What is AI governance? | Advantages & Challenges
Many organizations that have made the decision to deploy artificial intelligence have encountered many problems along the way. These include access to appropriate data, manual processes with risks, and methods and platforms that are not optimized for artificial intelligence. All of these challenges prevent organizations from using artificial intelligence to make transparent, accountable, and reliable decisions that comply with regulations and ethical standards. As a result, we need AI governance to solve the problems caused by these challenges. AI governance knowledge is designed to operationalize artificial intelligence, manage risk, and provide scalability while complying with the necessary regulations.
What is AI Governance?
If we want to explain AI governance more precisely, it should be said that this knowledge is a legal framework. With this knowledge, we can be sure that machine learning as a service technology is well placed in the direction of research and development. All these technologies aim to help humanity. AI Governance aims to bridge the gap between responsibility and ethics in technological development. Considering that today artificial intelligence has been implemented in various sectors such as the economy, transportation, business, education, and other cases, the issue of AI governance has become more important.
Key Principles of AI Governance
In the previous sections, we also said that the main goal of AI governance is to protect organizations that use artificial intelligence in software and technologies, and even towards their customers. AI governance does this by providing guidance and specific principles to promote the use of ethical artificial intelligence as a service in organizations. In the following, we will explain the important principles of Artificial Intelligence Governance:
Accountability
In this knowledge, it means holding organizations, employees, and experts accountable for their decisions and actions in the processes of designing, developing, using, or deploying artificial intelligence. If we want to give you an example in this case, we can mention the protection of skin color or gender discrimination in the creation of data sets and training algorithms.
Transparency
It does not matter whether the method of gathering information is artificial intelligence or human, because in either case there must be transparent procedures and decisions. When there is transparency, the importance of having an overview of the solutions becomes clear. For example, if the AI suggests wrong decisions, the transparency that exists can be used to find the key factors that led to the decision and identify the errors.
Fairness
There have been many discussions about the definition of fairness. But in any case, a single and universal definition of common values and common moral norms should be created and translated for artificial intelligence systems. This AI management principle ensures that potential biases in historical data and other information are reduced.
Safety
Artificial intelligence systems must incorporate the principles of privacy, security, satisfaction, and respect for people's rights and privacy. There are two important points about the main security of AI governance:
First, artificial intelligence systems should be scaled down to reduce effects that are unrelated to their primary goals. This refers to effects that are irreversible or difficult to reverse.
Second, in this matter, the question should be raised whether there is human monitoring of artificial intelligence systems in order to interrupt or stop the system if necessary.
Robustness
The artificial intelligence performance system is automatic, and the design and quality of its algorithms greatly affect its performance. If this is not done properly, it will have undesirable consequences. In fact, it can be said that when artificial intelligence is used in various scenarios, it should be expected that this system will work strongly.
What is the Importance of AI Governance?
There are always certain risks and limitations for artificial intelligence, and most of the time, in addition to being properly trained, artificial intelligence systems do not make correct decisions. As a result, it can be said that AI governance becomes important in these risks and limitations. AI governance provides a framework for monitoring AI risks and ensuring that AI is used ethically and responsibly. Knowledge of AI governance helps ensure transparency, fairness, and accountability in AI systems. It also respects human rights, guarantees privacy, and promotes trust. As a result, it can be said that AI governance is very necessary to prevent the intentional or unintentional exploitation of artificial intelligence and to prevent financial, credit or regulatory risks.
How to Measure AI Governance?
Remember, you can't manage what you don't measure. Improper measurement of artificial intelligence models exposes organizations to various risks. The first question to ask is: what measures matter? In order for an organization to answer this question, it must know what the definition of AI governance is and who in the organization is responsible and what their responsibilities are.
Typically, AI governance criteria are standardized for all organizations by government regulations and market forces. In addition, organizations should consider other measures to support their day-to-day operations. A number of criteria that organizations should consider include
Data: including lineage, origin, and measurement of data
Security: Understanding the manipulation or misuse of artificial intelligence environments
Cost or Value: Defining and measuring KPLs for the cost of data and the value created by data and algorithms
Bias: Need for KPLs that may have selection or measurement bias. Organizations should continuously monitor for bias through direct or derived data. It is also possible to create a set of KPLs that measure ethical information.
Accountability: Individual responsibility and use of the system and transparent decision making
Auditing: Ongoing data collection can be audited, allowing other parties or the software itself to perform ongoing audits.
Time: Consideration and measurement of time should be part of KPLs. This issue leads to a better understanding of the model over time.
The Advantages of AI Governance
AI Governance has many advantages that can be very beneficial to organizations using artificial intelligence. One of the key benefits of AI governance is the ability to remove the black box aspect around AI models. This ability allows stakeholders to understand how models work and make informed decisions about their use. AI Governance also helps scientists catalog their models and easily track which models are being used for specific tasks and how each model is performing. By leveraging this knowledge, organizations can improve their ability to find, understand, and trust the data they use to train their models, resulting in more accurate and reliable results. A strong governance framework can help organizations demonstrate their compliance and commitment to the responsible use of AI.
Challenges in AI Governance
Although AI governance ensures ethical artificial intelligence systems, there are still many challenges to this knowledge. Knowing the challenges of AI governance is important because it helps to achieve long-term benefits. These challenges are
Discrimination and bias
When artificial intelligence systems are trained on partial data, they are prone to bias and discrimination. Therefore, it is very important to pay attention to partial decision making and bias in artificial intelligence models to avoid discriminatory and unfair results.
Lack of accountability
Understanding artificial intelligence systems is not easy. Holding systems accountable for the outcomes of their decisions presents several challenges. As a result, artificial intelligence systems must adhere to the principle of transparency and accountability in order to properly understand how organizations use data to make decisions.
Limitations in resources and expertise
Developing and implementing AI governance effectively requires significant expertise and resources.
Changing technologies
Artificial intelligence is changing rapidly, creating challenges with evolving technologies and new risks.
The Role of AI governance tools in Ensuring Transparent Decision-Making
Explainability in AI is a critical aspect that ensures transparent decision-making, fostering trust and accountability in the deployment of artificial intelligence systems. The role of AI governance tools in achieving explainability is pivotal, as they provide mechanisms to unravel the complexities of AI models and algorithms, making their decision processes understandable to stakeholders.
Governance tools dedicated to explainability aim to address the "black box" nature of certain AI systems, where the decision-making processes are intricate and challenging for humans to interpret. By implementing these tools, organizations can open a window into the decision logic of AI models, enabling users to comprehend how and why specific conclusions are reached.
Explainability governance tools function in a variety of ways, including human-readable explanations, model-agnostic interpretability techniques, and feature significance analysis. By highlighting the critical qualities considered during the decision-making process, feature significance analysis aids in discovering the components influencing the model's judgment. Interpretability approaches that are not dependent on the model's underlying architecture, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), provide useful insights into the model's behavior.
Furthermore, these AI governance tools generate human-readable explanations that translate complex AI outputs into understandable terms. This is crucial for stakeholders, including non-technical decision-makers, end-users, and regulatory bodies, who need to grasp the reasoning behind AI-driven decisions.
Ensuring explainability in AI governance has various benefits. It enables in discovering and correcting model biases, aligning AI choices with ethical norms, and facilitating compliance with rules requiring openness in certain areas. Finally, it enables humans to make educated decisions based on AI results and promotes a responsible and accountable AI environment. In a society increasingly reliant on AI technology, governance tools' role in encouraging openness and explainability is critical for fostering trust and assuring ethical AI use.