As African businesses embark on their AI transformation initiatives, it is imperative to pay attention to the associated risks with AI as these risks can have negative cost and data privacy implications.
In this article, I will highlight and discuss in detail the risks and how you can mitigate them.
Notable Risks Associated With AI
- Data Privacy: This is a risk in AI where sensitive company or personal information is leaked into the training datasets which can be used by the AI model to make predictions and generate its outputs. If your dataset contains sensitive data, consider anonymizing them at the data transformation stage.
- Output Bias: This is a risk in AI where a particular group or type of people or entities are excluded or disenfranchised in a machine learning model’s predictions. This bias is usually introduced at the dataset extraction and transformation stages. For example, a company that uses AI to review CVs for jobs can create a bias that excludes certain people with ethnic or gender differences.
Biases can exclude certain customers during predictions and impact the experience of customers. Ensure your intent is fair to all.
- Misinformation: Generative or Machine Learning AI model produces wrong information either introduced during data preparation or by way of hallucination. A hallucination occurs when AI produces wrong or misleading results, which is not out of place in AI models today.
Ensure you implement the relevant guardrails to mitigate against this. Guardrails are a set of configurable rules and policies that help mitigate against adversarial inputs and attacks. There are tools for this or you can build your custom guardrail tool if you have the capacity internally. Test thoroughly during the AI alignment stage and also use AI monitoring tools to get visibility of your AI solution to proactively resolve issues as they occur.
- Unintended consequences: There is also a risk in AI where the model unintentionally makes decisions against a certain sample of people or entities even when there is no bias in your training datasets. Keep an eye out for this during monitoring.
- Prompt Injections or Jailbreaking: Once your solution is out there, you cannot control how people interact with it. People are unpredictable and can try to use your AI solution in ways that are out of your business context. This has cost implications as conversations with large language models are charged by the number of input and output tokens. It is important to prevent such misuse.
To save costs for your business, implement key guardrails to ensure your AI assistant blocks out-of-context questions. This can be done via advanced prompt engineering or other solutions.
- Shadow AI: Shadow AI is a concept in modern-day AI that speaks about the unauthorized use of AI tools by employees internally to speed up their tasks. Whilst Shadow AI comes with its benefits as staff supercharge their work by up to 10 times, the risks associated with it are also as important to note.
Sensitive company data can be leaked unintentionally by employees who use these tools which the major AI providers can use for their model retraining. The fallout here can result in model predictions to strangers using your company’s sensitive data.
Businesses need to set up and enforce strict policies and staff training on the responsible and ethical use of these tools in the course of their jobs.
Conclusion
- Supercharging your internal business processes or products using AI can be very exciting and rewarding to the business, employees, and their customers but the effects of these risks can negatively impact the transformation project and cost you even more.
- Implement a proper AI alignment process at the end of your AI transformation project to ensure your solution aligns with the set objectives.
- Implement extensive monitoring to get quicker insights and be proactive in addressing these risks and other issues.
