Significance of AI Governance

The recent expansion of artificial intelligence (AI) has led to the developing of a new discipline of AI governance.

FREMONT, CA: Artificial Intelligence (AI) governance is an overarching framework that manages an organization's use of AI through a comprehensive collection of processes, methodologies, and tools.

The purpose of AI governance is not limited to ensuring the efficient application of AI. In reality, the scope encompasses risk management, regulatory compliance, and the ethical use of AI.

It is essential to differentiate between AI governance and AI regulation. AI regulation refers to the laws and rules created by a government or regulator regarding AI that apply to all organizations under their jurisdiction. AI governance is the management of AI within an organizational context.

Pros and cons of deep learning

Organizations have developed IT governance procedures. Why, then, do they require AI governance? AI governance and IT governance may share some practices, but AI governance is a distinct discipline, especially at this early stage of AI adoption and maturity.

In common usage, AI refers to deep learning or machine learning approaches that rely heavily on artificial neural networks. Deep learning is predicated on the notion that decision-making rules are derived from data rather than being hardcoded by humans, as with traditional IT systems. When deep learning is applied to narrowly defined tasks in fields such as language processing, image recognition, and speech recognition, substantial gains in accuracy and performance close to that of humans are observed.

Such AI-powered automated decision-making systems are becoming nearly ubiquitous. People's shopping suggestions, news feeds, job applications, credit decisions, and healthcare recommendations are all determined by algorithms. AI and the automation it enables have significant business benefits but also drawbacks. The "why" behind a deep learning decision is neither intuitive nor easily understood, unlike hardcoded rules. As a result, AI is referred to as a black box.

Other limitations exist besides the absence of transparency:

The real world is constantly evolving, and an AI system's learned patterns or associations may no longer be applicable.

Data from the real world frequently differs from the data used to train AI models.

AI models are effective for only a subset of audiences—not all. This phenomenon is known as AI bias or algorithmic bias.

In all of these scenarios, organizations continue to rely on automated decisions despite their algorithms likely being flawed.

The importance of AI governance

AI's strengths and limitations become increasingly apparent as adoption increases. Governments are introducing new regulations and guidelines to prevent AI unintentional and intentional misuse. AI misuse can lead to operational, financial, regulatory, and reputational risks for an organization. It is also unlikely to align with the core values of an organization. The unique characteristics of AI necessitate the establishment of safeguards to ensure that AI functions as intended. This is the primary objective of AI governance.

After a few years of implementing and scaling deep learning in enterprise settings, AI governance playbooks and best practices are beginning to emerge. Among the most notable examples are the following:

AI governance is not the exclusive responsibility of software engineers or machine learning specialists. It is multidisciplinary, with technical and non-technical stakeholders participating.

AI governance is important for public and private sector end users and AI software vendors. A handful of forward-thinking organizations are even integrating AI governance into their corporate governance and environmental, social, and governance strategies, as it entails how an organization should implement AI ethics principles and ensure its responsible use.