In an era where AI systems are increasingly responsible for life-critical decisions—from diagnosing illnesses to driving autonomous vehicles—the code quality behind these systems has never been more crucial. Yet, the hidden peril of technical debt in AI frameworks remains largely overlooked, posing significant risks to businesses and society. This article uncovers the dangers of poor code quality in AI and provides a roadmap for mitigating these risks.
The Overlooked Peril of Poor Code Quality in AI Frameworks
Despite extensive literature on AI’s ethical risks, little attention has been paid to the quality of the code underpinning these AI tools. While ethical concerns in AI have rightly garnered significant focus, an equally pressing issue is the potentially hazardous implications of poor code quality in AI implementations.
This study examines the critical importance of code quality within AI frameworks and explores the significant risks posed by neglecting it. We assessed 20 of the most widely used open-source AI frameworks, calculated their technical debt, and compared the health and performance of their development processes. Due to space constraints, this article will be presented in three parts over three consecutive weeks.
The first part outlines the goals of the analysis, describes the experiment, and summarizes our findings.
The second part delves into the methods used to calculate technical debt and assess the development history. The
final part integrates the results, based on the hypothesis that a single entity—such as a private equity firm—develops all these AI frameworks, seeking to mitigate technical debt risks while optimizing development costs without compromising quality and setting on the risk the end user.
The Ethical Dilemma
Ethical AI use is crucial, as biased algorithms can lead to unfair decisions, and a lack of transparency can erode trust. However, a 100% ethical AI system can still be dangerous if the underlying code is flawed. A poorly implemented AI system can introduce bugs that compromise functionality, security, and reliability, posing significant risks, especially in critical applications like healthcare, autonomous driving, and financial services.
The Concept of Technical Debt
Technical debt is like dark matter in technology—unseen but with significant impacts on business operations. It refers to the future costs incurred by choosing an easy, limited solution now instead of a better approach that would take longer. This debt can manifest as increased maintenance costs, slower time to market, and higher risks of system failures.
The Sacrifice of Code Quality
The unethical sacrifice of code quality during AI integration and expansion not only sets businesses at risk but also carries significant social risks. For example, a recent outage caused by a buggy update in CrowdStrike’s Falcon platform highlights the broader implications of neglecting code quality:
- Business Risks: Poor code quality can lead to system failures, increased maintenance costs, and slower time to market. These factors collectively undermine a company’s competitive edge and operational efficiency.
- Social Risks: Beyond the business implications, poor code quality in AI systems can have dire social consequences. An AI-driven healthcare application with bugs could lead to incorrect diagnoses or treatment recommendations, endangering patient lives. In autonomous driving, flawed AI systems could result in accidents and fatalities.
The Risk of Sacrificing Code Quality in AI Systems
AI is applied in several critical sectors where system malfunctions could have catastrophic consequences. For instance:
- Healthcare: A malfunction in diagnostic AI could lead to incorrect treatment plans, while errors in AI-driven surgical robots could cause severe harm or death.
- Aviation: AI optimizes air traffic control and autopilot systems. Failures could result in mid-air collisions or crashes during critical flight stages.
- Nuclear Energy: AI controls reactor management and security systems. A malfunction here could cause a meltdown or undetected breaches, leading to catastrophic outcomes.
- Autonomous Vehicles: These rely on AI for real-time decision-making. System failures could result in accidents causing injuries or fatalities.
- Financial Sector: AI-driven trading systems and fraud detection tools are critical. Malfunctions could disrupt markets or allow large-scale fraud to go undetected.
- Defense and Military: Failures in AI systems, such as autonomous weapons or cybersecurity systems, could lead to unintended military actions or successful cyberattacks on critical infrastructure.
- Financial Sector: AI-driven trading systems and fraud detection tools are critical. Malfunctions could disrupt markets or allow large-scale fraud to go undetected.
These examples highlight the critical need for robust, reliable AI systems. A single bug in any of these areas could lead to significant business risks, social disruption, or environmental disasters.
Linking Technical Debt to Business Growth
A study by
McKinsey Digital highlights the substantial impact of technical debt on business performance. Companies with higher Technical Debt Scores (TDS) experience slower revenue growth compared to their peers with lower TDS. Specifically, firms in the top 20% for TDS have a 20% higher revenue growth rate compared to those in the bottom 20%. This correlation underscores how unmanaged technical debt can stymie business growth by diverting resources from innovation to maintenance and issue resolution.
Our analysis aimed to:
The Goals of our analysis
- Analyze the quality of widely used AI frameworks using static code analysis and classify the associated technical debt.
- Correlate development costs with business risks and social risks with technical debt classification.
- Define a methodology that measures the technical debt and provides all information required for risk mitigation concerning code refactoring costs.
The methodology
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- Assess Code Quality: We assessed the code quality of modern AI frameworks by applying a systematic technical debt calculation and classification.
- Define a Benchmark: We created a Code Quality Benchmark by averaging the findings across the sample, excluding quality outliers
3.Define a Benchmark: We created a Code Quality Benchmark by averaging the findings across the sample, excluding quality outliers
- Compare Costs: We compared the cost of technical debt with the overall cost of developing each library from scratch and classified the debt.
- Analyze Commits History: We analyzed the commit history of each selected open-source AI framework.
- Development Cost Analysis: We calculated the cost of new feature development versus bug fixes over time.
- Correlation: We correlated the technical debt classification with average development costs.
The Risk Classification
We have developed a straightforward classification table, heavily inspired by the
SQALE method, but with greater granularity. The majority of real-world products fall within the quality range of B to D. Codebases classified as E (or, even worse, Z) are particularly challenging to maintain
The Experiment and Results
We selected 20 of the most widely used open-source AI frameworks and assessed them against our Technical Debt Standard.
Below are the key findings:
- High-Quality Frameworks: Tensor Flow, PyTorch, and OpenCV exhibited excellent code quality with technical debt ratios below 5%.
- Risky Frameworks: FASTAI’s technical debt exceeded 90%, signaling significant risk if not addressed.
- Business Impact: Frameworks with lower technical debt are associated with faster innovation and lower long-term costs.
These insights underscore the importance of prioritizing code quality in AI development. The detailed results of our analysis can be reviewed in our test staging.
Conclusion
As AI continues to integrate into various aspects of business and society, the focus must expand beyond ethical considerations to include the quality of the underlying code. Ensuring robust, well-tested code is essential to avoid the multifaceted risks associated with poor code quality. By addressing this often-overlooked aspect, we can harness the full potential of AI responsibly and sustainably.
It’s time to prioritize quality in AI, not just for ethical reasons, but to ensure sustainable growth and innovation. Let’s commit to building AI systems that are as reliable as they are revolutionary.