Companies could be making decisions based on AI and ML models that are inaccurate, unethical and untrustworthy.

INTRODUCTION AND MOTIVATION

In today's data-driven world, the adoption of artificial intelligence (AI) and machine learning (ML) models by companies has become increasingly prevalent. These models are leveraged to make critical business decisions, optimize processes, enhance customer experiences and predict future trends. The allure of AI and ML lies in their ability to transform vast amounts of data into actionable insights, thereby promising heightened efficiency and profitability. However, this growing reliance on AI and ML models also raises critical concerns, including their transparency, fairness and accountability.

AI and ML models are now integral components of many industries, including finance, healthcare, marketing and logistics, to name a few. Yet, their complex and often inscrutable nature has given rise to the need for independent validation and forensic analysis — especially in the cases of bankruptcy, mergers and acquisitions, private equity, turnaround, restructuring, partnership, vendor management and more. In other words, firms seeking to take advantage of AI and ML models, in particular from external and third parties, open the door to risk of unknown consequences with unproven AI assets, without proper vetting and validation of models' performance. Therefore, as these models shape our world and influence pivotal outcomes, the demand for transparency, fairness and accountability becomes paramount.

This paper delves into the growing field of AI and ML model forensics, with a specific focus on independently validating the performance and value of models used by companies. We will explore:

  • The necessity and challenges of model forensics and independent validation
  • The methodologies and frameworks that can be employed to ensure that AI and ML models used by companies are not only accurate but also ethical and reliable
  • How A&MPLIFY by Alvarez & Marsal can help you navigate the challenging and nuanced waters of AI and ML validation and evaluation so you can make the best decisions for your organization

THE NEED FOR MODEL FORENSIC AND INDEPENDENT VALIDATION

A forensic analysis is essential for organizations looking to expand their portfolio or operations with AI and ML models. From bankruptcy committees attempting to restructure assets and mergers and acquisitions with AI-driven firms, through partnerships and alliances with external parties to leverage their models, it is prudent for stakeholders to have the means to assess the accuracy, reliability and ethical compliance of the AI technology underpinning the target company's value. Forensic analysis provides crucial insights into the performance, potential risks and ethical implications of AI models, helping organizations make informed decisions and mitigate unforeseen liabilities.

Government Regulations

Government regulations play a pivotal role in the need for AI and ML model forensics. Regulators recognize the potential risks and ethical implications associated with these models, especially when they are used to make important decisions. Laws like the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) include provisions related to the transparency and accountability of automated decision-making processes. Such regulations require companies to provide explanations for AI- and ML-driven decisions, thereby emphasizing the importance of understanding and validating these models.

In mergers and acquisitions, regulatory bodies often scrutinize how AI and ML models are used for risk assessment, pricing and compliance. A robust model validation framework must be in place.

In the context of bankruptcy, mergers and acquisitions, and other corporate scenarios, regulatory compliance is of paramount importance. For instance, in mergers and acquisitions, regulatory bodies often scrutinize how AI and ML models are used for risk assessment, pricing and compliance, making it crucial for companies to have a robust model validation framework in place.

Ethical Concerns

Ethical concerns surrounding AI and ML models are multifaceted and extend to a wide range of corporate scenarios. Harmful or hidden bias, fairness and transparency are some of the core ethical concerns. Models trained on biased data can perpetuate inequalities and reinforce existing stereotypes, which is a concern in bankruptcy, mergers and acquisitions, and private equity transactions, where decisions can affect the livelihood of individuals and communities.

Ensuring the ethical use of AI and ML models in the context of turnaround, restructuring and partnerships is vital. These scenarios often involve challenging decisions, such as layoffs or resource allocation, where ethical considerations are central. Companies need to validate their models to ensure that decisions made using AI and ML technologies are just, hidden or harmful biases are handled appropriately and in line with ethical principles.

Independent forensic analysis and validation address ethical issues such as hidden or harmful bias, fairness and transparency.

Implications for Typical Due Diligence Processes

Due diligence is a standard practice in scenarios such as bankruptcy, mergers and acquisitions, private equity, turnaround, restructuring, partnership and vendor management. Due diligence includes a thorough assessment of a company's assets, liabilities, operations and risks. In this era of AI and ML, the process must also encompass an evaluation of the AI and ML models in use.

Potential acquirers, investors or partners need to examine the AI and ML models employed by the target company, including model documentation, performance metrics, data sources and validation processes. This due diligence ensures that the models align with the company's objectives and ethical standards. In bankruptcy and restructuring, due diligence may identify opportunities for optimization or potential risks related to AI and ML model-driven decisions.

Ensuring model transparency, fairness and accountability is essential to navigate these scenarios successfully and make informed, ethical and compliant decisions. In an environment where ethical considerations and public perception are crucial, companies that excel in transparency and accountability will have a competitive advantage. Businesses that can demonstrate the fairness and reliability of their AI and ML models will be more likely to attract investors, partners and customers.

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Originally Published 31 January 2024

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.