Nemawashi: Laying the Groundwork

Originally Nemawashi refers "Turning the Roots". Ne implies Root and Mawashu implies to turn something, to put something around something else (Wikipedia).

The whole idea of Nemawashi is that if someone wants to transplant a tree to a new location, then, before transplanting the tree, one has to dig the ground carefully sometime before transplanting the tree. This would help to establish the tree to its new location and the soil of the new location accept the tree with minimal resistance and give the opportunity to grow the roots of the tree. This all because of the groundwork is done prior to transplantation of the tree.

Nemawashi is Japanese term that refers to the process of "laying the groundwork" for a project or decision like transplanting a tree by gathering support and consensus among stakeholders before formal discussions or meetings take place. It involves informal consultations, discussions and relationship-building to ensure alignment and reduce resistance when the proposal is officially presented on the table.

This practice is tied closely to the Japanese business culture that emphasize collaboration, respect and harmony in decision-making. It helps to gauge the reaction of the high-ranking people and give the opportunity to win the heart of them before the proposal puts on the table.

The Japanese use Nemawashi to foster collaboration, efficiency, and consensus-driven decision-making.

• It builds consensus early by ensuring that all stakeholders are consulted and aligned before formal decisions are made to reduce conflicts, delays, and resistance during implementation, leading to smoother execution.
• It encourages inclusivity by involving employees at all levels in informal discussions, promotes a sense of ownership and engagement. This leads to better ideas, innovation, and commitment to the organization's goals. 
• It emphasizes open and respectful communication, which strengthens relationships and trust among team members. This creates a harmonious work environment and improves teamwork.
• It reduces risks by addressing concerns and gathering feedback beforehand, Nemawashi practice helps identify potential issues early, allowing for adjustments before formal decisions are made. This minimizes risks and costly mistakes.
• It promotes long-term thinking using thoughtful, deliberate decision-making rather than rushed or top-down directives. This aligns with the Japanese focus on long-term sustainability and quality.
• It facilitates change management through easy transitions by ensuring everyone is on board and understands the rationale behind the decisions. This reduces resistance and fosters adaptability. 
• It aligns with Kaizen (Continuous Improvement) by encouraging ongoing feedback and iterative improvement. It ensures that decisions are refined and optimized through collective input.

For Example:

Quality department wants to install automatic inspection machines, but HOD feels that this proposal will definitely be challenged by the production department, create fear of job loss among the quality personnel, and due to the heavy cost involved this will not be supported by the finance department.

Quality department starts doing the groundwork by continuously discussing the benefits of Automatic Inspection Machines with the production head, chief financial officer and quality personnel.

• Automatic inspection machines help to improve the quality of production by rejecting only those parts which can't be able to be detected through 100% inspection by quality personnel. 
• Many critical defects are passed during 100% inspection can't be traced on conveyer by the quality personnel.
• This leads reduced customer complaints, eliminate rework and resorting, and so extra manpower for rework and resorting.
• Automatic machines will be used to detect only for those defects which can't be able to see by naked eyes, but they are potentially dangerous for the customer.
• The cost of automatic inspection machines installation will be recovered within a year and at the same time they will make our organization more competitive in the market.

Quality department continuously organizes the meeting with production, finance along with quality personnel and discuss the pros and cons of installation of automatic inspection machines try to win the heart collaboratively, to build consensus and to reduce resistance before formally put the proposal on table in front of top management.

Finally, inconclusion, the department becomes more efficient, and the employee morale remains high because the changes were introduced collaboratively. The company achieves its goals without the disruptions that often accompany top-down decisions.

This example demonstrates how Nemawashi ensures smooth decision making, fosters trust, and drive business excellence by involving stakeholders early and valuing their input.

Blockchain Technology and Lean Six Sigma

In today's rapidly evolving business environment, organizations across manufacturing and service industries are under constant pressure to enhance efficiency, maintain quality, and build trust in their operations. This drive for operational excellence requires not only proven methodologies like Lean Six Sigma but also the integration of innovative technologies like blockchain. Together, these approaches enable businesses to achieve superior process transparency, data integrity, and efficiency - hallmarks of a competitive and sustainable enterprise. 
 
For a manufacturing sector, blockchain's ability to track and trace materials, verify supplier data, and ensure compliance offers a revolutionary way to address persistent challenges, such as defects, inefficiencies in the supply chain, and delays. Lean Six Sigma's structured framework complements this by identifying root causes of waste and inefficiencies, enabling a proactive approach to improvement.
 
In the service industry, where customer experiences hinge on accurate data, seamless processes, and timely delivery, blockchain ensures reliable, tamper-proof information sharing across stakeholders. When paired with Lean Six Sigma, it becomes possible to enhance service quality by addressing bottlenecks, improving workflows, and reducing errors in customer-facing operations.

The Blockchain technology

Blockchain technology is an advanced database mechanism that allows transparent information sharing within a business network. A blockchain database stores data in blocks that are linked together in a chain. The data is chronologically consistent because you cannot delete or modify the chain without consensus from the network. Every time a change is entered with a timestamp with a unique hash in the block and this hash is shared with all other computers in the network, but this change is valid only the majority of participants accepted. As a result, you can use blockchain technology to create an unalterable or immutable ledger for tracking orders, payments, accounts, and other transactions. The system has built-in mechanism that prevent unauthorized transaction entries and create consistency in the shared view of these transactions. 

 

Blockchain emerged in late 2008, in the midst of the global financial crises. Satoshi Nakamoto released a new protocol for a "A Peer-to-Peer Electronic Cash System" and created a digital currency or cryptocurrency called Bitcoin based on blockchain technology, with the first Bitcoin transaction being realized on January 12, 2009. 

Traditional database technologies present several challenges for recording financial transactions. For instance, consider the sale of a property. Once the money is exchanged, ownership of the property is transferred to the buyer. Individually, both the buyer and the seller can record the monetary transactions, but neither source can be trusted. The seller can easily claim they have not received the money even though they have, and the buyer can equally argue that they have paid the money even if they haven't.

 

To avoid potential legal issues, a trusted third party has to supervise and validate transactions. The presence of this central authority not only complicates the transaction but also creates a single point of vulnerability. If the central database was compromised, both parties could suffer. 

 

Blockchain mitigates such issues by creating a decentralized, temper-proof system to record transactions.

 

Lean Six Sigma Methodology

Lean Six Sigma is a discipline that delivers customer value through efficient operations and consistent quality standards. It's a methodology that focuses on improving performance by systematically removing waste and reducing variation. Lean focuses on efficiency and eliminating waste. Six Sigma on the other hand, focuses on quality and consistency. When used together, these problem-solving skills can transform an organization.

How Blockchain can be integrated with Lean Six Sigma

1. Enhancing Process Transparency: 

• Immutable Records: Blockchain's decentralized ledger ensures all transactions or process changes are permanently recorded, allowing clear tracking of process steps.
• Real-Time Visibility: Smart contracts and real time data sharing enable instant updates across stakeholders, ensuring full transparency in processes like supply chain management or production. 

2. Improving Data Integrity

• Data Authenticity: Blockchain provides a single source of truth for process metrics and eliminates the risk of data manipulation or tampering. 
• Audit Trails: Permanent records ensure easy audits and help identify deviations in process performance.

3. Increasing Efficiency

• Automated Workflows: Smart contracts can automate routine tasks or approvals, reducing cycle times in workflows.
• Eliminating Redundancies: Blockchain ensures that data duplication and redundant approvals across departments are minimized. 

4. Strengthening Process Improvement Projects

• Data Collection: Reliable, tamper-proof data helps with accurate root cause analysis in Six Sigma's DMAIC or DMADV framework.
• Cross-Functional Collaboration: Blockchain facilitates seamless collaboration between teams by offering secure access to shared data.

Challenges of Implementing Blockchain in Lean Six Sigma

1. High Initial Cost: Developing and implementing a blockchain infrastructure requires heavy investment and training employees to use the technology adds to the cost.
2. Complexity: Integrating blockchain into legacy systems and aligning it with Lean Six Sigma methodologies requires significant technical expertise. Process reengineering may be necessary to make blockchain compatible with existing workflows. 
3. Data Privacy and Security: Although blockchain ensures data transparency, some organizations may struggle with balancing privacy and accessibility of sensitive information.
4. Scalability: As transaction volumes grow, maintaining the efficiency of the blockchain can become challenging, especially for real-time applications.
5. Cultural Resistance: Employees may resist change due to unfamiliarity with blockchain technology.

Example of Successful Integration of Blockchain and Lean Six Sigma Integration:

 

1. Walmart and Food Supply Chain:

• Walmart uses blockchain (IBM Food Trust) to track its food supply chain, enhancing transparency and reducing waste.
• Lean Six Sigma principles are applied to identify inefficiencies in the supply chain, such as delays in product delivery or issues in quality.

2. Maersk and TradeLens

•  Maresk's TradeLens platform leverages blockchain for shipping and logistics, offering real-time visibility of shipments across the supply chain.
• LSS principles were applied to streamline container tracking and reduce lead times.

 3. Pharmaceutical Industry (Pfizer and MediLedger)

• MediLedger, a blockchain based platform, ensures the integrity of the drug supply chain and reduces counterfeit medicines.
• Six Sigma tools helped identify inefficiencies in tracking drugs, and blockchain was implemented to provide end-to-end traceability.

4. BHP and Mining Operations

• BHP Billiton uses blockchain to improve transparency in mineral tracking and vendor performance. 
• Lean principles help eliminate waste in the supply chain, while blockchain ensures data accuracy and auditability

5. BMW and Auto Part Traceability:

• BMW uses blockchain to track the origin and quality of auto parts in its supply chain. 
• Lean Six Sigma tools are integrated to reduce defects and inefficiencies during production.

Inconclusion, integrating blockchain technology with Lean Six Sigma provides a unique opportunity for manufacturing and service industries to enhance transparency, secure data integrity, and drive process efficiency. By combining the power of data immutability and automation with structured problem-solving and continuous improvement, organizations can achieve sustainable operational excellence. This synergy not only addresses current challenges but also prepares businesses to thrive in an increasingly digital and competitive landscape.

Thinking, Fast and Slow

Decision-making and leadership are pivotal aspects of business and personal life. They way individuals and leaders make decisions can significantly impact outcomes, both in the short-term and long-term. Daniel Kahneman's dual-process theory as explained in his influential book "Thinking, Fast and Slow," provides a profound framework for understanding the cognitive process involved in decision-making. This theory delineates two distinct modes of thinking.

• System 1: Fast Automatic, and Intuitive
• System 2: Slow, Deliberate, and Analytical

In leadership, decision-making is a multifaceted process that requires a well-calibrated balance of intuition and analysis. A leader's ability to discern which approach to employe in differing situations directly impacts the efficacy of their guidance and possible success. Some leaders tend to rely on rapid, instinctual judgements, and others are more into strategic and well-grounded decisions.

Understanding System 1 and System 2

 

Often the best leadership and teamwork come from the balance between the two. 

Understanding the interplay between System 1 and System 2 thinking is pivotal, as both are intrinsic to the art of leadership. Mastery of these cognitive systems can foster a team culture that is reflective, nuanced, and profoundly influential.

System 1 Thinking:

• Characteristics: Fast, Automatic, and Intuitive
• Function: Operates effortlessly and quickly, often using heuristics (mental shortcuts) based on past experiences and patterns.
• Examples: Recognizing a friend's face in a crowd, answering simple math questions (e.g., 2+2)

This system operates subconsciously, functioning with remarkable speed and efficiency, often without conscious awareness. It is our intuitive and automatic mode of thinking, which can be quite adept at making quick judgments based on patterns and past experiences. However, it is also susceptible to cognitive biases, which can cloud our decision-making processes.

System 2 Thinking:

• Characteristics: Slow, Deliberate, and Analytical.
• Function: Requires conscious effort and attention, used for complex problem-solving and critical thinking.
• Examples: Solving a complex math problem, planning a long-term project.

This system takes a more deliberate and logical approach to problem-solving. It is slower and requires more cognitive resources, but it excels at analytical thinking and complex decision-making tasks that necessitate attention and careful consideration.

 

Leaders must learn to balance these two systems, engaging System 1 for its rapid processing while employing System 2 when thorough analysis is essential, ultimately cultivating an environment of informed and balanced leadership decisions.

The Basic Idea

When commuting to work, you always know which route to take without having to consciously think about it. You automatically walk to the subway station, habitually get off at the same stop, and walk to your office while your mind wanders. It’s effortless. However, the subway line is down today.

 

While your route to the subway station was intuitive, you now find yourself spending some time analyzing alternative routes to work in order to take the quickest one. Are the buses running? Is it too cold outside to walk? How much does a rideshare cost?

 

Our responses to these two scenarios demonstrate the differences between our instantaneous System 1 thinking and our slower, more deliberate System 2 thinking.

 

However, even when we think that we are being rational in our decisions, our System 1 beliefs and biases still drive many of our choices. Understanding the interplay of these two systems in our daily lives can help us become more aware of the bias in our decisions—and how we can avoid it.

Why Dual Process Thinking?

1. Efficiency and Speed: System 1 thinking allows for quick decisions based on intuition and past experiences. This is particularly useful in situations where immediate action is required, such as avoiding danger or making snap judgments.
2. Accuracy and Deliberation: System 2 thinking provides a more analytical and deliberate approach to decision-making. It’s essential for complex problem-solving, critical thinking, and tasks that require careful consideration.
3. Evolutionary Advantages: In prehistoric times, rapid responses could mean the difference between life and death. Those who could quickly identify threats and opportunities had a higher chance of survival. Over time, this fast-thinking system became ingrained in the human brain.
4. Balancing Cognitive Load: Not all situations require deep thought and analysis. By using System 1 for routine tasks and System 2 for more complex ones, humans can manage their cognitive resources more effectively.
5. Flexibility and Adaptability: Having two systems allows humans to adapt to a wide range of scenarios. Whether it's making a quick decision in a high-pressure situation or planning for long-term goals, dual process thinking provides the flexibility to choose the appropriate approach.

This combination of fast and slow thinking enables humans to navigate the complexities of life more effectively. It's a fascinating aspect of human cognition!

Balancing System 1 and System 2 Thinking

To navigate the dynamics of leadership with acumen, it’s essential for leaders to maintain an equilibrium between instinct and intellect. Understanding the dichotomy of System 1 and System 2 can prevent overconfidence in intuitive conclusions. Leaders can foster wisdom by integrating the experiential with the analytical, realizing when to harness the rapid, pattern-based insights of System 1 and when to summon the methodical, logical prowess of System 2.

 

The following points elaborate how the balancing can be done between System 1 and System 2

1. Enhancing Decision-Making with Dual Systems: In the constant balance of leadership decision-making, the harmonious interplay between System 1 and System 2 thinking is essential. System 1, operating with fluid intuitiveness, can yield swift judgments based on heuristic cues, mental shortcuts and past experiences. Conversely, System 2 provides a methodical counterbalance, using deliberate reasoning and critical analysis. Effective leaders harness both systems judiciously – they cultivate the rapid, subconscious processing of System 1 when immediacy is paramount and deploy the calculative scrutiny of System 2 when complexity demands rigor. The mastery of switching between these cognitive gears optimizes decision-making and embodies the wisdom of nuanced, situational leadership.

2. Tackling Complex Problems: Invariably, leaders confront convoluted dilemmas that test their intellectual power and decision-making integrity. Such scenarios mandate an interlacing of intuition and analysis, drawing from both cognitive systems. Expert leaders know when to let System 1 guide them through gut feelings and patterns while also discerning when these issues necessitate the slower, more judgmental process of System 2.

3. Navigating Risk and Uncertainty: Wisdom in leadership during uncertainty is demonstrated through the judicious use of both Systems 1 and 2. Intuition guides immediate actions, while analysis informs long-term strategy. Leaders' adept in utilizing both cognitive processes are better equipped to anticipate risks, prepare contingencies, and lead with confidence even amidst turbulence. This duality of thought enhances the resilience of the team and the stability of the organization when facing the unknown.

4. Interaction Between Systems and Emotions:

• Dynamic Interplay: There is a dynamic interplay between emotions and both systems. While System 1 may generate an emotional response, System 2 can reflect on this response and potentially reshape it based on further thought and analysis.
• Impact of Mood and State: The current emotional state of an individual can influence the effectiveness of both systems. For example, when someone is under stress, System 2’s ability to regulate emotional responses from System 1 can be impaired, leading to more emotionally driven decisions.

5. Cultivating Wisdom in Leadership Practices: Leadership wisdom emerges from the interplay between instinct and intellect, where intuition informs but does not dominate strategic decisions. This nuanced balance fosters discernment and judicious action.

 

In leadership, a synergy between the fluid intelligence of System 1 and the analytical prowess of System 2 is vital. Harnessing both allows for responsive leadership that remains rooted in a landscape of data-driven strategy and evidence-based practice

 

The art of “knowing when” and “knowing how” becomes the cornerstone of a leader’s wisdom.

6. Learning from Mistakes: Mistakes, while undesirable, are unavoidable. Reflective practice is key to learning from errors. When leaders engage in introspection after a misstep, they activate their System 2 thinking, promoting a detailed analysis of the event. This process helps in identifying the underlying factors and in devising strategies to prevent recurrence. Importantly, acknowledging the existence of an error is the first step to wisdom-enhancing correction.

 

Wisdom is not innate, but can be cultivated through experiences, especially missteps.

 

Leaders who demonstrate a growth mindset — an understanding that ability can be developed through dedication and hard work — tend to foster a culture where mistakes are viewed as opportunities for advancement. This perspective encourages team members to approach challenges boldly and learn from outcomes, creating a resilient and innovative workforce.

7. Encouraging Reflective Thinking: Reflective thinking is fundamental in the process of learning from mistakes. It demands purposeful pausing to consider the implications and lessons of a misstep.

 

In essence, encouraging reflective thinking involves creating a supportive atmosphere where individuals can pause and analyze their actions and outcomes. By integrating regular reflection periods into routine activities, leaders can instill a habit of consideration, fostering a deeper understanding of experiences and their influence on future decisions. This continual loop of action and reflection leads to more thoughtful and effective strategies.

8. System Approaches to Develop Wisdom: Cultivating wisdom necessitates a harmonious balance between rapid intuition and measured thinking. Leaders must master the interplay of both cognitive faculties to excel.

 

Intricate decision-making hinges not only on raw knowledge but also on using that knowledge wisely; System 1 and System 2 play crucial roles here. Quick, automatic, and often subconscious processes (System 1) coexist with the slow, effortful, and conscious thought processes (System 2), together informing enlightened leadership actions.

 

Leaders can enhance their wisdom by consciously transitioning between System 1 and System 2 thinking. Recognizing when to trust gut feelings and when to deliberate carefully over decisions is an art honed through mindful practice and self-awareness.

Examples: Balancing System 1 and System 2 Thinking from Manufacturing and Service Industry

 

Production Line Management

System 1 Thinking:

• Example: A production line supervisor notices a sudden malfunction in one of the machines and immediately decides to switch to a backup machine to keep the production line running.
• Quick Action: “Machine 4 is down. Let’s switch to Machine 6 to avoid downtime.”

System 2 Thinking:

• Example: The operations manager analyzes machine performance data over the past year to identify patterns of malfunction and plans a maintenance schedule to prevent future breakdowns.
• Strategic Maintenance: “Based on our data, we need to schedule regular maintenance checks every three months to minimize machine downtime.”

Customer Service Management

System 1 Thinking:

• Example: A customer service representative quickly addresses a customer's complaint about a billing error. They use their experience and intuition to provide an immediate solution and keep the customer satisfied.
• Quick Response: “I understand the issue. Let me correct that billing error for you right away.”

System 2 Thinking:

• Example: The customer service manager reviews customer complaint data over a quarter to identify recurring issues and develop long-term improvements to prevent similar problems in the future.
• Detailed Analysis: “We’ve noticed an increase in billing errors in the last quarter. Let’s analyze our billing processes and implement more stringent checks.”

Aviation Industry: Emergency Landing

System 1 Thinking:

• Example: A pilot experiences an engine failure shortly after takeoff. They rely on their training and intuition to quickly execute emergency procedures and choose a safe place for an emergency landing.
• Quick Response: “Engine failure. Initiate emergency landing procedures. Identify the nearest suitable landing site.”

 

System 2 Thinking:

• Example: The investigation team analyzes flight data, maintenance records, and environmental factors to determine the root cause of the engine failure and implement long-term solutions to prevent future occurrences.
• Detailed Analysis: “We need a comprehensive review of engine performance and maintenance logs to identify the cause of the failure and implement preventive measures.”

Conclusion

Balancing System 1 and System 2 thinking is a critical skill for effective decision-making and leadership across various industries. Daniel Kahneman's "Thinking, Fast and Slow" model provides a valuable framework for understanding these two distinct modes of thinking: fast, intuitive System 1 and slow, analytical System 2. By leveraging the strengths of both systems, leaders can make quick, informed decisions in high-pressure situations while also engaging in thorough analysis for long-term strategic planning.

Black Box Paradox in Artificial Intelligence (AI)

The Black Box Paradox in AI

One of the early promises of artificial intelligence was that it could deliver the decision making free of discrimination. But the AI has impacted human lives in many aspects and very soon the humans started realizing that artificial intelligence can also suffer from the same biases as the human intelligence.

A few years ago, Amazon mostly abandoned a system it was using to screen the job applicants when it discovered it was consistently favoring men over women. Similarly, in 2019, an ostensibly race-neutral algorithm widely used hospitals and insurance companies was shown to be preferencing white people over black people for certain types of care.

When everyone is hyping that AI provides solutions to every problem, but most of these AI models operate in Black Box i.e. internal workings are a mystery to its users. Users can see the system's inputs and outputs, but they can't see what happens with in AI tool to produce those outputs and this is known as the Black Box Paradox in AI.

The Black Box Paradox refers to inherent opacity of the AI systems, where the decision-making processes are often obscure and difficult to comprehend for humans. This lack of explainability makes it challenging to understand how AI arrives at its conclusions leading to question about transparency, and reliability of the system.

Consider a Black Box model that evaluates job candidates resumes. Users can see the inputs-the resumes they feed into the AI model. And users can see the outputs-the assessments the model returns for the resumes. But users don't know exactly how the model arrives at its conclusions like what factors it considers, how it weighs those factors and so on.

Algorithm of YouTube, Facebook, Instagram can't explain why a particular video gets viral immediately after its upload. This is hidden under many layers of training of the algorithmic model of the YouTube, Facebook, and Instagram.

The Root Cause: Deep Learning

Understanding why this happens requires knowing little bit about how machine learning models are built. 

Suppose you want to teach a child the difference between a Cat and a Dog. You would probably start by showing him a bunch of pictures of both Cats and Dogs, and during that process, the child would absorb some features of Cats 😺 and Dogs 🐕. Then, hopefully, when you show him a picture he never seen before, he can figure out if it's a Cat or Dog.

This method of learning by examples reveals one of the significant ways in which bias can infiltrate a machine learning model. For instance, a facial recognition algorithm is trained mostly on the images of the lighter skinned people, it may lack accuracy in identifying darker skinned individuals.

In much the same way, in real life, people are biased toward the fair skinned people considering them as more beautiful and smarter than dark skinned people because culturally people have deep learning of this thought.

Similarly, Amazon's resume screening model proved to be biased toward men because it was trained to recognize keywords from resumes of its most successful current employees - who were disproportionately men.

The deep learning algorithms are a type of machine learning algorithm that uses multilayered neural networks. Where a traditional machine learning model might use a network of one or two layers, deep learning models can have hundreds or even thousands of layers. Each layer contains multiple neurons, which are bundles of code designed to mimic the functions of the brain.

Deep neural networks can consume and analyze raw, unstructured big data sets with little human intervention. They can take in massive amounts of data, identify patterns, learn from these patterns, and use what they learn to generate new outputs, such as images, video and text.

However, these deep neural networks are inherently opaque. Users-including AI developers -can see what happens at input and output layers, also called "visible layers." They can see the data that goes in and predictions, classifications, or other content that comes out. But they do not know what happens at all network layers in between, the so-called "hidden layers."

Explainable AI (XAI) or White Box AI: Leveraging AI models while ensuring accountability and transparency.

Explainable AI (XAI) or White Box AI is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms.

It is an emerging field that aims to make AI systems more transparent and understandable to humans. It provides the tools and techniques to explain the reasoning behind AI decisions, allowing auditors, analysts, and stakeholders to trace how these decisions are made.

By incorporating XAI, financial institutions can identify and mitigate biases, ensure compliance with regulations, build trust with customers and regulators, and unlock the full potential of AI technology.

It is crucial for an organization to have a full understanding of an AI decision-making processes with model monitoring and accountability of AI and not to trust them blindly. Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep learning and neural networks.

Explainable AI Techniques

1. Prediction Accuracy: Accuracy is a key component of how successful the use of AI is in everyday operations. By running simulations and comparing XAI output to the results in the training data set, the prediction accuracy can be determined. The most popular technique used for this is local interpretable Model-Agnostic Explanations (LIME), which explains the prediction of classifiers by the ML algorithm.
2. Traceability: Traceability is another key technique for accomplishing XAI. This achieved, for example, by limiting the way decisions can be made and setting up a narrower scope for the ML rules and features. An example of traceability XAI technique is DeepLIFT (Deep Learning Important FeaTures), which compares the activation of each neuron to its reference neuron and shows a traceable link between each activated neuron and even shows dependencies between them.
3. Decision Understanding: This is the human factor. Many people have a distrust in AI, yet work with it efficiently, they need to learn to trust it. This is accomplished by educating the team working with the AI so they can understand how and why the AI makes decisions.

Uses of Explainable AI:

• Healthcare: Accelerate diagnostic, image analysis, resource optimization and medical diagnosis. Improve transparency and traceability in decision-making for patient care. Streamline the pharmaceutical approval process with explainable AI.
• Financial Services: Improve customer experiences with a transparent loan and credit approval process. Speed credit risk, wealth management and financial crime risk assessments. Accelerate resolution of potential complaints and issues. Increase confidence in pricing, product recommendations and investment services.
• Criminal Justice: Optimize processes for prediction and risk assessment. Accelerate resolution using explainable AI on DNA analysis, prison population analysis and crime forecasting. Detect potential biases in training data and algorithms.

In conclusion, without knowing how an AI model making decisions leads to the lack of transparency and accountability in the decision making known as Black Box Paradox in AI. This generates confusion, and distrust in AI models. The problem can be addressed with the use of Explainable AI (XAI) which gives the detailed understanding of the process of decision making and leading to the better use of AI models while ensuring accountability and transparency.