Artificial intelligence (AI) is permeating every facet of our lives, from employment and credit decisions to healthcare, housing, and even law enforcement. These high-stakes settings demand a closer examination of the integration of high-stakes AI into decision-making processes. It’s in this context that the concept of AI audits emerges—a critical practice for ensuring the responsible and ethical use of AI systems.

The Rise of AI in Decision-Making

In recent years, AI has made significant inroads into sectors that profoundly impact individuals and society as a whole. Algorithms now influence who gets hired, who gets a loan, who receives medical treatment, and even who comes into contact with law enforcement. While AI holds the promise of efficiency, accuracy, and automation, it also raises serious concerns about fairness, bias, and accountability, not only amongst end customers, but also amongst the executives that must finance AI programs

The Need for AI Audits

Recognizing the potential harms that poorly designed AI systems can cause—sometimes even matters of life and death—there is a growing consensus that crafting policies for the responsible use of AI must include rigorous oversight mechanisms. This is where AI audits come into play.

AI audits, however, are still in their early stages, leaving many questions unanswered. Who should conduct them? When should they be carried out? How should they be conducted? And perhaps the most fundamental question of all: should they even be done?

The Optimistic Perspective

Many experts and stakeholders in the AI field are optimistic about the prospects of AI audits. They see these audits as a critical tool for ensuring the technical accuracy and reliability of AI models. The hope is that well-defined best practices and standards for AI audits will emerge, creating a framework for accountability and transparency in AI decision-making.

Skepticism and the Concerns of Civil Rights Advocates

However, not everyone shares this optimism. Some civil rights advocates are deeply sceptical about both the concept and practical use of AI audits. Their concerns revolve around the idea of “audit-washing,” wherein bad actors manipulate audit requirements to appear compliant without conducting meaningful reviews.

This scepticism is grounded in the recognition that AI systems can perpetuate biases and discrimination if not carefully designed and monitored. The fear is that AI audits may become a token gesture, offering the appearance of accountability without addressing the underlying problems.

Best Practices for AI Audits

AI audits require collaboration, transparency, risk management, compliance, and standardization. Auditors should also consider the legal framework and regulations to measure damages stemming from AI-based decision-making

Let’s look at each

1. Collaboration: Effective AI audits require collaboration between internal teams and third-party auditors. Auditors should also collaborate and coordinate with other stakeholders and experts in AI and ML audits.
2. Transparency: It is recommended to keep a comprehensive record of data procurement, provenance, preprocessing, storage, and lineage. Auditors should also ensure transparent communication systems with all parties involved in the audit.
3. Risk Management: Auditors should assess the risks related to the rights and freedoms of data subjects. They should also describe the strengths and weaknesses of the AI and ML systems, as well as the gaps and issues that they identified.
4. Compliance: AI audits offer a way to assure compliance of AI-driven models with legal requirements and specified protocols. Auditors should consider data protection principles according to the Protection of Personal Information Act (PoPIA)
5. Standardization: AI auditing practices help standardize and professionalize this maturing field. AI auditing practices bring industry experts, entrepreneurs, and regulators together to define AI audit standards.

Common Weaknesses that Auditors may expose in your AI models

Companies that take a proactive approach to internally auditing their AI models will find it easier and more cost-effective to manage any external audit requirement. Some of the common weaknesses in AI models may include:

• Ethical issues: AI and ML systems can introduce ethical concerns, such as biased decision-making or the potential for discrimination. Auditors should assess the fairness and ethical implications of the system’s outputs.
• Data security threats: AI and ML systems can pose significant threats to the confidentiality, integrity, and availability of data and algorithms. Auditors should evaluate the system’s security measures and potential vulnerabilities.
• Lack of explainability: AI and ML-based decisions may not be easily explainable, making it challenging to understand the reasoning behind certain outcomes. Auditors should assess the system’s transparency and the availability of explanations for its decisions.
• Learning limitations: AI and ML systems are only as effective as the data used to train them and the various scenarios considered during training. Auditors should evaluate the system’s training data and consider potential limitations in its ability to adapt to new situations, along with any substantial differences between training and production datasets.

Address AI Audit Concerns with MLOps

 MLOps, or Machine Learning Operations helps ensure the accuracy and reliability of machine learning models by including data engineering, emphasizing reproducibility, ensuring robustness, and promoting continuous improvement.

MLOps, can help avoid common weaknesses of AI and ML systems in the following ways:

1. Automated monitoring: MLOps provides automated monitoring of the ML system, which can help detect data drift and other issues that can lead to inaccurate results.
2. Improved collaboration: MLOps promotes collaboration between data scientists and operations professionals, which can help ensure that the ML system is aligned with business needs and regulatory requirements.
3. Standardization: MLOps provides a standardized framework for developing and deploying ML models, which can help ensure consistency and reduce the risk of errors.
4. Improved security: MLOps can help improve the security of ML systems by providing tools and processes for identifying and mitigating vulnerabilities.
5. Increased transparency: MLOps can help increase the transparency of ML systems by providing tools for tracking the provenance of data and models, as well as for explaining the reasoning behind the system’s decisions.

In summary, MLOps can help avoid common weaknesses of AI and ML systems by providing automated monitoring, improving collaboration, standardizing processes, improving security, and increasing transparency. Robust documentation, in turn, assists both internal and external auditors to make informed decisions about the veracity of models.

Conclusion

As artificial intelligence continues to shape the landscape of decision-making across various sectors, the debate over AI audits intensifies. Who should conduct them, when they should occur, and how they should be carried out are all critical questions that need thoughtful answers. Ultimately, the question of “if” AI audits should be conducted is a fundamental one that will shape the future of AI governance.

In this evolving landscape, it is crucial to strike a balance between optimism and scepticism, acknowledging both the potential benefits and the genuine concerns associated with AI audits. Crafting policies and practices that ensure AI’s responsible and ethical use will require a nuanced and inclusive dialogue that considers the perspectives of all stakeholders.

References:

• Collaborative Intelligence: How Humans and AI are Transforming our World by Evan Selinger, Brenda Leong, Albert Fox Cahn (September 11, 2023)