AI Ethics and Accountability: Modern Artificial Intelligence Development

Name: AI Ethics and Accountability: Modern Artificial Intelligence Development
Author: Briana Brownell

Learn about modern artificial intelligence development including the potential risks and ethical consequences of AI

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Course: On-Demand

(ominous music) What is artificial intelligence? No one seems to be able to agree. That's because there is a disagreement about what intelligence really means. So there are actually several different definitions of AI. We'll discuss the four distinct AI paradigms described by Stuart Russell and Peter Norvig in their influential textbook "Artificial Intelligence, A Modern Approach". Many disagreements about what is and what is not AI boils down to a disagreement on which paradigm we're considering. These definitions fall along a quadrant with two dimensions. The first dimension is about whether we want our AI to be logical or intelligent in some abstract, optimal, or rational way, or if we want it to be intelligent in a human-like way. These might be only slightly different or very different depending on the scenario. We know that humans don't always act rationally. So, to build an artificial intelligence, do we want that AI to act rationally, but not necessarily human-like, or do we want it to act humanly but not necessarily rationally? The second dimension in our definition of AI is process versus outcome. Do we want the internal thinking process to be human-like, like our brains, or to be rational? Or instead, are we really worried about the outcome and whether the AI acts in a logical or human-like way? Putting these two dimensions together gives us four paradigms. The first, thinking humanly is best described as cognitive modeling. This paradigm is concerned with attempting to replicate human cognitive processes using computers. For example, mapping and simulating cognitive processes as closely as possible using what we know about the architecture of the human brain. The second, acting humanly, is most associated with the touring test, which attempts to ascertain whether a machine is able to think like a human. This second model seeks to define AI as a system that acts in a human-like way regardless of how it is constructed internally. At its extreme, it asks, "Can a computer act in such a way that it is indistinguishable from a human?" In the case of the touring test we focus on communication between an AI and a human. Can this AI have a conversation with a human in such a way that the human doesn't realize they're talking to an AI? The third, thinking rationally, replaces the internal structure of human cognition with abstract reasoning. These systems of reasoning may differ from how humans think and contain many abstract optimization problems that are typically solved mathematically. This is often called the Laws of Thought paradigm because it provides an abstracted way of modeling thought processes, like perception and reasoning. Finally, acting rationally looks at the development of rational agents. There are many different algorithms that could be used to make intelligent decisions in a rational way using different methods of optimization. These intelligent agents may not act or make decisions in a human-like way at all. Most businesses use the rational agents paradigm because it takes the broadest definition of intelligence. It does not expect the internal workings nor the external output to be human-like, but rather allows the designer to choose the type of optimization and the internal structure to create something intelligent. It's also the most distinct from cognitive modeling, the furthest away from thinking and reasoning the way that humans do. Although this model is the most common, it's not the only one that has a place in modern AI. Artificial neural networks, which initially arose from the cognitive modeling approach, have regained popularity in the last decade. There is a lot of work being done in that area, especially to make systems of reasoning that are more human-like and to incorporate AI systems that can complete many different tasks just like our brains can. This, in essence, is the difference between narrow, or weak AI, versus general, or strong AI. Most AI systems today are narrow AI systems that perform a narrow range of tasks, sometimes just one. An example would be classifying images into a set of categories or transcribing speech to text from an audio interview. General AI, sometimes called artificial general intelligence or AGI, is a wider AI system that can perform many kinds of intelligent tasks, much like humans are able to. The ethics and accountability around narrow AI differ from those around AGI. As more and more AI crops up in various places, technologists are increasingly interested in creating ethical AI and understanding accountability in terms of AI systems.

Intermediate Provider : Briana Brownell 7 Lessons · 24m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

These lessons on Modern Artificial Intelligence Development will cover the history and background of artificial intelligence and the fundamental aspects and concepts of artificial intelligence, including how AI works as a decision system. We’ll also explore how AI is used in everyday life, machine learning as an automated system that learns from information, and the unique considerations about big data used in AI systems.

Humankind has been fascinated with the creation of artificial intelligence since ancient times, and modern artificial intelligence has become an area of intense interest in terms of research and development. With the growth in technology and advances made in computer science, computers have evolved from simple calculators to sophisticated machines. We'll consider AI systems from four paradigms: cognitive modeling, laws of thought, Turing's test, and rational agents. We'll see how the differences between them impact how we build and analyze the AI system, and ultimately, how that impacts ethics and accountability.

With increasing interactions with AI systems, there are more and more issues around ethics and accountability. You will learn about the ethical and legal issues that surround AI, and the role of AI in society.

What You'll Learn

Describe the history and background of artificial intelligence
Differentiate between the four paradigms of AI: cognitive modeling, laws of thought, Turing's test, and rational agents
Recognize the role of AI in critical systems and the implications of AI for critical systems
Explain how AI works as a decision system and how machine learning learns from information
Examine the unique considerations of big data used in AI systems
Identify the ethical and legal issues surrounding AI and the role of AI in society

Key Takeaways

AI can be understood through four paradigms - cognitive modeling, laws of thought, Turing's test, and rational agents - and the differences between them affect how AI systems are built and analyzed.
The choice of AI paradigm ultimately impacts questions of ethics and accountability.
Machine learning is an automated system that learns from information, and big data raises unique considerations when used in AI systems.
Increasing interactions with AI systems raise growing issues around ethics and accountability.
The course addresses the ethical and legal issues surrounding AI and the role of AI in society.

Frequently Asked Questions

What topics does this course cover?

It covers the history and background of artificial intelligence, fundamental concepts of AI including how AI works as a decision system, machine learning, big data, AI in everyday life, AI in critical systems, and the risks, ethical, and legal issues surrounding AI.

What are the four paradigms of AI discussed in this course?

The course considers AI systems from four paradigms: cognitive modeling, laws of thought, Turing's test, and rational agents, and examines how their differences impact how AI systems are built, analyzed, and held accountable.

What skills will I gain from this course?

The course covers skills including AI/ML inference, applications of artificial intelligence, artificial intelligence systems, artificial intelligence development, and computational intelligence.

Does this course address the ethics of AI?

Yes. It explores the ethical and legal issues that surround AI, the role of AI in society, and how the differences between AI paradigms impact ethics and accountability.

What lessons are included?

The lessons are: What is Artificial Intelligence?; Decision systems as AI; Machine Learning; Big Data; AI in everyday life; AI in critical systems; and Risks and Consequences of Artificial Intelligence.