Introduction
Artificial intelligence (AI) is a field of computer science that studies
how to make intelligent machines, or systems that can perceive and act in the
world. AI is used in many fields, including computer vision, speech recognition
and natural language processing. It has been used by companies like Google and
Facebook to improve their products and services by making them more efficient
or effective at performing tasks humans do easily. But there are ethical issues
involved as well—for example: who will be responsible if an AI system makes a
bad decision? How transparent should AI be? Is there any way to ensure bias
doesn't creep into machine learning systems? In this article we'll explore some
of these questions using examples from current research projects within
academia and industry.
What is AI?
AI is a field of computer science that focuses on creating machines that
can think, reason and make decisions like humans. Machine learning (ML) is a
subset of AI that involves computers learning from data to create their own
programs or algorithms. Deep learning (DL) extends ML by incorporating layers
of artificial neural networks (ANNs) into the network itself rather than just
feeding it data about what's happening in the world around it.
The different types of AI include:
- Soft computing:
This type uses fuzzy logic instead of hard-and-fast rules like Boolean
logic used in traditional computers; soft computing can be used to predict
weather patterns or determine whether someone has high blood pressure
based on their behavior patterns over time—it's not quite as accurate as
more rigid forms but still produces useful results overall.
- Hard computing:
This refers to using standard formal methods such as propositional
calculus or first-order predicate calculus--the same kind you learned back
in high school math class! These methods allow us humans great accuracy
when solving problems; however, they're not always easy for computers
because there are too many possible outcomes (you could have one million
different outcomes for any given problem).
How does AI work?
AI is a type of machine learning, which is a process by which computers
can learn from data. It's not the same as machine learning, however. Machine
learning is an umbrella term for techniques that allow machines to make decisions
based on past experience and the information they have access to at any given
time.
AI processes this information in a different way than most traditional
methods: instead of using algorithms or rules (like those used in traditional
computer science), AI uses artificial neural networks (ANNs). ANNs are made up
of neurons—the basic building blocks from which all brains work—that act as
nodes connecting one neuron with another through electrical impulses. These
connections allow for complex computations such as vision recognition and
speech synthesis; when multiple ANNs are combined together into systems with
many layers, these become powerful enough for us humans too!
Who is making decisions based on AI?
AI is being used in many areas, including healthcare, finance and
education. It’s also increasingly being used by organizations to make hiring
decisions. In criminal justice and law enforcement, it can be used to predict
crime or identify suspects based on their social media posts. In the military,
AI can be used for battlefield simulations and drone strikes.
Bias in machine learning systems
Machine learning systems are only as good as the data they are trained
on. The more diverse and representative of all possible inputs that you have,
the better your machine learning system will be able to perform its tasks. If
there are certain groups of people who are excluded from being represented in
your dataset, then this can lead to bias in your system's predictions or
decisions about those groups—and therefore may yield unfair results for some
users.
For example, imagine a scenario where a company has access only to data
about gender: males vs females (with 0% overlap between them). A neural network
using this information might learn that men should get jobs over women because
they're better at math; however, if we add any other factor into our model
(like race), then we'll see how much different these two groups actually behave
when given equal opportunities!
Possible solutions to bias in machine learning systems
- Explainable AI
It's possible to build AI systems that have the same biases as humans, but they can also be programmed to learn from their mistakes and improve over time. Companies like Google are already doing this with their AI system AlphaGo, which beat Lee Sedol in 2016 at Go (the Chinese board game). The system learned from its mistakes and improved its gameplay abilities through trial-and-error playtesting until it could beat a human professional player for the first time ever without losing many games in between.
2.
Ethics in AI development:
AI systems should be created with integrity so that they don't harm
people or other living things; however, no one has yet figured out how exactly
we should ensure that our future data scientists keep us safe while also being
fair towards everyone else on Earth (i.e., not just white males).
Ethics of AI
AI is no longer the stuff of science fiction. Today, it's a reality that
we're all facing, with both positive and negative implications for our world.
The ethical challenges posed by AI are not new; they've existed throughout
history, but they've never been as difficult to solve before now. For example:
what should we do if our autonomous vehicle crashes into someone? How can we
ensure that robots don't become self-aware and decide to destroy us all? What
kind of person will have access to these technologies in the future?
To answer these questions—and many more—we need to carefully consider how
technology is developed and used today so that tomorrow's leaders can learn from
past mistakes made by those who came before them.
Ethics in AI. Takeaway
The ethical considerations of AI are not new. In fact, they go back to
the very beginning of the field, when Alan Turing wrote about "thinking
machines" in 1950. The question he raised was whether our computers could
think for themselves and make decisions based on their own logic or if they
would always be controlled by people who knew more than we did about what was
best for them.
The answer has evolved over time: today's AI systems are increasingly
powerful and capable—but also more transparent about their inner workings (and
therefore accountable) than ever before. While this may sound like progress
toward greater human control over technology, it hasn't led us away from concerns
about bias in machine learning systems or questions about whether these
technologies should be trusted with sensitive data such as medical records or
financial information; these issues remain unresolved even today!
Conclusion
The future of AI is exciting. We’ve made great progress in using machine learning to build systems that are powerful and useful, but they also raise important ethical questions. As we continue to develop these technologies, we need to be aware of their potential pitfalls and ensure that our goals are aligned with those of society as a whole.
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