Introduction
There are standards and principles for how things are supposed to be done. When making coffee, for instance, you can add cinnamon or do it my way and add some cloves (I did it once, being adventurous or something of the sort). Some guiding principles already exist for AI and are applied when building AI systems.
The principles of AI governance, which range from ethics to explainability, privacy, inclusivity, and safety, help propel the development of AI systems in ways that ensure they are fair, beneficial, and non-harmful to users. Let’s explore these principles today. Since we already mentioned coffee, why not make a cup while at it?
1. AI Ethics
When it comes to the workings of Artificial Intelligence or, sometimes, machine learning models, the emphasis on ethics has been high. Ethics in AI focuses on ensuring that the said systems operate in an ethical and just manner. This entails ensuring fairness, accountability, and transparency. Fairness entails avoiding biases that could lead to discrimination against any group or individual. To achieve ethical standing in AI systems, the data used to train models must be representative and diverse enough to reflect all groups affected by the decisions made by the AI system.
Accountability in AI ethics is a mechanism to hold the operators and developers accountable for how the systems operate. This comes in through proper documentation of decision-making processes and the expected outcomes. This also touches on the transparency aspect, which ensures that stakeholders understand how the AI systems' decisions are reached. Essentially, AI developers should detail the capabilities and limitations of their systems, as well as the logic used to make decisions.
In the coffee world, AI ethics is like selecting the source of coffee beans. You would want to ensure that the beans are not sourced from exploited workers. Similarly, AI ethics ensures that the technology does not cause harm or discrimination to anyone.
2. Privacy and Data protection
Safeguarding data is crucial. Today, even a minor data leak could result in catastrophic harm to an individual. We have many interconnected services that use the same data; protecting this is important. In an AI system, ensuring data protection is paramount. This is especially essential when handling sensitive or personal information. In an era where more AI systems are coming on our mobile devices, users want to ensure that the data does not end up with the wrong people. Many developers are using approaches such as AI on devices; Google mentioned this about Gemini to ensure private data is not exposed. Any personal data collected should be stored and used responsibly.
Data minimization is the practice of using only the data necessary for a particular purpose. Another approach is to make it optional for users to decide how and what they want to share with the system. As the data owner, you should have control over your data. Another way to enhance privacy and data protection is through public awareness campaigns that inform users and the public about the benefits, risks, and AI's capabilities. Awareness ensures users can make the right decisions when using AI systems.
Coffee sip: Do you add any secret ingredients to your coffee? Not cloves.
3. Robustness and safety
Almost everything is prone to breaking down, having faults, and making errors. This is also the case with AI systems. Robustness and safety in AI systems focus on ensuring the system can operate safely under various conditions. This mainly entails error handling, in which the system should be designed to detect and mitigate errors and anomalies without causing harm to the user. A philosophical question: should AI systems respond to questions indicating the user wants to harm themselves or someone else? Ethics and freedom of information!
AI systems should have safety protocols that prevent/mitigate adverse outcomes associated with system malfunctions. The systems should be safe for all users. This is akin to ensuring your coffee makers do not break or scale someone mistakenly. AI systems need to be reliable under different conditions of use.
4. Inclusivity and Diversity
We mentioned this when discussing ethics. The idea of inclusivity and diversity is to ensure that AI systems account for diverse perspectives and help prevent bias while promoting fairness. This can be achieved by having diverse teams to help make decisions and develop and deploy AI systems. This includes a range of perspectives that help reduce the risk of bias. Further, ensuring stakeholder engagement with groups from marginalized or underrepresented backgrounds in the development of AI systems ensures that their needs and viewpoints are included in the process. Essentially, inclusivity in AI entails decision systems that respect diverse user needs and perspectives.
In coffee terms, if you are serving guests, make coffee that caters to their different dietary needs.
5. Societal and Environmental Well-being
Somehow, this principle is related to inclusivity and ethics. When designing AI systems, ensure that their impacts on society and averments are essential and positive or neutral. Sustainable AI approaches focus on AI solutions that are environmentally sustainable and contribute positively to ecological well-being. Their impacts on society, i.e., on employment, human behavior, and societal structures, should be positive and promote progress.
The core purpose of AI is to benefit people regardless of the purpose for which it is used. In coffee terms, it’s about choosing biodegradable pods, supporting sustainable farming, and generally choosing coffee that's good for the environment.
6. Regulation and policy compliance
Regulations! The idea behind AI governance is essential for AI developers to build systems that meet the regulations and policy requirements. Compliance with international and local laws is crucial when building AI solutions. This involves adherence to standards that govern the development of AI systems and the use of technology. It’s also about following the legal guidelines for building and using AI solutions. Legal compliance ensures that AI practices adhere to the legal requirements for data protection, consumer rights, and fairness. If you get your coffee from a coffee shop, this means the shop is adhering to health and safety standards when preparing your coffee.
7. Explainability
We mentioned transparency and explainability, which are closely linked. However, transparency also focuses on the ability to explain how AI systems reach decisions. The workings of the AI and how it makes decisions should be interpretable. It should be easy for humans to understand, which is important when validating and trusting AI outputs. Explainability also involves comprehensibility. The explanations should be easy to understand for all levels of statehooders and not just experts. Simple language, interactive demonstrations, and visual aids can be used to explain how the system works. We all should understand how the buttons on our coffee maker work and how they affect the product.
8. Alignment
Humans have diverse values and goals. Alignment in AI means designing the system so it aligns with human values and achieves its intended goal. AI systems should reflect the ethical values and principles of the societies in which it operates. This entails the use of extensive stakeholder engagement tools in the development of AI systems. There should also be a range of iterative feedback mechanisms and feedback integrated into the development of the systems. AI systems should also meet the intended goal requirements without deviating in potentially harmful ways. Unintended negative consequences should be avoided when designing AI systems. In a coffee cup, Alignment in AI is similar to ensuring your coffee machine’s settings are adjusted to produce the coffee strength and flavor you prefer.
These principles underscore the necessity of AI governance being proactive and comprehensive. AI governance should also be dynamic and inclusive. As technology changes and policies are implemented, it is important for an AI system developer to ensure that these principles are in mind and followed. As mentioned earlier, the core goal of AI is to assist humans in their activities and lives; these principles ensure that it achieves this goal.
