Ethical AI : The Model Dilemma
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.
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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