AI & Responsibility
1. AI & Responsibility
Welcome to Introduction to Responsible AI. This module will consist of four lessons. You will learn how to describe the importance of responsible AI. List Google's AI principles, describe the best practices for responsible AI development. Identify how responsible AI principles can be applied during product development. Let's start with AI and responsibility. Artificial intelligence, or AI, is everywhere, you may have even heard the phrase generative AI. Most of us already have daily interactions with artificial intelligence. From predictions for traffic and weather to recommendations for TV shows, AI is becoming more common. And new AI systems are continuously being developed at an extraordinary pace. Still, AI is not infallible. No AI model has 100% accuracy, and AI systems are complex. When AI systems are used in real world contexts, they can fail to behave in expected ways, which reduces their realized benefit. Failures in behavior can come from model misuse, like the case of fraudsters who use AI to mimic a CEO's voice. This is because AI models are often under specified. They perform well in the situation in which they are trained, but might not be robust or fair in new situations. There is a common misconception with artificial intelligence that machines play the central decision making role. In reality, it's people who make decisions. People are involved in each aspect of AI development. They collect or create the data that the model is trained on, they design and build these models. They control the deployment of the AI and how it's applied in a given context. Essentially, human decisions are threaded throughout our technology products. And every time a person makes a decision, they are actually making a choice-based on their own values. Whether it's the decision to use generative AI to solve a problem as opposed to other methods. Or anywhere throughout the machine learning lifecycle, that person introduces their own set of values. This means that every decision point requires consideration and evaluation. To ensure that choices have been made responsibly from concept through deployment and maintenance. Because there's potential to affect many areas of society, it's important to develop AI technologies with ethics in mind. When we're talking about new AI technologies and ethics, there's a lot of work in progress on laws, policies, regulations, etc. Unfortunately, there is not a wide global consensus on what these ethics are yet. What we have learned is we can't wait for society to catch up and codify the rules. We have a voice in shaping what those norms will be. It should be noted that ethics is different from laws and policies. Law draws insights from ethics, and ethics inform policy but most ethical norms are not codified. So we need to define what is ethical behavior to put it simply, ethics can be explained in these three ways. Ethics is what we ought to do, not the same as what is actually done or what most people say or think should be done. Ethics is what others can rightly blame us for not doing, even if we suffer no actual punishment. Ethics is what sustains our flourishing together in human society, ethics is an evolved tool for living well as social creatures. Responsible AI doesn't mean to focus only on the obviously controversial use cases though. Without responsible AI practices, even seemingly innocuous or good intent, AI use cases could still cause ethical issues or unintended outcomes. They might not even be as beneficial as they could be. Ethics and responsibility are important, not least because they represent the right thing to do. But also because they can guide AI design to be more beneficial for people's lives. So what is responsible AI? What we can say is that responsible AI requires an understanding of the possible issues, limitations, or unintended consequences. This understanding is aimed at developing AI ethically. There is not a universal definition of responsible AI, nor is there a simple checklist or formula that defines how responsible AI practices should be implemented. Instead, individuals and organizations are developing their own AI foundational principles that reflect their mission and values. While these principles are unique to every organization, if you look for common themes. There is a consistent set of ideas across fairness, interpretability, privacy, and safety. What we found at Google is that following these foundational responsible AI principles leads to developing successful AI. We learned that accountability ensures that your products are beneficial to everyone. Evaluating your AI systems, both when they perform as intended and when they don't, is crucial to building accountable products. Building responsibility into any AI deployment makes better models and builds trust with your customers and your customers' customers. If at any point that trust is broken, you run the risk of AI deployments being stalled or unsuccessful. At worst, it is harmful to the stakeholders that those products affect. Lack of trust in AI systems is a growing barrier to adoption in enterprise. With more organizations selecting enterprise products based on AI commitments and practices, ethical development drives innovation. Empowering AI decision makers and developers to consider ethical considerations enables them to find new, innovative ways to drive your mission forward.2. Let's practice!
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