – someone must examine and possibly overturn the initial verdict.
HR must know why an AI made a specific choice. If they do not, then AI should not be allowed to make decisions.
Privacy & Data Protection: How Much Monitoring is Too Much?
Before, AI technology in ‘human resources’ was more concerned with the employment aspect, but now, it is starting to expand into employee surveillance. Companies don’t only have an endless collection of data in documents like CVs, performance reviews, emails, and Slack messages, but even facial expressions from video interviews. How far can a company go in monitoring its employees before crossing ethical boundaries?
Some organizations employ AI technologies to predict the likelihood of employees resigning by tracking their email usage, message sentiments, and even their attendance in meetings. Others calculate productivity by counting the number of keystrokes or by simply watching an employee’s eye movement on a screen. Although these tools have their advantages, they pose significant moral problems.
Is it appropriate for an employer to listen to privately held conversations when they are listening for productivity clues? Should AI be allowed to detect if a worker’s tone in emails is suggesting alienation? Where is the distinction between engaging and critically insightful observations that hamper freedom?
How Do We Fix It?
- Follow strict data privacy laws (like GDPR and CCPA): In this case, employees should be made aware of what information is being collected, why data is being captured, and how it is intended to be used.
- Use data anonymization: Insights can be drawn from data without needing personally identifiable information. In this case, data anonymization serves to protect employee privacy.
- Give employees control: Employees have the right to opt out of any AI monitoring that is too intrusive such as analyzing private communications or personal behaviors.
AI should aim to support employees rather than make them feel like they are being watched.
AI In Performance Evaluations: Can AI Measure Success Fairly?
Though AI has been used to assess employee performance in many businesses, doing so will only prove to be problematic if AI is fully relied on for the assessment.
For instance, AI can assess an employee’s performance by analyzing the number of emails he/she sends, how quickly he/she responds to the messages, and other similar metrics. But does this show how effective someone is at their job?
What about the employee who performs better with deep focus and thus spends less time in meetings but produces outstanding work? What if the person is in a creative role whose success is not tied to any metrics, for instance, response speed? Employees with different work styles can be unfairly punished if AI focuses solely on numbers.
There is also the issue that AI may ignore the many aspects of work that are not visible, like mentorship, problem-solving, or innovative thinking, which are important and sometimes find expression outside of the traditional metrics.
How Do We Fix It?
- Use AI as a tool, not the final judge: AI can offer useful information, but the final assessment should always be a matter of human judgment.
- Allow employees to challenge AI-based assessments: There must be a defined procedure for review by human management beyond just the automated system.
- Incorporate qualitative factors: AI must be programmed to identify and consider non-measurable attributes like collaboration, imagination, and ability to lead as value addition.
Performance evaluation should be done in a way that captures the entire contribution of the employee, not just the easiest-to-measure aspects.
How to Ensure AI in HR Makes Fair and Unbiased Decisions
The use of AI in human resources is extremely beneficial – automating the recruitment process, and talent management, and helping the organization in important decision-making. However, AI still carries the risks of bias, unethical practices, and lack of transparency. The only answer is to shift towards a more responsible use of artificial intelligence.
Below is how HR leaders can mitigate the chances of unfair, unethical, and anti-human decisions when it comes to AI usage.
Conduct Regular AI Audits to Detect Bias
AI models are not fixed, they evolve with data and real-world patterns. The machine learning model can, in the long run, develop some bias that affects some groups negatively even if there were no processes made to enable this behavior. That is why auditing is important.
What should HR teams check for?
- Compare AI hiring decisions with company diversity goals: Is there demographic discrimination by the AI? If the business is striving for diversity and the AI is shortlisting candidates from a particular set of people, interventions need to be made.
- Run fairness tests: HR needs to create different candidate profiles to test if AI scores certain demographic groups lower. For instance, do resumes with feminine names score lower than those with masculine names?
- Monitor promotion and performance decisions: If AI is involved in career progression, HR should follow the patterns. Are known underrepresented employees being ignored? Are some demonstrated work styles being overly punished?
- Make adjustments when biases are detected: A more representative dataset can be used to re-train AI models to make sure there is no bias.
AI should be treated just as another HR staff. Its performance should be assessed like how any other employee is evaluated.
Keep Humans in the Loop – AI Shouldn’t Replace HR
AI is capable of performing several HR tasks such as analyzing CVs, predicting key performance indicators of employees, and making hiring recommendations but it should never be the one to make final decisions. No matter how sophisticated AI is, it will never exercise human intelligence, emotions, and morality ethics which are deeply required in making human resource management decisions.
That is the reason why a “Human in the Loop” is crucial.
How does this work in practice?
- AI provides recommendations, but humans make the final call: AI should be able to recommend specific candidates to supervisors and recruiters, however, the supervisors and recruiters should make the decision after reviewing each case.
- Recruiters train AI over time: AI technologies improve when human resource professionals provide input. If AI gives a flawed recommendation, HR needs to step in and correct the system’s mistakes.
Follow Established Ethical AI Guidelines
Some ethics policies have already been created so that HR teams do not have to come up with them from scratch. Such policies focus on the responsible use of AI, including issues of accuracy, fairness, and accountability.
Here are some of the most widely respected frameworks HR can use:
- IEEE Ethically Aligned Design Guide: Provides support for the ethics associated with Artificial Intelligence development and decision-making. Also describes how to ensure AI operates without violating human values.
- EU AI Ethics Guidelines: Focuses on transparency, explainability, and accountability in AI-driven decisions. Particularly useful for companies operating in Europe.
- SHRM AI Ethics in HR Framework: This is designed for Human Resource (HR) professionals and shows how AI can be ethically implemented in recruitment, performance assessment, and even surveillance.
How can HR apply these guidelines?
- Ensure AI decisions are explainable: For example, if a candidate is eliminated or an employee is not promoted, the HR manager should be able to justify the suggestion made by the AI.
- Prioritize fairness in AI design: There should be collaboration with data scientists to create AI systems that do not perpetuate bias against certain demographics.
- Make AI accountability a company policy: Outline how AI decisions will be checked and who can change them.
With the help of ethical Artificial Intelligence technologies, HR professionals can guarantee that AI is utilized in a balanced manner.
Train HR Teams on AI Ethics & Bias Detection
AI could seem like a tech-driven tool, but its impact is profoundly social. That is why HR professionals need to be trained in AI ethics so that they comprehend how the system works, how to detect bias, and take action accordingly.
What should HR teams learn?
- How AI makes hiring and evaluation decisions: An HR professional must know the reasoning AI uses, such as its logic in reviewing resumes, candidate ranking, and employee performance evaluation.
- How to identify AI bias in HR processes: AI’s prejudice is not always overt. HR teams should learn to recognize more hidden forms of bias, such as AI-preferred candidates who come from certain schools, AI-ignored candidates from minority groups, or AI- over punished lenient working conditions.
- When and how to override AI recommendations: AI making biased or wrong decisions should encourage the HR team to intervene.
How to implement AI ethics training in HR?
- Workshops and Courses: Have HR teams trained on the ethical use of AI by bringing in AI ethics experts.
- Hands-on bias detection exercises: Create mock scenarios of AI hiring and assess them for embedded biases.
- Continuous Learning: Since AI is constantly changing, HR teams need to keep learning about new and improved practices and regulations.
HR is essential in ensuring that ethical practices are put forth and AI as a technology is only as ethical as the people controlling it.
Conclusion
When it comes to AI’s application in Human Resources, it isn’t simply about improving productivity; it is about equality in the workplace. Used the right way, AI can mitigate bias, encourage diversity, and increase productivity. On the downside, the lack of ethical measures could increase prejudice and distrust on a large scale.
At Auzmor, we think that AI shouldn’t just be a replacement but should enhance the interaction between humans and HR professionals. Compliance shouldn’t be the goal of ethical AI, rather, it should implement real people-centric policies by fostering diversity and inclusion.
It is up to us how the integration of AI in HR functions will look like. Let’s make sure that the path to the future is paved with equity.
