The Expanding Role of AI Across the Employment Lifecycle

From recruitment to workforce development, AI can be utilised to drive efficiencies across the entire employment lifecycle. Effective use of AI can reduce workload, enable teams to focus time and energy where it’s most needed, and also transform how teams manage work. For human resource management teams, some of the ways AI can be used include:

• Screening large volumes of resumes to extract key skills and match candidates' experience to job descriptions
• Scanning public profiles and job platforms to identify potential company fits
• Recommending learning paths and stretch assignments based on an employee’s role, performance, and career aspirations
• Performing a gap analysis of organisational skills and opportunities
• Identifying patterns in employees' performance data
• Providing HR assistance to build policies, programs, and
• Assisting in the payroll process

While AI is a powerful tool to aid human resource management teams, its integration into the workforce presents legal and compliance challenges.

Bias and Discrimination Risks

Large language models (LLM) are advanced machine learning models that are designed to understand and generate human language. They are trained on a vast amount of data and derive answers based on these data inputs. While answers from LLMs can read ‘human’ and include mannerisms and language not too dissimilar from any other colleague, LLMs are incapable of self-consciousness and emotional intelligence. Answers are derived from a mathematical ‘truth’ or probability based on its dataset. This means that if there is bias within the data LLMs are trained on, they are incapable of distinguishing that bias from a factual truth. It is inevitable that any answer generated by an LLM will reflect errors and biases that occur within its training data, and it is nearly impossible to provide an adequate amount of unbiased data to an LLM. In an employment space, AI has been found to perpetuate gender and race discrimination in its hiring processes and can pose risks to an organisation’s compliance with anti-discrimination, human rights and employment laws.^[1]  An organisation intending to utilise AI in its employment processes may be at risk of perpetuating bias within its organisation without adequate controls and systems in place to mitigate it.

Workplace Surveillance, Approval, and Protection of Workers’ Privacy

Compared to the days of filing cabinets and punch cards, much has evolved at work. Contemporary workplaces operate with a plethora of digital dashboards and algorithmic management systems tracking how long a worker takes to complete a task, answer emails, and even moments of inactivity. Productivity, engagement, and prediction of potentially reaching burnout are increasingly carried out through management by AI and data analytics. While these tools have also improved efficiency, they raise some fundamental questions about trust, autonomy, and how far monitoring and control need to go.

Today, AI-powered monitoring systems are a common feature of remote and hybrid workspaces. The tools gather data input via everything from the patterns a person types on to e-mail behaviour to biometric inputs like facial recognition or voice analysis. Sometimes, monitoring extends into workers’ homes, blurring the line between professional and personal life. The data is then analysed in search of trends, underperformance, or predictions of disengagement. Such insights can indeed help teams work well and take care of employee well-being, but biased conclusions are also a possibility. For example, slower task completion may be perceived as poor performance, even if one is managing health conditions or caregiving responsibilities.

Internal policies and privacy audits form an important part of responsible data use. Policies should detail what data is gathered, for what purpose, who has access to it, and employees' rights in terms of viewing or correcting their information. Privacy audits check for compliance, find the gaps, and minimise the risks of non-compliance. In the same way, AI-use policies set ethical boundaries, ensure accountability, and guarantee fairness in AI-driven decision-making. Organisations should train employees on these policies, review them periodically, and keep them aligned with the requirements for legality and ethics.

In sum, AI-driven surveillance will enhance efficiency, support employees' well-being, and engender more trust when it is transparent and ethical. Conversely, misuse of these tools can take a devastating toll on morale, compromise privacy, and harm organisational culture. This deliberate, human-centred approach means having clearly articulated policies and regular audits to review workplace monitoring's balance of technological innovation with respect for employee rights, privacy, and wellbeing.