Due Diligence: A Historical Perspective of Its Use in AI

The concept of due diligence has evolved dramatically over the years, particularly in the context of artificial intelligence. From its origins in financial practices to its modern application in evaluating AI technologies, the journey of how diligence is carried out reflects the changing priorities and complexities of technological advancement.

1940s-1950s: Foundational Practices

The term “due diligence” first gained prominence in the mid-20th century, primarily in legal and financial contexts. During this time, businesses conducted basic checks to assess the viability of investments. The focus was on verifying tangible assets and financial records. These early practices laid the groundwork for the rigorous assessment processes we see today. While AI was merely a theoretical concept in this era, the principles of careful evaluation were already being established.

1960s-1970s: The Rise of Computing

The advent of computing in the 1960s introduced new dimensions to diligence. As businesses began adopting computer systems, the scope of evaluations expanded to include technological infrastructure. The whole rigour now involved assessing the reliability of hardware, software, and data storage systems. During this period, the idea of automating decision-making processes through AI began to surface, but it remained largely experimental.

1980s: The Dawn of Commercial AI

By the 1980s, AI had moved beyond academic research into commercial applications. Expert systems, designed to mimic human decision-making, gained traction in industries like healthcare and finance. Diligence processes adapted to include assessments of these AI-driven tools. Evaluators began scrutinising the accuracy, reliability, and scalability of AI systems. This marked the first time AI-specific criteria were incorporated into diligence frameworks, highlighting a shift towards more specialised evaluations.

1990s: The Internet Boom

The 1990s brought the internet revolution, which transformed how businesses operated. For the first time, companies had access to vast amounts of digital data. In this era, diligence processes expanded to encompass cybersecurity and data integrity. AI technologies, such as recommendation engines and predictive analytics, started to play a critical role in e-commerce and digital marketing. Evaluating the ethical implications of AI began to emerge as a concern, though it was not yet a formalised part of the process.

2000s: Big Data and Machine Learning

The early 2000s ushered in the era of big data and machine learning. AI systems now relied heavily on vast datasets to improve accuracy and performance. Diligence frameworks evolved to address the challenges of data quality and governance. This period saw the introduction of tools for auditing data pipelines and evaluating bias in training datasets. The keyword “ethics” gained prominence as organisations recognised the societal impact of AI-driven decisions.

2010s: The AI Renaissance

The 2010s marked a renaissance in AI, driven by advancements in deep learning and neural networks. AI systems became capable of tasks such as image recognition, natural language processing, and autonomous driving. Due diligence processes during this period grew increasingly sophisticated. Evaluators began examining algorithmic transparency, explainability, and compliance with emerging regulations like GDPR. Companies faced greater scrutiny over the social and economic implications of their AI applications.

2020s: The Age of Accountability

Today, the importance of diligence duly carried out in AI is greater than ever. As AI systems are deployed in critical areas such as healthcare, finance, and law enforcement, the stakes have risen dramatically. Modern processes includes real-time monitoring of AI systems, assessments of their adaptability to new data, and thorough evaluations of their ethical impact. Businesses are also expected to address issues like fairness, bias mitigation, and environmental sustainability.

The journey of due diligence from a simple financial tool to a comprehensive framework for AI evaluation mirrors the rapid technological advancements of the past century. As we move forward, the focus on accountability and ethical responsibility will continue to shape how the process is conducted in the world of AI.