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A Guide to Writing an AI Policy for Your Organization

How HR can manage the use of AI in companies to mitigate risks.

Christopher Mannion
Systems engineer with 15 years experience in People Management, HR Analytics, and Talent Acquisition
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An HR professional draftin a workplace AI policy for her organization.
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As artificial intelligence is rapidly introduced across various sectors, we should expect its impact on HR and the workplace to be incredibly transformative. AI technologies, ranging from simple automation tools to complex machine learning algorithms, reshape how businesses operate, enhance productivity, and refine decision-making processes.

This evolution significantly shifts from traditional operational methods to more efficient, data-driven approaches. The potential of AI to augment human capabilities and streamline workflows is immense, promising to elevate organizational efficiency and redefine the nature of work itself.

But what does this mean for HR teams who must establish the policies and guidelines for AI's safe and ethical use? This article covers some ideas to help you create an AI policy for your organization.

In This Article

Why Workers Use AI

Traditional workplace processes, often characterized by manual data entry, cumbersome recruitment methods, and subjective decision-making, are fraught with inefficiencies.

These outdated methods not only consume valuable time but also leave room for errors and biases, ultimately affecting the overall productivity of organizations. In contrast, AI offers a compelling suite of solutions to these challenges. By automating routine tasks, AI technologies free human workers to focus on more complex and creative aspects of their jobs.

Benefits of AI for Productivity and Decision-Making Support

While the benefits of increased productivity can lead to substantial cost savings as headcount requirements decrease, adopting AI tools may start with individual employees seeking ways to improve their output. The two prominent use cases for front-line AI use are increased productivity and supporting decision-making. Here are some examples you may have seen already.

  • Generative AI can quickly generate personalized cold outreach emails, saving hundreds of hours of manual email writing each year.
  • Various data visualization tools make use of artificial intelligence to “read” spreadsheets and present the information as charts, graphs, and other presentation aids.
  • GPT interfaces can empower employees to quickly analyze large datasets using prompts to find patterns or summarize multiple points of view into a straightforward narrative

Good and Bad AI Use Cases

Using AI to perform everyday tasks works in some circumstances, but not all. Because the use of artificial intelligence is fraught with concerns like data hallucinations, inadvertent intellectual property theft, and breaches of data privacy, employers are concerned about how and when workers apply these tools.

The only way to govern the use of AI is to have clear workplace policies about what is and isn’t accepted.

Where AI Can Add Value to HR

Recruitment and Talent Acquisition

One of the most transformative applications of AI in HR is in recruitment, where generative AI is beginning to play a pivotal role.

AI recruiting tools can swiftly analyze thousands of resumes and conduct live interviews to score candidates against established job requirements. The HR team can also automate the screening process using HR chatbots to replace valuable recruiter time. This speeds up the hiring process and ensures that it is more objective, as AI algorithms can be designed to ignore demographic information, reducing unconscious bias in hiring practices.

Skills Analysis and Hiring

Furthermore, advancements in skills ontology, powered by AI, enable a more nuanced understanding of the skills landscape within and outside organizations. AI can map out skills across industries, identify skill gaps, suggest training for current employees, or pinpoint ideal candidates in the recruitment pipeline.

This approach supports skills-based hiring, which focuses on the capabilities of candidates rather than their educational background or previous job titles. Hiring for skill over qualifications expands the labor pool and promotes a more diverse workforce.

Employee Experience

AI also enhances employee engagement and retention through personalized experiences. AI-driven platforms can offer tailored learning and development opportunities, recommend career paths, and even predict which employees might be considering leaving the company, allowing HR to proactively address concerns and improve employee satisfaction.

Pitfalls to Avoid and Negative Implications of AI

Despite AI's promising applications in HR, organizations must navigate significant challenges and limitations.

Monitoring for AI Biases

A primary concern is the need for human oversight. No matter how advanced, AI algorithms lack the human touch often necessary in HR processes. For instance, while AI can shortlist candidates based on skills and experience, it may not fully grasp the nuances of cultural fit or personal attributes crucial for specific roles.

Another critical issue is the potential for bias in AI algorithms. While AI can potentially reduce human bias in decision-making processes, the algorithms can perpetuate or even exacerbate biases if trained on biased data sets. This can lead to unfair treatment of candidates or employees and harm an organization's reputation and diversity efforts. Human intervention is necessary to run bias audits within the AI’s results.

For example, an AI tool for workforce planning trained on a history of men getting promotions will perpetuate this in its recommendations. It is up to human resources professionals to ensure that women are considered fairly in internal mobility and employment decisions.

Data Privacy

Data privacy concerns also loom large in the use of AI in HR. If not handled securely, sensitive employee data and trade secrets can be exposed to breaches and misuse risks.

Organizations must ensure their AI systems comply with data protection laws and uphold the highest data privacy standards.

An HR professional using a laptop to write a workplace AI policy in an office environment.

Writing an AI Policy for Your Organization

Many HR teams are starting from zero due to the rapid development of front-line use cases for AI over the last year. Unlike other HR policies that have been around for many years, we’re figuring out workplace AI policies on a case-by-case basis. It is also wise to seek legal advice once a policy is drawn up.

When drafting an AI policy, leaders should look to address four key areas:

Purpose and Scope

  • Clearly define the objectives of using AI within the organization and the boundaries within which AI technologies should be available for employee use.
  • Decide to what extent you will allow personal AI tools to be used vs. enterprise-wide applications.

Data Management

  • Define what the organization deems confidential information and what information is protected by intellectual property rights.
  • Establish guidelines for data collection, storage, processing, and sharing to protect employee privacy.
  • How your team stores and manages sensitive data must comply with data protection laws in collaboration with legal and cybersecurity teams.

Transparency and Explainability

  • Ensure AI systems are transparent and their decisions can be explained to maintain trust and accountability.
  • Choosing the right vendor is critical to ensuring you have end-to-end visibility of how decisions are made and data is used.

Bias and Fairness

  • Implement measures to prevent and mitigate bias in AI algorithms to promote fairness and inclusivity in HR processes. This may involve additional training, audits, and external monitoring.
  • Build safeguards into workflows to calibrate AI outcomes with your industry's and jurisdiction's real-world requirements. For example, using an AI hiring tool, you must periodically monitor whether your hiring practices follow equal employment opportunity requirements.

What to Include

An effective AI policy should include the following elements:

  • Governance Structure: Outline the roles and responsibilities of those overseeing AI initiatives.
  • Ethical Principles: Commit to ethical standards, such as fairness, accountability, and transparency, guiding AI development and deployment.
  • Compliance with Laws and Regulations: Detail the legal frameworks governing AI use, including labor laws and privacy regulations, ensuring the organization remains compliant.
  • Employee Engagement and Training: Include provisions for educating employees about AI tools, their benefits, and potential risks, fostering a culture of informed use and innovation.

Ethical considerations should be at the heart of your AI policy, emphasizing respect for individual rights, non-discrimination, and privacy.

Establishing usage norms that reflect ethical values and complying with existing laws, such as GDPR in Europe, CCPA in California, and New York City Local Law 144 of 2021, is essential. Addressing compliance issues proactively can prevent legal pitfalls and reinforce your organization's commitment to ethical AI use.

What Employers Must Know About Ungoverned Use of AI

While some companies embrace AI tools, others have banned generative AI tools, like ChatGPT and Bard, and other forms of AI completely. This is a dangerous solution. Employees see the value in AI tools that perform tedious tasks in seconds. Employees tend to break rules that prohibit access to AI resources, outright or covertly.

Setting clear parameters and expectations regarding the use of AI is a more realistic expectation than withholding access to artificial intelligence altogether.

Understanding Capabilities and Limitations

For employers, understanding the capabilities and limitations of AI is crucial for its practical and ethical application. Implementing AI tools in HR processes, or anywhere else in the organization, requires realistic expectations of what AI can do.

AI has evolved from simple, rule-based algorithms to complex neural networks and Large Language Models (LLMs) capable of processing and analyzing vast amounts of data. This evolution has expanded AI's potential applications in the workplace, from predictive analytics in talent management to personalized employee learning and development programs.

The use of generative AI also means we can use AI-generated content in communications, presentations, and research.

However, the effectiveness of AI predictions, decisions, and output heavily relies on the quality and quantity of data it is trained on. Employers must recognize that AI is not infallible; its predictions are probabilistic, not deterministic.

This means that while AI can significantly enhance decision-making by providing insights based on data analysis, it cannot replace human judgment. Responsible use of AI tools means that we rely on them as aids in decision-making processes, not as replacements for human decision-makers.

The Potential Risks with Ungoverned or Uninformed Use of AI Tools

The critical risks for HR are based on uninformed or ungoverned use of AI tools. Reliance on biased models can lead to discriminatory hiring practices. At the same time, violating data privacy laws can result in legal repercussions and damage an organization's reputation. Moreover, an overreliance on AI can dehumanize HR processes, undermining the value of personal interaction and human judgment. This can have catastrophic consequences for the employer brand, making hiring and retaining great employees harder.

To mitigate these risks, employers must adopt a governed approach to AI use, ensuring that AI tools are transparent, explainable, and aligned with ethical guidelines and legal requirements. This involves understanding the capabilities and limitations of AI and actively monitoring its use to prevent misuse and ensure compliance with data protection laws.

Protecting Data Privacy

The increasing sophistication of cyber threats and the potential for AI systems to be exploited for malicious purposes means data privacy is more important than ever.

Data breaches involving personal information can lead to severe reputational damage, financial loss, and legal penalties. Moreover, the ethical implications of mishandling employee data can erode trust and morale within the organization, leading to a toxic workplace culture. Therefore, ensuring data privacy is not just a legal obligation but a critical component of ethical AI governance and a cornerstone of organizational integrity.

To safeguard sensitive information in the age of AI, organizations must adopt a comprehensive approach to data privacy encompassing legal compliance, technological safeguards, and organizational culture.

Here are five best practices for securing sensitive information:

Data Minimization

Collect only the data necessary for the specific AI application and avoid storing personal information that is not required. This principle of data minimization reduces the risk of data breaches. It ensures compliance with privacy laws that often mandate it.

Encryption and Anonymization

Employ robust encryption methods to protect data at rest and in transit.

Additionally, consider anonymizing data used for AI training and analysis so that it cannot be traced back to individual employees. This enhances security and mitigates privacy concerns associated with AI processing.

Access Controls

Implement strict access controls to ensure that only authorized personnel can access sensitive information. This includes using multi-factor authentication, role-based access, and regular audits of access logs to prevent unauthorized access and detect potential breaches.

Regular Security Assessments

Conduct regular security and penetration testing to identify vulnerabilities in AI systems and data storage infrastructures. This proactive approach allows organizations to address security gaps before they can be exploited.

Vendor Management

If third-party vendors are involved in the AI applications, ensure they adhere to strict data privacy and security standards. Conduct due diligence and include data protection clauses in contracts to hold vendors accountable for maintaining the confidentiality and integrity of sensitive information.

Many of these practices should already be part of a modern data security strategy. If you are an HR leader tasked with drafting your AI policy, ensure you work closely with subject matter experts from your engineering teams.

Final Thoughts on Writing a Workplace Policy for AI

As explored throughout this article, AI holds tremendous potential to transform workplace processes, from recruitment and talent management to employee engagement and decision-making. However, the journey toward fully realizing this potential is fraught with challenges, including ethical considerations, data privacy concerns, and the need for robust governance frameworks.

Adopting AI in HR applications requires a balanced approach that leverages the technology's capabilities while mitigating risks. Organizations must be proactive in understanding the capabilities and limitations of AI, ensuring data privacy, and monitoring AI usage to prevent unlawful or unethical practices. Developing a clear AI governance framework, informed by the key considerations and best practices outlined in this article, is essential for AI's ethical, lawful, and efficient use.

Christopher Mannion
Systems engineer with 15 years experience in People Management, HR Analytics, and Talent Acquisition
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Chris Mannion is the co-founder and CEO of Sonar Talent, a recruiting intelligence platform that lets recruiters access untapped talent to accelerate hiring. Chris holds an MBA from the MIT Sloan School of Management and has spent the last 15 years improving systems and teams across the world.

With experience ranging from aerospace engineering in the military, e-commerce supply chain operations, and building out the first talent acquisition COO function at Wayfair, Chris brings unique perspectives to recruiting. He is now drawing on his operational experience to launch a product that brings this capability to all recruiters

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