Before directly stepping into AI governance let's first discuss briefly AI (Artificial Intelligence). The term Artificial Intelligence was first introduced in the year 1956 by John McCarty at the Dartmouth conference. He expounded that artificial intelligence is a science and engineering of creating intelligent machines. AI is a technique of getting machines to work and behave like a human. In the recent past, AI has been able to accomplish this by creating machines and robots that are being used in a wide range of fields including healthcare, robotics marketing, business analytics, and many more.

However many AI applications are not perceived as AI the reason is we often tend to think AI as robots. Have you ever think how Google can give you such exact search results or your Facebook feed always gives you relevant content based on your interest the answer to this question is Artificial Intelligence.

Artificial Intelligence

Artificial intelligence basically means giving a machine able to think like humans. It mainly fulfills the gap between machines and humans. It enables the machines to think and work like humans in the language of computer science.

ML Vs DL Vs AI

ML (Machine Learning) is a part of artificial intelligence but they both are different from each other. Machine learning are algorithms that analyze data, learn from that data, and make informed decisions based on that understanding for example you instruct a camera how to identify a dog by showing different breeds & faces of dogs so that when that camera faces a new dog it can automatically identify by itself that it is a dog because it has the experience and become an expert of identifying dogs but if a cat comes in front of that camera it fails drastically because it was instructed to identify dogs only. This is how machine learning works.

Have you ever wonder how alexa and siri works? Or how netflix discover which movies we like or dislike? Behind all these recent advances in intelligence is deep learning. DL (Deep Learning) is a subset of machine learning. The idea behind deep learning is that scientists thought that can we make a machine to learn like a human. In deep learning, you create a multi neural network architecture and mimic the human brain. For example, virtual assistant, google translation, face recognition, etc.

But when we talk about AI (Artificial Intelligence), The system which is used is intelligent like a human brain. AI technology does not have any limitations per se. It can add its inputs to grasp things, create new things, work, and take actions like humans. Hence, we can say that DL is a subset of ML and ML is a subset of AI.

AI governance

Artificial intelligence is not a sector-specific technology. It perhaps touches every sector of human life. We have a lack of effectiveness of governance for AI. There should be a lawful structure for ensuring that machine learning technologies are well researched and well developed to help humanity and navigate the adoption of AI systems adequately. We have to put In a place some kind of strong governance structure that would be more agile and had broader scope covering other sectors of society as well. Governance that would be more adaptive and responsive and nothing really effective implemented yet.

AI governance focus to inject ethics into technology advancement. Due to the huge growth in the implementation of artificial intelligence in almost every sector including healthcare, transportation, economics, education, public safety, and business the concern of AI governance is becoming important.

Why AI governance is important?

How do you essentially make sure that AI practices are good or bad or maybe ugly? How do you make sure that they all converge to something that will bring to something that will in essence bring well-being to humans and not bring harm? AI technology can cause more damage to humans and to the universe despite becoming profitable to humans if not governed accordingly.

Many nations many governments and even many industries have said that we need strong AI governance and due to this fast-emerging technology many nations many industries many practitioners have actually come up and articulated for themselves that how they would see themselves being governed in the use of AI and right now there were not many localities make any legislation on AI. We need to have a good set of guidance in the form of AI governance that tells people how to practice the use of AI in such a way that it will benefit humankind and not the other way around.

We have to adequately address AI governance before it can cause big consequences, in the same way, which data security issues or personal privacy caused like financial loss and damage to a company's reputation and other big cross boundaries crimes. The adoption of AI in government sectors may differ in developed, developing, and emerging countries due to different technological levels of adoption. There is still an emerging need for understanding the scope and effect of artificial intelligence based applications.

Conclusion
AI governance becomes necessary where machine learning algorithms are involved in making decisions. Where AI-based decisions are unfair or contradict human rights then AI governance comes into the picture. AI governance gives clarity to what role moral and ethical intuitions play when interacting with AI and also which sectors are appropriate and inappropriate for AI automation and to what extent the legal and institutional structures need to be involved, access, and control personal data.

AI governance frameworks could help organizations to learn, govern and monitor AI adoption. Not only government organizations but CEO of companies that are using AI technology also take responsibility and create AI governance charter for their organization. AI governance must be clear and relevant and also applicable to all leaders in an organization.