The Untold Importance of AI Model Auditability and Traceability
In today’s age, artificial intelligence (AI) has become a significant aspect of our everyday lives. From virtual assistants to self-driving cars, AI has revolutionized how we interact with technology. As AI continues to improve and reshape industries, it inevitably raises concerns about its accuracy, fairness, and accountability. AI models must be auditable and traceable, ensuring transparency and accountability for all stakeholders.
What is AI model auditability and traceability?
In simple terms, AI model auditability and traceability refer to the ability to understand how an AI model or algorithm is making decisions and to trace its actions and outputs. It essentially answers the question of how and why AI models make specific decisions. For instance, if an AI model is making recommendations on which credit score to approve, an auditor should be able to understand how the algorithm came up with the decision.
AI models must be transparent, accountable, and fair. Without auditability and traceability, AI models can reproduce biased decisions that are discriminatory against certain groups of people. Such unfairness can impart significant harm and affect people’s lives negatively.
Auditing AI models is necessary
AI model auditing involves examining the inputs, validation processes, and outputs of AI models. It is necessary to examine AI models from start to finish to understand how they operate. Auditors test an AI model to ensure that it meets particular standards before deployment.
Auditability and traceability are crucial when introducing AI models within specific contexts. For example, AI models used in the healthcare sectors should be accurate, secure, and meet compliance requirements to protect patient data. Similarly, an AI model developed for fraud detection should be auditable, to prevent any revenue loss through fraudulent activities.
One real-world example of the importance of AI model auditability involved the COMPAS Algorith, a machine learning tool used in the American criminal justice system. The tool used algorithms to predict a defendant’s likelihood of reoffending, facilitating decisions related to bail, sentencing, and parole. Unfortunately, studies have suggested that the algorithm may have been racially biased, erroneously assigning a high-risk score to darker-skinned individuals compared to light-skinned individuals. The algorithm’s lack of traceability and auditability meant that it was tough to understand how it came to its decisions. Without proper auditing, such AI models can propagate discriminatory outcomes hiding under the guise of objective and neutral decision-making.
Tracing AI models is equally critical
AI model traceability refers to the ability to identify inputs, processes, outputs, and outcomes from an AI decision-making system. It ensures that the model is answerable for each of its decisions and transactions. Traceability is critical in assuring AI models’ integrity and overall effectiveness.
Tracing AI models is necessary to ensure that they work within the desired parameters. When specific standards are in place, it’s easy to identify when AI models are behaving strangely. For instance, an AI model developed to detect 98% of fraud must accurately identify any fraudulent activity. In case the system fails, it is essential to trace the immediate issue and address it.
The need for AI model traceability is clearly illustrated in the healthcare sector. Tracing allows healthcare providers to track patients’ health records and make well-informed decisions about their care. AI models must ensure that patients’ data is secure, anonymous, and meet established standards to protect patient’s information.
As AI models continue to become more sophisticated, tracing their decision-making process and performance becomes paramount. It ensures that they work as intended and that any issues are identified and addressed, minimizing the probability of harm.
Conclusion
Incorporating AI models in our daily lives may be convenient and useful, but it is not without challenges. AI model auditability and traceability play a major role in making AI models work transparently, accountably, and fairly. The ability to review an AI model decision making process is essential, especially in high-risk sectors such as healthcare and finance.
Auditing and tracing AI models are crucial steps in ensuring that they conform to established industry standards and regulations. AI models must comply with specific ethics and regulations to ensure that they are transparent, accurate, and secure. AI model auditability and traceability should never be overlooked, and businesses should invest appropriately in them.
Our future is intertwined with AI models. It’s upon us to make sure that they operate transparently, accountably, and fairly. Only then can we enjoy the benefits of AI models without compromising our trust in these systems.