Artificial Intelligence may be a tool a bit like the other - it's not inherently good or bad - it's the actors and their intents that matter here. AI is helping in healthcare and governance to improve people's lives. It is also getting used online for cheating, forgery, sowing discord, also as for advanced offensive weaponry.
Building AI with ethics may be a pertinent problem now quite ever as AI is being
applied to more sectors. Companies are not only using AI to recommend the next
product to us, they are also using it in areas that are risk-sensitive. The extent to
which machine learning is employed in safety-critical applications today has made the
issues of ethical AI even bigger.
We have not been ready to solve this dilemma from a person's perspective, how can we expect machines to know this?
“Growing complexity of systems and processes in business and governance, as
well as the growing volume of our personal online and offline interactions - they all
need AI solutions for better management. The ethics in AI, therefore, are hugely
important”.
While the answer to ‘What is ethical’ varies for every industry, in basic it leads to
the aspects of privacy, morality, transparency, security, solidarity. The ethics for AI
include the purpose of AI’s deployment (healthcare or warfare), and the fairness in
the AI’s decision-making.
But we need to make the Artificial Intelligence and Machine Learning models more ethical for people to trust it.
While Artificial Intelligence and Machine Learning are getting used to bridge the gap in many sectors, though we don’t trust these models enough to give them power to decide about life and death.
“Covid has been a raging issue for the last couple of years. There have been
peripheral issues where ML was used, but not in many cases where it involved a
risk of life -- despite the severity of the crisis. We don't yet trust AI and ML when
it involves making decisions that affect a life”.
Brillica Services is a prominent provider of comprehensive Artificial Intelligence and Deep Learning certification training course that empowers you with in-depth knowledge on neural networks, logistic regression, vectorization and provide hands-on experience on real-world applications of deep learning.
As AI decisions influence and impact people’s lives at scale, it's crucial that
organizations take a proactive approach to designing AI responsibly. Architecting
and deploying AI models that are trustworthy, fair and explainable is the key.
The conversation about ethics and responsible AI remains evolving and no-one has
a definitive answer to how to move ahead. However, there have always been
some best practices and things to keep in mind while designing and working with
Artificial Intelligence and Machine Learning.
“Organizations got to adopt proven qualitative and quantitative techniques to
assess potential risks and mitigate bias in AI models. Deploying the right set of
tools and establishing practices to thoroughly and continuously investigate
sources of bias and understand the trade-offs and impacts of fairness decisions is
critical.”
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