The Risks of AI usage in your business: Addressing Unreliable and Untraceable Results

Artificial intelligence is transforming how we share and analyze data, but this rapid adoption brings serious risks. Organizations and individuals that rely on AI tools to process sensitive information and provide responses may not realize the data sharing risk this entails or how this technology can produce unreliable or untraceable results. These issues raise concerns about privacy, accountability, and trust in AI-driven systems.

Understanding these risks is essential for anyone involved in data sharing or decision-making based on AI outputs.

How AI Affects Data Sharing

AI systems often depend on vast amounts of data to learn and make predictions. When sharing data with AI platforms, users expect accurate and reliable results. However, AI models can sometimes generate outputs that are inconsistent or difficult to verify.

One reason is that AI algorithms, especially those based on machine learning, can operate as "black boxes." They process data through complex layers, making it hard to trace how a specific result was reached. This lack of transparency complicates efforts to audit or validate AI decisions.

Another concern is data privacy. Sharing data with AI tools may expose sensitive information if proper safeguards are not in place. Such sharing may also require consent by the owner or individual. Data breaches or misuse can occur without strict procedures in place.

AI in the news

Several recent real-world cases highlight the risks of relying on AI without proper oversight:

• Most recent, and widely covered, was Deloitte issuing a partial refund to the Australian government after a report they generated leveraging AI contained several errors. These errors were from "hallucinations" which is false or misleading information created by AI which can seem plausible, but is not based on real data or events.

  
  

• Anthorpic settled a copyright lawsuit where the authors of a book claimed the company used their material to train the AI model without obtaining consent or obtaining the material legally. The book is considered copyrighted material, and since the AI model obtained the content from a pirated website, courts claimed Anthroptic did not properly compensate the owners of the material, which resulted in damages.

  
  

Why Traceability Matters in AI

Traceability means being able to track the origin and processing steps of data and AI outputs. It is crucial for:

• Accountability: Knowing how AI reached a decision helps assign responsibility if errors occur.

  
  

• Trust: Users are more likely to rely on AI when they understand its processes.

  
  

• Compliance: Regulations like GDPR require transparency in data handling and decision-making.

  
  

Without traceability, organizations risk making decisions based on flawed AI results or illegally obtained data, which can lead to financial losses, legal penalties, or damage to reputation.

Addressing the Risks: Best Practices

To reduce the risks of unreliable and untraceable AI results in data sharing, consider these strategies:

• Implement Explainable AI: Use AI models designed to provide clear explanations of their decisions. This helps users understand and verify outputs.

  
  

• Maintain Data Quality: Ensure training data is accurate, complete, and representative to minimize bias and errors.

  
  

• Secure Data Sharing: Use encryption and access controls to protect sensitive information during transfer and storage.

  
  

• Audit AI Systems Regularly: Conduct independent reviews to check for biases, errors, and compliance with standards.

  
  

• Document Data Lineage: Keep detailed records of data sources, transformations, and AI processing steps.

  
  

• Educate Users: Train staff and stakeholders on AI limitations and proper interpretation of results.

  
  

The Role of Regulation and Ethics

Governments and industry groups are developing guidelines to govern AI use in data sharing. These include requirements for transparency, fairness, and privacy protection.

Ethical AI use means prioritizing human rights and avoiding harm. Organizations should adopt ethical frameworks that guide AI development and deployment, ensuring that technology serves society responsibly.

Is your business ready to leap ahead with AI? Let Easy Audit help you ensure ethical and compliant practices as you adopt new technology.