Comparison Of Ethical AI With Other Types Of AI

The goals, methods, and results of ethical AI can be contrasted to those of other AI subfields. Some important differences may be seen between ethical AI and other forms of AI, as listed below:

• Artificial intelligence that is built to carry out a single task, such as image recognition, natural language processing, or speech recognition, is called narrow AI. Ethical AI, as contrast to narrow AI, is concerned with making sure that AI is created and used in a way that is consistent with ethical values.
• The term “general AI” is used to describe AI systems that can accomplish any mental work a human can. Even while general AI is still in its infancy, it is already apparent that ethical considerations will play a significant role in shaping its future.
• In order for computers to learn from data without being explicitly programmed, a form of artificial intelligence known as machine learning is required. To ensure that machine learning models adhere to ethical norms, “ethical AI” places an emphasis on eliminating bias and increasing transparency.
• The term “deep learning” refers to a subfield of machine learning that makes use of elaborate neural networks. To prevent bias and ensure that everyone is treated equally, deep learning models must be open, explicable, and fair if AI is to be considered ethical.
• Robotics is a subfield of artificial intelligence that seeks to create autonomous robots capable of doing human-like activities. Because robots can have such a wide range of effects on people and communities, it’s crucial that we think about the ethical implications of our work.

To sum up, the main distinction between ethical AI and other forms of AI is the former’s focus on responsible and sustainable development and application of the latter’s AI technology. Ethical AI gives greater weight to issues of openness, fairness, and responsibility in the creation and application of AI than other subfields in the field.