Risk Management in Generative AI

Generative artificial intelligence (generative AI, GenAI or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. Any new technology comes with its own set of risks that must be managed. Here are some key risks to consider:

Generative AI has the ability to tailor its output based on the desired tone, voice, or style. However, this flexibility can also be a double-edged sword. For instance, if a company specializing in toddler toys uses a chatbot with a playful tone in its communications but the chatbot starts delivering overly serious and descriptive responses, it can undermine the brand identity

Early adopters of generative AI, such as ChatGPT , have faced challenges with unfiltered data feeds. When these systems are connected to the web and continuously learning from new data, there's a risk of incorporating incorrect or malicious information. Companies need to ensure that their AI models don't learn from prejudiced, unethical, or incorrect data. 

Generative AI can produce responses that sound accurate but are actually misleading. This phenomenon, known as AI hallucination, coupled with inherent biases in the training data, can lead to problematic outputs. For instance, an AI-generated video showcasing the evolution of visual expression might inadvertently exclude significant cultural contributions, leading to historically incomplete narratives.

Some users may attempt to circumvent existing safeguards within generative AI tools to obtain prohibited or dangerous content. For example, by manipulating the AI into playing a specific role and feeding it creative conditions, it might be tricked into providing illegal or harmful information.

Organizations must stay vigilant and continuously update their security measures to prevent such exploits. Let’s consider few of the ways to mitigate these risks,

Mitigating risks associated with generative AI involves fostering a culture of awareness and education. Employees should be educated about the potential for misrepresentation and misuse of AI-generated content. For instance, deepfakes can misrepresent a company or its employees in compromising ways. Educating employees about the importance of data privacy and responsible sharing can help mitigate these risks.

Collaborations between public and private sectors can bolster the efforts to manage AI risks. Initiatives like StopNCII.org, which helps victims of non-consensual intimate image abuse, exemplify the impact of such partnerships. Organizations should also implement policies that mandate audits by internal or external experts to ensure compliance and mitigate bias risks

Governments play a central role in creating strong policies to govern AI systems and address privacy or security threats. Mandating watermarks, signatures or back-end imprints on AI-generated content, like international and national laws for deepfakes, can help identify AI-created content during audits. It is also important for professionals and institutions to contribute to transparency initiatives like the Foundation Model Transparency Index

Transforming generative AI’s weaknesses into strengths can help in mitigating risks. Tools like iA Writer can help track words generated by AI, while DupliChecker can check for plagiarism. Retrieval augmented generation (RAG) models can limit AI hallucinations by using proprietary data. Additionally, AI can develop tools to detect AI-generated content with high accuracy, helping organizations improve their risk mitigation strategies.

Ref: Upadhyay, M. A. (2024). Generative AI for Marketing (Paperback, p. 178).

Are you building an AI Agent? Submit it here