Putting Generative AI to Work Responsibly
A modified version of this article was originally published on Spiceworks.
We can all agree that generative AI has completely transformed the way we approach content creation. However, the question remains: How can businesses harness the benefits of generative AI while safeguarding themselves from vulnerabilities?
In this article, Brad Fisher, CEO of Lumenova AI, shares insights on navigating the uncertain landscape of integrating generative AI into your risk management process.
Here are some key considerations to keep in mind:
1. Defining guardrails is essential
- Begin by formulating policies and procedures based on ethical and corporate principles. Identify all the areas that these policies should cover, such as data protection, intellectual property (IP) safeguarding, cybersecurity, and more.
- Define what is acceptable and unacceptable when it comes to using generative AI within your organization.
- Determine which types of information can be shared in prompts and identify what constitutes “sensitive data” that should not be submitted.
- Implement measures to protect your IP throughout the entire process and establish protocols for handling crises.
- If you plan to integrate generative AI into your business, consider whether you need data protection policies or data transfer impact assessments. Assess the confidentiality implications and, among others, determine the protocol for data deletion from the AI system.
2. Assign responsibility and educate your staff
- Consider appointing an executive dedicated to applying Responsible AI principles across your organization.
- Enable your employees to understand the risks associated with generative AI and familiarize them with the guardrails you have put in place to ensure responsible usage.
- Develop training programs or case studies to educate your staff, or explore existing resources that may serve this purpose.
- Foster a culture of transparency and accountability around generative AI.
- Encourage open dialogue among employees, where they can ask questions and share thoughts and opinions about the technology.
- Communicate your organization’s stance on the use of generative AI internally and externally.
3. Stay aware of regulatory requirements
- Ensure that your use of generative AI complies with relevant legal and regulatory frameworks, such as data privacy laws (e.g., GDPR, CCPA) and intellectual property regulations.
- Anticipate upcoming laws or regulations that might impact your company’s use of generative AI, such as the EU AI Act.
- Consider industry-specific regulations that may apply to your organization.
4. Monitor and evaluate
- Establish mechanisms to track AI-generated content and monitor risks on an ongoing basis.
- Define guidelines for regular review of outputs, bias detection, and system updates as necessary.
- Assess whether your organization has the necessary tools and processes to effectively manage the risks associated with generative AI, including code libraries, testing procedures, and quality control.
The Best Path Ahead
Renegade AI risk refers to the potential danger posed by AI systems operating independently from human control, which could cause unforeseen damage or societal disruption. To mitigate this risk, we must develop strategies for monitoring, controlling, and overseeing AI systems. These strategies should include safeguards to ensure human control and alignment with our values and goals.
In essence, organizations must ask themselves: Do we want to face congressional testimony about our renegade AI? Clearly, the risk is not worth it.
Organizations should take necessary steps to ensure responsible and compliant AI practices. This includes conducting regular audits, implementing reporting and monitoring systems, and providing training and guidance to those overseeing AI operations.
When navigating Responsible AI regulations, companies should seek external advice from industry experts and legal professionals. Staying up-to-date with the latest regulations and adapting procedures accordingly is crucial. Understanding the risks associated with new regulations and how they intersect with existing policies and processes will guide the organization in determining the best way forward.
Developing Predictive ML Models with Generative AI: The Responsible Way
Furthermore, using generative AI tools to build predictive models introduces an additional layer of risk and responsibility.
Given the potential impact of machine learning models on people’s lives, it becomes imperative to prioritize responsible and ethical development. Transparency, accountability, and reliability must be the foundation of model development.
Building responsible machine learning models requires transparency throughout the development process, ensuring that users understand the model’s inner workings and limitations.
Have you incorporated generative AI into your business? What challenges have you encountered?
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