Digital Marketing Programs & Skills To Master
Advanced Digital Marketing & Growth Hacking Program
6 Courses in 1 Program
5 Case studies
17 Tools & 5 Live Projects
PG Program in Digital Marketing and Analytics
15 Courses in 1 program
25 Case studies
40 Tools & 15 Live Projects
Mastery (Coming soon)
UI/UX Programs & Skills To Master
Advanced Certification in UI/UX & Design Thinking
6 Courses in 1 program
3+ Case Studies
7 Tools & 8 Live Projects
PG Program in UI/UX & Design Thinking
9 Courses in 1 program
10 Case Studies
12 Tool & 15 Live Projects
Data Science Programs & Skills To Master
Advance Data Science
with AI & Machine Learning
2 Course in 1 program
11 Tool & 2 Live Projects
PG in Full Stack Development with Java
20 courses | Duration: 7 Months
19 Tools & 10 Live Projects
PG in Data Science & Data Analytics
9 courses | Duration: 6 Months
Case Studies: 20
AUGUST 13, 2022
AI has taken over the technological landscape by storm. Artificial Intelligence regulates almost everything,
from using text editors to summoning Alexa. Given AI's potential, companies rapidly implement it into their
business operations. However, with AI's advanced analytics comes the issue of transparency of ethics—a
concern raised by professionals and experts.
Explainable AI (XAI) offers a more efficient
to harnessing AI's
capabilities while maintaining
XAI takes AI up a notch; it describes Artificial Intelligence models and their abilities. XAI is a set of
processes that makes machine learning-generated results comprehensible, reliable, and trustworthy.
Explainable Artificial intelligence enables accurate and transparent characterisation of AI models, enabling
confident and AI-driven decision-making. Not only does XAI increase productivity in a work environment, but
it also helps organisations modify their AI approach. An example of XAI improving AI tools is the role of
explainable AI in credit risk management.
As AI continues to develop, humans are under pressure to understand the algorithm from scratch and retrace
the entire process. The complete algorithm calculation process, known as a 'black box,' is a direct result
of data—something even data scientists, who developed the calculation, struggle with understanding.
Therefore, they cannot precisely explain how AI generated the output; what was the thought process?
Explainability in AI solves the AI-caused dilemma by helping developers
the workings of a system,
including reassuring them of the system's functionality, regulatory guidelines, and how the AI-enabled
framework affects decision-making or modifies outcomes.
Despite's AI boundless potential, data scientists worry about its ethical ramifications. Due to AI's
abstract stance on fair and safe use, awareness of the need for a more responsible framework for AI
technologies is rapidly increasing. Companies and scientists are considering the moral, ethical, and legal
viewpoints of deploying AI-powered machines.
Advancements in machine learning allow AI systems to make decisions on behalf of humans without manual
assistance or interference. While this may seem efficient and advanced, it compromises human autonomy and
control. Some explainable AI examples include self-driven cars and
autocorrect. Self-driven cars function
without manual operation. They accelerate, drive, hit the brakes, switch lanes, reverse, and park
independently. However, as advanced as it might seem, removing human control might lead to roadside
accidents. Similarly, phones autocorrecting words implies that Artificial Intelligence understands the
person's mind and text better than the person does, thus removing the user's autonomy from the equation.
Therefore, with AI development comes the issue of control, transparency, and accountability;
principles follow the transparency model.
A call for transparency and accountability in XAI ensures that AI-led systems follow a responsible approach,
exhibit reliable behaviour, and justify their actions regarding data acquisition, usage, and
Accountability means explaining and justifying one's actions to the people involved.
when the decision-making systems successfully and correctly describe the thought process behind their
such as when, how, and why. In the case of AI, accountability refers to the need to adhere to ethical,
societal, and legal rules integral to specific operations that the AI system must abide by. Responsibility
Artificial Intelligence requires the framework to justify its guiding principle and explain its functioning.
Transparency means clarity. It refers to the need to clarify the factors surrounding
involved in AI
operations, such as the mechanisms through which Artificial Intelligence tools work, adapt to their
surroundings, and use the data generated.
The impact of Artificial Intelligence on society and daily activities is unquestionable, undeniable, and
unmatched. As technology advances, AI's potential will grow, seeping into the remaining corners of the
community. However, like other tools and technologies, conventional AI models also have shortcomings and
limitations that raise concerns and question their authority, authenticity, and reliability. The AI vs XAI
debates touches upon these concerns in great detail.
One thing that Artificial Intelligence has in common with society is biases. Similar to how biases affect
One thing that Artificial Intelligence has in common with society is biases. Similar
how biases affect one's societal standing, biases in AI-powered data models and processes affect their
functioning. AI bias is not only terrifying but it also inserts identified or hidden prejudices of the
developers. Unlike tainted data, biased Artificial Intelligence works in multiple stages. For instance,
AI in deep-learning processes can affect the entire framework and design guidelines; incorporating
explainable AI in deep learning can rectify this.
Artificial Intelligence has shortcomings, including a lack of transparency and
accountability. The input data can be tampered with, modified, or wiped out without visibility. A
AI model is necessary for data scientists or developers to track the root of the problems or identify the
A frightening downside of unregulated AI practices is creating accurate user
without their knowledge
or consent. AI algorithms identify user behaviours, such as typing patterns, log-in times, and data
to develop their personal profile. AI models can predict a user's check-in times, actions, messages, and
location based on their online activity and history. Creating profiles according to AI prediction works on
accurate and precise understanding of a user's online behaviour or interactions with their contacts or the
applications on their devices.
XAI plays a significant role in the field of data science.
Incorporating XAI in data science projects reduces the margin of error, especially
fields that require
accurate decision-making tools, such as healthcare, legal, education, and finance. A single wrong
can crumble their systems. Furthermore, oversights in result generation make it difficult to trace its
On the other hand,explainable AI principles minimise the scope of errors
reduce their consequences by
identifying the source of the problem and improving the model. Deploying XAI helps AI models like AI bots
perfectly analyse human speech to create authentic pieces that look natural and are error-free.
AI bias is an understated concern that comes with using AI tools. The developers'
reflects in the
functioning of the AI model, such as racial profiling in self-driven cars and gender prejudice in Amazon
devices. XAI eliminates AI bias by explaining the model's decisions.
AI devices must comply with regulatory guidelines to operate freely without
restrictions. Various AI models
require regulatory compliance and confidence from the owner or operator to work seamlessly. Some explainable
AI examples where XAI ensures AI models receive high-code assurance can be seen in autonomous
card readers, and medical diagnostics.
XAI comes in handy in various data science projects, from credit risk management and
scores to deep learning
and decision-making. However, in addition to data science projects that involve regulations, XAI can be
implemented in industries and sectors that work without regulation restrictions, such as:
XAI has a bright future in the AI industry. Its design, features, and abilities seek
technological landscape and sector positively.
Explainable AI's importance lies in its ability to increase productivity. XAI models
mistakes and identify the room for improvement, making it efficient for ML teams to monitor and oversee AI
systems conveniently. For instance, analysing specific modules of an AI model result can help developers
ensure whether the model identifies user patterns and provides accurate predictions. Correct analysis
the margin error, increasing productivity and improving performance.
XAI helps firms prevent and control risks. Since XAI adopts a moral and ethical
approach, it ensures AI
models work according to media, society, legal, and regulatory norms. Explainable AI principles include
abiding by moral codes. Legal teams use XAI to ensure the system adheres to prescribed rules and
and aligns with the organisation's internal goals, policies, and objectives.
Explainable AI's importance is boosted by its
to enhance the brand value. It
allows technical and
business departments to collaborate and ensure the company meets its goals timely, thus generating value
improving its brand image.
Artificial Intelligence is a technological wonder designed to make daily operations
efficient and to
take the digital age to new heights. However, AI's limitations hinder its functionality, such as a lack of
transparency and AI bias.Explainable AI gives meaning to an AI model's
It rectifies the existing
problems within the AI system by making the framework clear, transparent, error-free, and unbiased.
May 6, 2022
The Importance of Empathy in UI/UX Design
The Power of Influencer Marketing in the Digital Age
The Rise of Explainable AI (XAI) in Data Science