The use of AI during the hiring process has its advantages and disadvantages, both from the candidate’s perspective, as well as from the perspective of the hiring company. Here at Bright Purple, we are paying particular attention to the ethical use of AI in recruitment.
Our recent poll on ethical concerns related to the use of AI showed that the top concern in our audience is data privacy and consent, followed by potential bias in decision making.
The use of AI is here to stay, so it will be important to address concerns as early as possible in the process. Below you will find tips how to address your concerns, both for candidates and companies. Please leave your comments below so we can continue this conversation.
Please read the Bright Purple GDPR Agreement for candidates here.
From the Candidate’s Perspective
✅ Pros
• Better responsiveness and speed: When a hiring company uses AI tools to screen CVs or match candidates, you may benefit from faster feedback and a more streamlined experience.
• More objective screening: In theory, if the algorithm is properly designed, it can focus more on skills and experience rather than subjective first impressions.
• Improved matching: AI can help align you with roles you might not otherwise have discovered, potentially widening the opportunity set.
⚠️ Cons
• Transparency gap: You might not be fully aware of what data is being gathered (résumé, online profiles, video-interview traits) or how it’s analysed. According to recent commentary, transparency and explainability remain major concerns. (Browne Jacobson)
• Consent and control: You might give data once (upload your CV, complete your profile) but lose track of how long it is retained, reused, or shared. “Indirect collection” of personal information (e.g., from social media) is flagged as a risk. (The Barrister Group)
• Bias and automated decision-making: If decisions are fully automated (or heavily influenced by an algorithm) you may feel your humanity is reduced to a score or a data point. Biases in data training can carry through. (Browne Jacobson)
• Data minimisation & purpose limitation: Do you know exactly what data is essential versus excessive? The principle of collecting only what is needed is emphasised by privacy guidance. (Browne Jacobson)
• Right to challenge: Under some jurisdictions (e.g. EU/UK) if an automated decision significantly affects you, you may have rights—but awareness of them is often low. (LSE Blogs)
🔍 Summary
As a candidate, you stand to gain from AI-driven recruitment (faster process, better role-match) but you also face real concerns around knowing what’s happening, consenting meaningfully, being treated fairly, and having recourse if something goes wrong.
👉 If you are applying for jobs in organisations using AI: ask (or look for) clear statements about data use and algorithmic processes.
From the Hiring Company’s Perspective
✅ Pros
• Efficiency and scale: AI tools can help sift large volumes of applications, freeing HR teams to focus on higher-value human tasks.
• Data-driven decisions: With appropriate data and tools you might achieve more consistent screening, identify patterns or skills that human screeners might overlook.
• Competitive advantage: Using leading-edge tools may signal a forward-looking employer brand and help attract digitally-savvy talent.
⚠️ Cons
• Compliance risk and privacy obligations: When using AI in recruitment you bear responsibilities around data protection (e.g., lawful basis for processing, transparency, rights of individuals). For example, the UK regulator stresses that “if not used lawfully … AI tools may negatively impact jobseekers who could be unfairly excluded … or have their privacy compromised.” (ICO)
• Transparency, auditability and bias: You will need to monitor your AI systems for bias, ensure you explain how decision-making works (at least sufficiently) and conduct risk assessments. (Browne Jacobson)
• Data-minimisation and purpose limitation: Using an AI tool does not absolve you from just collecting what you truly need and using it only for the intended purpose. (Browne Jacobson)
• Candidate trust and reputational risk: If candidates feel they haven’t consented, don’t understand what’s happening, or perceive unfairness, this can damage employer brand.
• Complexity and vendor risk: If you buy third-party AI tools you must ensure their processes, training data and outputs align with your privacy obligations (you may act as data controller, vendor may act as processor, etc). (Browne Jacobson)
🧭 Practical tips for hiring companies
• Publish clear notices: what data is collected, how it is processed (especially by AI), how long it is retained.
• Secure proper consent or lawful basis: especially if profiling or automated decision-making is used.
• Conduct a Data Protection Impact Assessment (DPIA) for AI recruitment solutions. (Browne Jacobson)
• Monitor and audit for bias and accuracy: don’t assume the model is fair indefinitely.
• Make human oversight possible: for decisions with significant impact, ensure a human-in-the-loop or appeal mechanism.
📝 Conclusion
Using AI in recruitment brings real benefits: speed, scalability, potential for improved matching and reduced manual bias. But those benefits are only responsibly delivered when data privacy and consent are embedded from the start.
From the candidate’s side you should expect clarity, control and fairness. From the hiring-company’s side you must build transparent processes, lawful data usage, continuous oversight and candidate-centric trust.
The future of recruiting is hybrid: AI + humans. Embracing that future means balancing efficiency with ethics.