Responsible use of ai in hiring: Ensuring fairness, transparency, and explainability

By Erinn Tarpey, Chief Marketing Officer at Beamery

AI is reshaping the hiring process – helping organizations sift through applications faster, identify better-fit candidates, and reduce bias by focusing on skills rather than traditional pedigree. 

Over two-thirds (67%) of professionals believe AI will play a key role in talent hiring strategies this year (Korn Ferry).

But as AI takes on a greater role in talent decisions, the imperative for responsible and ethical use grows stronger.

Applied well, AI can unlock more equitable outcomes in hiring. But applied poorly, it can perpetuate bias, create opacity, and erode trust. That’s why fairness, transparency, and explainability must be foundational principles – not afterthoughts – in the design and use of AI systems in HR.

Why AI In Hiring Needs Guardrails

AI systems are trained on data. If that data reflects historical bias – such as prioritizing graduates from elite universities or favoring specific demographic groups – those patterns can be baked into algorithms, even if unintentionally. Without careful oversight, AI may reinforce systemic inequities, not remove them.

The impact of a flawed hiring model isn’t abstract. It can mean a qualified candidate is never seen by a recruiter. Or that certain groups are consistently deprioritized without clear reason. This risk is compounded by the complexity and opacity of many AI models, especially when decision logic isn’t visible to those using or affected by the system.

To truly support fair, skills-based hiring, AI must be built and used responsibly – with clear standards for what “good” looks like.

Fairness: Focusing On Skills, Not Titles

Fairness in hiring starts with rethinking what makes someone qualified. Traditional hiring practices often rely on proxies like job titles, degrees, or past employers – criteria that are easily biased and don’t always reflect capability.

AI can help level the playing field by surfacing candidates based on their underlying skills, experience, and potential. But fairness isn’t automatic. It requires:

• Bias testing and mitigation: AI models should be regularly evaluated for disparate impact across gender, race, age, disability, and other protected characteristics.
• Inclusive training data: Algorithms should be trained on data sets that reflect the diversity of the real workforce – not just one narrow demographic.
• Human oversight: AI shouldn’t make hiring decisions in a vacuum. Recruiters and hiring managers must remain in the loop to catch edge cases and provide context.

Fair hiring isn’t about treating everyone the same – it’s about creating processes that give all candidates a truly equal opportunity to succeed.

Transparency: Shedding Light On How AI Works

One of the biggest barriers to trust in AI is opacity. Candidates may not know they’re being evaluated by algorithms. Recruiters may not fully understand how recommendations are generated. This lack of transparency can fuel suspicion and undermine confidence in the hiring process.

Responsible AI use demands greater clarity – both internally and externally. That means:

• Clear disclosure to candidates when AI tools are being used in the hiring process, and for what purpose.
• Auditability for teams using AI, with clear documentation on how models are trained, how decisions are made, and what safeguards are in place.
• Governance structures that define who is accountable for monitoring AI systems, addressing issues, and ensuring compliance with local regulations.

Transparency builds trust: but only if it’s meaningful, accessible, and embedded into the way teams work.

Explainability: Understanding The “Why” Behind AI Recommendations

Closely tied to transparency is explainability: the ability to understand and articulate why a model made a specific recommendation or decision. For example, what factors led to a candidate being given a particular rank or score for a given role? What is the weighting of those factors? 

Explainable AI is especially important for compliance. Regulatory frameworks in many regions (such as the EU AI Act and NYC Local Law 144) require that organizations can demonstrate how automated tools impact hiring outcomes and ensure those tools are not discriminatory.

Practical steps to improve explainability include:

• Using interpretable models where possible, or adding explanation layers to more complex ones.
• Providing recruiters with deeper insights around recommendations, not just scores.
• Giving candidates access to feedback, particularly when AI plays a role in decisions that affect them.

In hiring, explainability isn’t just a technical feature – it’s a matter of fairness and accountability.

Embedding Responsible AI In Hiring Practices

Building AI tools that are fair, transparent, and explainable isn’t just the responsibility of data scientists or engineers. It requires collaboration across HR, legal, compliance, and leadership teams to define what responsible AI use looks like in your context.

Key actions for organizations include:

• Establishing clear AI governance frameworks, including roles, responsibilities, and escalation paths.
• Involving diverse stakeholders in AI system design and vendor evaluation, to spot unintended consequences and improve inclusivity.
• Continuously monitoring AI systems for drift, bias, and effectiveness – and being willing to adjust or retire tools that no longer serve their purpose.
• Training recruiters and hiring managers to use AI tools responsibly, with an understanding of both their capabilities and their limitations.

Responsible AI use in hiring is not a one-time compliance task – it’s an ongoing commitment to fairness, inclusion, and better outcomes for candidates and organizations alike.

The Path Forward: Equitable Hiring Powered By Responsible AI

AI has the potential to dramatically improve how organizations attract, assess, and retain talent – especially when it comes to skills-first hiring. But that promise can only be realized if AI is used responsibly.

By prioritizing fairness, transparency, and explainability from the outset, organizations can build hiring processes that are not only faster and more efficient – but also more equitable, more human, and more aligned with the future of work.

Because in the age of AI, the question isn’t just what your technology can do. It’s whether it does the right thing.

— Erinn Tarpey is Chief Marketing Officer at Beamery. An expert in scaling B2B SaaS marketing for global enterprises, she leads the company’s brand, positioning, and go-to-market strategy. Erinn is recognized as an expert in HR and finance technology marketing, and works closely with enterprise organizations to connect marketing efforts with business outcomes. She has held senior roles at Visual Lease, iCIMS, and several SaaS procurement platforms. Prior to Beamery, she served as CMO at Visual Lease, where she led revenue-driving marketing initiatives and helped the company achieve significant growth during her tenure.