Putting Generative AI to Work Responsibly
This article was originally published on Spiceworks.
We all agree that generative AI has revolutionized our relationship with content creation, but the question remains. Brad Fisher, CEO, Lumenova AI, shares how businesses can harness all its benefits without leaving themselves open to vulnerabilities.
Here are some things that might be helpful to have in mind while navigating the still-uncertain landscape of integrating Generative AI into your risk management process:
1. Defining guardrails is essential
- Start by creating policies and procedures based on ethical and corporate principles, and think about all the areas that might need to be covered by these policies – such as data safeguarding, IP protection, cybersecurity, and others.
- Define what your organization should and shouldn’t be doing with generative AI.
- Determine what type of information can be shared in the prompts and what constitutes the ‘sensitive data’ that can’t be submitted.
- Establish what measures should be taken to protect your IP throughout the entire process and what should happen in case of a crisis.
- If you plan to incorporate generative AI in the fabric of your business, consider whether there are data protection policies or data transfer impact assessments to cover first. What are the confidentiality implications for this information? What will happen if you will need to delete data from the AI? How will it work?
2. Assign ownership and educate your employees
- Consider appointing an executive whose full-time duty will be to apply Responsible AI principles throughout the organization.
- Ask yourself how you could enable your employees to gain a real-time understanding of the risks associated with generative AI and the guardrails you have put in place to ensure the responsible use of this technology.
- Are there any training programs or case studies you can provide? Or do these need to be created or researched first?
- How will you create a culture of transparency and accountability around generative AI?
- How can you foster an open dialogue about generative AI and encourage employees to ask questions and share their thoughts and opinions about the technology?
- How will you communicate your stance on the use of generative AI both internally and externally?
3. Keep the regulatory landscape in mind
- Ensure that the use of generative AI complies with any relevant legal and regulatory frameworks, such as data privacy laws (ex., GDPR, CCPA), intellectual property laws, and other ethical or regulatory frameworks.
- Ask yourself if any upcoming laws or regulations might apply to your company’s use of generative AI. For example, the EU plans to regulate generative AI in its upcoming EU AI Act.
- Keep in mind industry-specific regulations. Some industries have specific regulations that govern the use of AI. Do these apply to you?
4. Monitor and evaluate
- Consider how your organization will keep track of AI-generated content and how risk will be monitored on an ongoing basis.
- Establish clear guidelines for regularly reviewing outputs, monitoring for bias, and updating the system as needed.
- Ask yourself if you have the tools and processes required to effectively manage the risks associated with generative AI. This might include code libraries, testing, or other QC procedures.
The Best Path Forward
Renegade AI risk is a term used to refer to the potential risk posed by artificial intelligence (AI) systems that have been designed to operate independently from human control or influence. Such AI systems could cause unforeseen damage or disrupt our society if they act in unpredictable or dangerous ways. This type of risk is becoming increasingly relevant as AI systems become increasingly sophisticated and autonomous. To mitigate this risk, we must develop effective strategies for monitoring, controlling, and overseeing AI systems. Such strategies should incorporate safeguards to ensure that AI systems remain under human control and that their behavior always aligns with our values and goals.
In short, here are the questions organizations should ask themselves: Would you want to end up testifying to Congress about your renegade AI? Neither would I. Not worth the risk.
Organizations should take the necessary steps to ensure their AI is responsible and compliant with applicable laws and regulations. This can include conducting regular audits and reviews, establishing reporting and monitoring systems, and providing adequate training and guidance for those overseeing AI operations.
The best approach for companies to take when navigating the various Responsible AI regulations is to seek external advice from industry experts and legal counsel. Companies should also stay current on the latest Responsible AI regulations and review any changes or updates to the regulations. Companies need to understand the risks associated with each new regulation and the implications for their business. Additionally, companies should consider how the regulations intersect with their existing policies and processes and adapt their existing procedures to align with the new regulations. Ultimately, companies should use the guidance of experts to determine the best path forward for their organization regarding Responsible AI regulations.
Building Predictive ML Models with Generative AI: The Responsible Way
On top of everything mentioned above, an extra layer of risk and responsibility comes with using generative AI tools to develop predictive models.
As machine learning models can have a significant impact on people’s lives, this is a matter that needs to be addressed as a priority. ML models should always be developed responsibly and ethically.
Models must be developed with transparency and accountability, tested for reliability, and used responsibly. The ethical considerations of using AI must be given the same importance as the technical aspects of model development.
Ultimately, the key to building responsible machine learning models is to ensure that the development process is transparent and that users understand how the model works and its limitations.
Have you integrated generative AI into your businesses? What challenges have you faced?
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