Despite the undoubted potential of generative AI, risks and concerns are associated with its use, especially in a business environment. Here we take a closer look at some of these risks and how to manage them:
Legal and regulatory risks
: Generative AI may raise legal and regulatory issues. Data security, privacy, liability, and compliance issues may arise. Specific legal regulations are not yet in place, which may change rapidly.
Organizations must ensure they comply with all relevant laws and regulations and assess the potential risks of using AI-based systems. This requires thorough research and keeping up to date with developments in the relevant regulatory framework.
Loss of control over generated content:
Generative AI models may generate content that does not meet the organization’s expectations or standards. There is a risk that inaccurate or inappropriate content will be generated, damaging the company’s reputation or resulting in legal consequences.
Organizations must ensure that they have sufficient control mechanisms to review and adjust the quality and appropriateness of the content generated. Incorporating human reviewers or moderators into the content generation process can be a valuable control mechanism. Human reviewers can review and verify the output of AI models to ensure accuracy, relevance, and alignment with organizational standards.
Generative AI can raise ethical issues, particularly related to bias, discrimination, or manipulative practices. AI models learn from data and may reinforce existing biases or inequalities in the data.
Organizations need to ensure that they have ethical policies and procedures in place to ensure that generative AI is used responsibly and does not negatively impact users, customers, or society. They should regularly evaluate the output of their AI models to assess potential bias or discriminatory patterns.
Generative AI models are not error-free and may produce unpredictable or unreliable results. There is a risk that generated content may need to be more accurate, consistent, or simply unusable.
Once generative AI technology is in use, organizations should establish robust monitoring processes to continuously evaluate the performance and reliability of the models. They can monitor AI-generated content for accuracy, consistency, and adherence to quality standards.
Dependence on external service providers:
Many organizations deploy generative AI systems via APIs or third-party services. This can create a dependency on external service providers whose availability, quality, and security are only sometimes guaranteed.
Organizations should look for service providers that adhere to industry standards and promote interoperability. When providers use standardized APIs, data formats, or protocols, it is easier to switch between providers or seamlessly integrate alternative solutions.
These risks have led some companies to decide against using generative AI. Concerns about legal issues, loss of control over content, ethical issues, and unreliable results are understandable and require careful consideration. But does this mean that companies must entirely forgo the benefits and opportunities that generative AI can offer? Not necessarily. When the right steps and proper precautions are taken, the benefits of generative AI can highly ouweight the risks.
When simpleshow developed an automated script writing feature using generative AI, the Story Generator
, careful consideration was given to mitigating risks and creating a safe and secure environment for business users to create professional explainer videos instantly.