Newsflash I Exploring Generative AI: Challenges and Opportunities for Employers and Employees


The advent of Generative AI promises to revolutionise work as we know it. So what do employers and employees have to look out for when navigating this fast-paced environment?

1. The power of Generative AI

Generative AI has emerged as a ground-breaking technology with the ability to generate data and content that closely resembles human-created materials. Its potential applications span various key areas, including human resources and talent management. Employers can utilise Generative AI to automate content creation, simulate human-like interactions, and develop creative works, revolutionising how businesses engage with customers and clients. However, this complexity comes with unique legal considerations that should not be underestimated.

As employers and employees explore the potential of Generative AI, they must recognise the transformative power it holds yet also exercise caution to avoid unintended consequences. There are two separate aspects that must be considered: Issues arising from data input, like accidental disclosure of confidential information and privacy concerns, on the one hand; and issues arising from Generative AI’s data output, on the other. The latter can range from the often opaque transparency and the unclear trustworthiness of generative systems to concerns regarding Intellectual Property rights or discrimination. In the following, we will examine these aspects using practical examples and conclude with suggestions on how to maximise safe adoption through comprehensive employee training and awareness.

2. Information input: safeguarding confidentiality

2.1. Confidential information

Trade secrets, know-how, and internal information are amongst modern companies’ most valuable assets. However, the adoption of Generative AI introduces distinct new concerns for employers. Consider scenarios where engineers, analysts, or sales personnel input sensitive company data into online tools, aiming to refine their calculations, predictions, or models. This extends beyond preserving confidential data – a risk emerges of disclosing secret information or generating erroneous outcomes, potentially cascading into far-reaching liabilities.

In some cases, these risks have already materialised, as there has been instances of sensitive trade secrets being uploaded to external servers via popular tools such as ChatGPT. Accidental leaks of internal information like source code have highlighted the urgency to reinforce internal security measures. In light of these developments, businesses must take a proactive approach to responsible Generative AI implementation, safeguarding data integrity and embracing the technology’s transformative potential in a cautious yet innovative manner.

2.2. Data privacy

Employers and employees must also prioritise personal data privacy and security when implementing Generative AI. On the one hand, the vast training datasets used when implementing an AI model may contain sensitive information about employees or customers. On the other hand, using third party models may result in privileged or confidential data being shared. Mishandling this data can result in data breaches, loss of customer trust, and legal liabilities. Employers should adhere to robust data protection measures, and update their data protection policies to deal with the new challenges of AI systems and data processing to stay compliant with relevant privacy laws, like the GDPR (EU) and the PDPA (Singapore).

As Generative AI evolves, employers should conduct thorough data impact assessments to understand the potential risks and ensure compliance with data protection regulations. Anonymisation and data encryption techniques can help protect sensitive data used in AI model training, minimising the risk of unauthorised access or data leaks. Moreover, clear policies and procedures should be established to govern the handling of AI-generated content and the secure disposal of data after its use.

3. Information output: building reliable trust

3.1. Lack of transparency and explainability

Generative AI models often operate as “black boxes”, lacking transparency and explainability in their decision-making processes. This lack of insight into how AI systems arrive at their conclusions raises concerns about accountability. Employers may encounter challenges in justifying AI-generated decisions to stakeholders, regulatory bodies, or even their own employees. Striving for transparency and implementing mechanisms to explain AI-generated outcomes is essential to build trust and maintain ethical standards.

Employers should prioritise AI model interpretability and transparency in their AI adoption strategies. By using techniques such as interpretable machine learning models or decision-making frameworks, employers can gain more visibility into AI-generated content and better understand the rationale behind specific outcomes. Demonstrating accountability and a commitment to explainable AI practices can help employers navigate potential legal and regulatory challenges effectively.

3.2. Output trustworthiness

Due to the nature of the predictive natural language processing algorithm underlying Generative AI, caution should be exercised with regards to the trustworthiness of the content it generates. Generative AI models do not conduct logical operations, instead relying solely on probability of a certain outcome being the most likely. This can be mitigated to a certain extent by training, yet the models in Generative AI may often still “hallucinate” (i.e. make up) facts, figures, and random information.

These issues highlight important implications, which can be illustrated through the example of the popular ChatGPT chatbot. It has been trained solely on substantial textual data, and draws from its learned patterns to generate output resembling its training data. Therefore it cannot be solely relied upon for delivering accurate facts. It lacks the ability to independently verify the accuracy of the generated content. This limitation extends to straightforward logical operations, such as calculations. ChatGPT prioritises probable responses rather than performing actual computations, offering answers based on its training and not on logical analysis.

As a result ChatGPT is better suited for creative work and exploratory discussions rather than precise factual inquiries. When seeking concrete answers in fields like natural sciences or historical events, ChatGPT’s responses will very likely not guarantee accuracy. Similarly, when dealing with intricate mathematical equations, engineering problems or legal enquiries, relying on ChatGPT leads to potentially incorrect or misleading outcomes.

3.3. Intellectual property concerns

The capabilities of Generative AI to produce content that resembles original works pose intellectual property challenges. Employers must be cautious when using AI-generated materials, as they could inadvertently infringe upon copyright, trademark, or patent laws. Determining the ownership of AI-generated content and ensuring proper attribution can be challenging.

Employers should also be wary of potential copyright infringement during the training of AI models. The data used for training, if not properly sourced or attributed, could include copyrighted material, leading to unintentional violations. Developing clear policies regarding the use of AI-generated content is essential, encompassing not only its application but also its creation. Properly obtaining necessary permissions for data usage can help mitigate potential legal risks.

Fostering a culture of respect for intellectual property rights within the organisation is equally vital. Educating employees about copyright and intellectual property laws can prevent unintentional infringement. Moreover, staying updated on emerging laws and regulations related to AI-generated content is essential to remain compliant with evolving legal standards. By proactively addressing intellectual property concerns, employers can harness the transformative power of Generative AI while safeguarding their business from potential legal complications.

3.4. Potential discrimination and bias

A further important concern associated with Generative AI is the potential for discrimination and bias in the content it generates. AI models are trained on vast datasets, and if those datasets contain biased information, the AI can perpetuate or amplify existing biases. When used in recruitment or talent assessment, Generative AI may inadvertently favour certain demographics or exclude others, leading to discriminatory hiring practices, incompliance with anti-discrimination laws and guidelines and reputational damage. Employers must be vigilant to avoid perpetuating bias and ensure fairness in their AI-powered decisions.

To address potential bias and discrimination in AI-assisted recruitment, employers should prioritise diversity and inclusion in the data used for training, ensuring it is representative of the concerned population as a whole. Regular audits and evaluations of the AI model’s performance can help identify and rectify any biases that may arise. Employers must also implement thorough testing and validation protocols to ensure fairness and non-discrimination in AI-driven decisions.

4. Employee training and awareness

Across the wide range of issues mentioned, ensuring employee training and awareness regarding the implementation of Generative AI is paramount to mitigate potential risks and pitfalls. By conducting comprehensive training programs, employees can better understand the considerations and legal responsibilities surrounding AI adoption. Increased awareness fosters a culture of responsible AI usage, empowering employees to make informed decisions and effectively address any challenges that may arise. Moreover, clear communication on AI-generated content usage and data privacy practices builds trust and transparency within the organisation, creating a cohesive approach towards the responsible integration of Generative AI in day-to-day operations.

5. Conclusion

Navigating the landscape of generative AI’s potential demands a nuanced understanding of its complexities. As employers embrace its transformative capabilities, they must strike a balance between innovation and accountability. By prioritising confidential data input and trustworthy data output as well as focusing on training and awareness in the workforce, businesses can confidently navigate the AI-powered future, ensuring that progress is driven by informed decisions and practical wisdom. Employers are well advised to update their policies and practices, and train their employees with regards to the challenges that come with the usage of tools that are powered by (Generative) AI. We are happy to assist.

Contact Persons
Dr. Maximilian Kressner, M.Jur. (Oxford)

Dr. Maximilian Kressner, M.Jur. (Oxford)
+65 6408 8000

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