Artificial Intelligence And Governance
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 IntelligenceArtificial 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 AIML (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 governanceArtificial 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.
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.
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