Author/contributor
Armando Guio Espanol
Associate
Generative AI has evolved since it first made headlines. It expanded its capabilities beyond text and image generation and now includes audio, video, and multimodal outputs. Recent advancements, such as the release of GPT-4o and the introduction of Gemini, Google’s largest and most capable artificial intelligence (AI) model, demonstrate the growing integration of generative AI into enterprise tools and workflows, including in fields like law, healthcare and education.
The increasing popularity of generative AI systems has ignited a debate over their commercial use. Some enthusiastically advocate for widespread adoption across industries, while others raise alarms about the technology’s current limitations. Neither position seems to be reasonable. A more nuanced approach recognizes the potential benefits of generative AI when deployed responsibly and with a clear understanding of its risks and limitations.
The Growing Impact of Generative AI
If we want to understand the potential of generative AI fully, it’s important to examine its impact across industries. Let’s look at a couple of examples:
- Healthcare: Generative AI is becoming more prevalent in healthcare, e.g., by analyzing medical scans to detect diseases like cancer earlier, designing personalized treatment plans based on individual patient data, and even creating realistic simulations for surgical training. For example, DeepMind’s AlphaFold has revolutionized protein folding prediction, a critical step in drug discovery and disease research, potentially accelerating the development of new treatments.
- Education: In the education sector, AI is used to develop personalized learning plans, create interactive educational content, and even assist in grading and assessment.
- Law: Generative AI is also making waves in the legal field by automating tasks like legal research, document drafting and analysis.
The far-reaching applications of generative AI in fields like healthcare, education, and law are undoubtedly promising. However, to harness this technology’s full potential, we must balance its transformative power with careful consideration of the ethical implications and risks it presents.
Considerations for Responsible Generative AI Adoption
A key challenge for generative AI is overcoming biased training data to ensure fairness and equity in AI development. Biased AI can lead to unfair or discriminatory results, potentially causing harm and reinforcing existing inequalities. To ensure fairness, companies must actively work to reduce these biases by using diverse data, thorough testing, and ongoing monitoring of AI-generated content.
The rise of AI is also challenging traditional notions of creativity and ownership. As AI systems increasingly generate content that mimics human ingenuity, legal frameworks are struggling to adapt. A central question is whether AI-generated content should be eligible for copyright protection, a debate with significant implications for creative industries. This ongoing discourse could reshape the very definition of authorship and ownership in the digital age, raising complex issues that demand thoughtful consideration.
For now, businesses should consider these points before deciding how to incorporate this technology:
1. Understand How the Technology Works
Generative AI stands out from other AI systems, especially machine learning, because of its exclusive features, advanced training methods, and distinct data processing. Understanding these new elements reveals how generative AI achieves its results, showcasing its true capabilities and limitations. It’s important to remember that generative AI, while powerful, is still a machine under development with inherent constraints.
2. Read the Small Print
It’s important to read the terms and conditions of these services before accessing them. The terms and conditions will include valuable information about the following:
- liability issues set by developers of this technology;
- obligations assumed by the user(s); and
- limitations that may exist regarding the use of images and texts generated with these systems.
This becomes especially important when defining issues such as intellectual property protection and the risks of infringement that may exist by using the texts and images these systems generate. It will also allow you to have elements to decide those cases in which it is necessary to disclose if a text or image has been developed using this technology.
3. Understand the Privacy Concerns
Businesses must be cautious about the information they input into generative AI systems. Using sensitive company information could lead to privacy breaches or unintended data exposure. Establishing clear policies on what data can be used with these tools is vital for maintaining privacy and security.
A safe approach is to make sure you are not inputting personally identifiable information, client details, or sensitive case information into any of these tools. LLM tools may store and use data you input, which means sensitive or personal data could be retained and potentially accessed by unauthorized parties. Moreover, the free versions of these tools may not have the same level of security, increasing the risk of data breaches, etc.
4. Know That Autonomy by Default Does Not Exist
The core idea is that AI systems, even those capable of generating complex outputs, are essentially tools created and controlled by humans. It doesn’t make decisions outside of the instructions or algorithms it’s been programmed with. Any “autonomy” it appears to have is an illusion of complex programming. It can augment human work but shouldn’t be relied upon as a sole decision-maker.
The use of AI raises important questions about responsibility and accountability in the workplace. If an AI makes an error or causes harm, who is ultimately responsible? Setting clear work policies for the adoption of this technology is relevant at this stage.
5. Ethical AI Practices
As AI becomes more integrated into various aspects of life, ethical considerations become increasingly important. Companies should be aware of and adhere to these considerations to ensure responsible AI use. A good starting point is the OECD AI Principles. This guide promotes the use of AI that is innovative and trustworthy but also respects human rights and democratic values. By prioritizing accuracy, privacy, fairness, human oversight, and transparency, businesses can harness the power of generative AI while mitigating risks and maintaining trust.
6. Train Your Team
Ensure your team receives comprehensive training on the specific generative AI systems they’ll be using, emphasizing responsible and effective usage for everyone. They need to understand not only how to use the systems but also the type of information required and its subsequent handling. Collaborate as a team to explore the potential benefits and applications of generative AI, maximizing its positive impact on your workflow.
7. Find The Right Tool
Given how fast AI technologies are developing, there are plenty of options in the market. Before deciding on a specific tool to integrate with your project, make sure you shop around. The tool you choose must be the best option not only functionally (ease of use, scalability, cost), but also security-wise. Consider the terms of use since they differ from product to product, and the way your information is handled will vary. Choose the tool that not only has the best functionalities but also offers the best guarantees for your protection.
Final Thoughts
The rise of generative AI has opened up new possibilities for businesses across various industries, from healthcare and education to law. However, the responsible adoption of this technology requires careful consideration and a thoughtful approach.
The full impact of AI remains to be seen, but the law will undoubtedly play a crucial role in guiding its ethical and responsible development and use. Several key areas already require legal attention to ensure this transformative technology benefits society while minimizing potential risks.
While powerful, generative AI is still a tool that requires human oversight and decision-making. Businesses should avoid over-relying on these systems’ outputs and maintain a clear understanding of the autonomy and decision-making authority granted to the technology.
