Overview
AI governance is becoming an essential part of modern business strategy. While generative AI apps raise awareness and foster new use cases for AI, they also cause businesses to explore the variety of model types used across their ecosystem. Further, the foundation models we see today won’t look like the ones we see within the next few years. Effective governance ensures that AI systems and all model types that make them are not only effective but also align continuously with regulations, ethical standards, and societal values.
For some industries in particular – banking, insurance, healthcare – AI governance is becoming a competitive and risk management necessity. Robust model governance frameworks help organizations navigate a complex blend of business strategy, risk mitigation, and ethics, allowing them to harness the full potential of AI technologies.
The following stories will help you understand the role and purpose of AI governance, the factors driving its development, who is responsible for establishing a formal AI governance function, and the typical outcomes both the present and over the long-term.
Overview
AI governance is becoming an essential component in modern business strategy. In an era where AI technologies are rapidly evolving and becoming ever more integrated into everyday business processes, effective governance ensures that AI systems are not only innovative but also align with regulations, ethical standards, and societal values.
For some industries in particular – banking, insurance, healthcare – AI governance is becoming a competitive and risk management necessity. Robust governance frameworks help organizations navigate a complex blend of business strategy, risk mitigation, and ethics, allowing them to harness the full potential of AI technologies.
This page was prepared to help you understand the role and purpose of AI governance, the factors driving its development, who is responsible for establishing a formal AI governance function, and the typical outcomes both the present and over the long-term.
What is AI Governance?
AI governance refers to the framework of policies, guidelines and practices that determine and monitor how artificial intelligence (AI) is developed, deployed and controlled within an organization. It is an evolving practice, adapting to the rapid development of AI technologies and their changing applications. Key considerations for AI governance include:
Innovation
The performance and safety of AI innovation are enhanced when models are built and managed according to quality and ethical standards. Embedding clear requirements enables faster model development, approvals and deployments. The absence of standards and poor governance regimes can delay innovation or limit its value.
Risk
Businesses need to protect themselves and their customers from undesirable outcomes. Governance of quality and ethical standards helps businesses to understand and mitigate risk and safety concerns. Appreciation of risk and safety is often inconsistent throughout organizations, but governance can help to overcome this challenge.
Quality
Enforcing consistent model development and testing best practices delivers more robust applications that perform better in deployment. Governance helps businesses to define good and bad outcomes, set clear expectations, manage data quality and integrity, and safeguard successful AI systems.
Goals
Businesses of any size can struggle to maintain alignment between their corporate goals and strategy, the work done by various operational teams, affected users, and regulatory bodies. These goals can be protected through governance that drives more predictable project journeys.
Brand
Brand equity takes years to build but is quickly damaged by negative news and social media debate. Media and societal sensitivities about AI add prominence to negative stories. Standards and governance help businesses prevent negative events and improve their defense posture should a problem occur.
The Business Benefits of AI Governance
AI is proving to be revolutionary for business innovation. AI-powered automation of routine yet complex tasks reduces manual workloads and leads to efficiency, productivity and cost savings. New ways of analyzing and exploiting data resources are giving rise to more competitive products and services, and even new business models.
Business leaders and those responsible for building and managing AI systems face two major challenges as they seek to drive innovation:
Squandering Resources
Many businesses are squandering resources and undermining the intended outcomes of AI systems. A report in Harvard Business Review stated: “Most AI projects fail. Some estimates place the failure rate as high as 80 percent – almost double the rate of corporate IT project failures a decade ago.”
Strict Regulations
There is growing concern that expanding and increasingly strict regulations will limit the speed and add to the difficulty and cost of getting AI systems into production.
Turning this number on its head is reason alone to invest in a framework and controls that can achieve alignment, embedding best practices into the building of novel and complex systems. Adding to the business case is that these same controls also support auditing tasks and regulatory compliance. The complete business case for AI governance unites a program for achieving business and stakeholder objectives with controls for risk.
A true end-to-end governance process across the AI model lifecycle calls for collaboration between risk managers, model builders and business leaders. This requires common language, frameworks for effective partnerships, and continuous adaptability given that AI systems often support real-time business decisions. The good news is that, despite starting from different perspectives, all of these roles have overlapping interests and goals.
Establishing clear policies, ongoing governance processes, and workforce training on responsible AI use help build a culture of responsible AI practices, aligning AI strategies with business ethics and values. The outcomes include greater internal collaboration, better quality models, increased confidence in their outputs, deepening of trust with clients, and speed and cost efficiencies.
From GLM to LLM, you must think about governance of foundation models and generative AI products in the context of your entire modeling ecosystem. When buying a foundation model, it’s important to think about robust IT, security, data, and AI-specific model governance. This will help the business make sure that the foundational model deployment and performance meet its intended purpose. Additionally, building a process around inventorying generative AI use cases, as well model development best practices for fine-tuned modeling systems built on top of the foundation model are critical to a well-executed AI governance program.
AI Governance & Risk Management
Many leaders see AI as essential to their company’s future competitiveness and are willing to fund innovative projects. However, alongside headlines about AI’s benefits, stories warning of its dangers have raised public concern and prompted legislators and regulators to introduce specific rules for AI ethics and safety.
Those responsible for AI systems – risk managers, model builders, business leaders – can use AI governance to manage and mitigate these risks:
Bias & Discrimination
AI algorithms can inherit biases present in their training data, leading to discriminatory outcomes.
Ethical Concerns
Decisions made by AI can conflict with human ethics – a particular concern in sensitive areas like healthcare and financial services.
Transparency & Explainability
Some AI models, especially deep learning systems, can make it hard to understand how decisions are made.
Regulatory Compliance
The legal landscape governing the use of AI is evolving rapidly, both in the U.S. and internationally.
Implementation Uncertainties
Controls can increase costs and development time, and challenge implementation outcomes and ROI.
Change & Loss of Work
AI-enabled automation can change the nature of work and eliminate some categories of work.
Imagine you’re a manager in an insurance company and you asked a colleague to explain the reasoning to decline a policy. “I'm not telling you!” would not be an acceptable response, yet this is essential the response we’re given by many AI systems.
Without proper governance, deploying AI is like asking your employees not to reveal why, what and how they're making decisions that affect your business. AI governance provides a framework to prevent inscrutable AI, with controls for transparency, accountability, data privacy, robust security, and sustainable development and deployment.
Governance is the foundation of responsible AI (RAI), a set of principles that emphasize ethical and fair practices. Businesses can adopt RAI principles to ensure AI decisions minimize biases and align with corporate values, human ethics, workplace standards, and regulatory requirements.
As governments around the world introduce laws and guidelines for AI usage, businesses must ensure their AI systems comply with these regulations. Compliance is both a legal and ethical obligation; adhering to the regulations requires a proactive approach.
Businesses need to stay informed about the evolving regulatory landscape and adapt their AI governance frameworks accordingly. This involves regular assessments and updates to AI policies and practices, ensuring they meet the latest standards. Moreover, regulatory compliance in the field of AI is not just about following rules. It’s about embracing the spirit of these regulations, which is to promote the responsible use of AI. A commitment to RAI not only safeguards against legal risks and negative publicity but also enhances a business’s reputation and trustworthiness.
Debates about the need for AI regulation have intensified with the advance of the technology and its increasing integration into various sectors. However, until very recently, fines against the misuse of AI have been low in number and monetary value. The negative publicity from high profile AI failures has been invariably far more costly.
This is set to change. Dedicated AI regulations are coming into force and the penalties for non-compliance are much more severe. The insurance industry is the first in the U.S. to be targeted with AI-specific regulation, with the State of Colorado adopting a first-of-its-kind regulation affecting life insurers.
The EU AI Act is more expansive and although it does not directly affect U.S. businesses, its impact is expected to be global. Fines for breaches of the Act can be as much as €15M Euro or 3 percent of global annual turnover.
Flawed AI models are an intrinsic risk for any business, threatening high profile damage to brands and professional reputations, loss of customer confidence, and regulatory censure and fines.
The impact is not always so obvious. Glitches in AI systems can go unnoticed for weeks, months and longer, introducing hidden costs, messing with day-to-day operations, and causing delays and headaches. Flawed insights flowing from flawed models can send a company down the wrong path, affecting its ability to stay ahead in the market.
This makes AI governance a strategic imperative in a business world that is continually expanding its use of models in all facets of an enterprise. Maintaining a competitive edge and aligning with societal values and expectations are the factors that grab attention when thinking about a formalized governance function. However, most of the time and rather more prosaically, the value of controls and monitoring emerges from having efficient and accurate business systems.
Monitaur’s Role in AI Governance
Monitaur plays a pivotal role in facilitating effective AI governance. By offering tools and services that enable transparency and accountability in AI systems, Monitaur helps businesses navigate the complexities of AI governance.
Monitaur solutions are based on a three-stage “policy-to-proof” roadmap that charts a path from defining governance frameworks into actionable governance practices that can be rolled out at scale. It provides a system of record that enables the whole business to achieve key AI objectives in parallel to safeguarding risks.
Moreover, Monitaur's expertise in AI governance positions us as a valuable partner for businesses looking to implement responsible AI practices. Our approach combines technological innovation with a deep understanding of the ethical and regulatory aspects of AI.