This can be done in the following ways:

1. Interpretable Models: Use AI models that are intrinsically interpretable, such as decision trees or linear models, to help stakeholders understand how decisions are produced using clear rules and features.
2. Feature Importance: Provide insights into the importance of features in AI models to emphasize which elements contribute the most significantly to predictions or choices, hence improving transparency.
3. Explainable AI Techniques: Use techniques such as LIME (Local Interpretable Model agnostic Explanations) or SHAP (SHapley Additive Explanations) to create explanations for specific predictions, hence increasing transparency while maintaining accuracy.
4. Documentation and reporting: Keep detailed records of AI system development, training data, and performance metrics to ensure transparency in implementation and consequences.
5. Ethical and Regulatory Compliance: Follow ethical norms and regulatory criteria for AI deployment, ensuring that AI system judgments are lawful and ethical, and can be justified to stakeholders.
6. Continuous Monitoring and Stakeholder Engagement: Implement tools for continuing monitoring and auditing of AI systems to detect biases, errors, or performance degradation, ensuring transparency and accuracy throughout time. Involve stakeholders in the creation and testing of AI systems, such as end users, domain experts, and impacted communities, to receive feedback and ensure that their requirements and expectations are met.

Human Teachers are better in a variety of ways: 

Empathy and Emotional Support: Human teachers can empathize with students, understand their emotions, and provide individualized emotional support, which is critical for student motivation and wellbeing.

Adaptability and Flexibility: Teachers can change their teaching approaches in real time based on student feedback and needs, providing personalized learning experiences that AI systems may fail to reproduce.

Social and Emotional Learning: They help students build social interactions, teamwork, and communication skills, all of which are necessary for future interpersonal connections and professional success.

Critical Thinking and Creativity: Teachers promote critical thinking, creativity, and problem solving abilities through interesting discussions, projects, and activities that go beyond rote learning.

Role Modeling and Mentorship: They act as role models and mentors, passing on not only academic information but also life skills, values, and ethical behavior by their own example and guidance.

Cultural and Contextual Understanding: Teachers contribute cultural awareness and contextual understanding to their lessons, connecting curricular content to real world applications that AI may miss.

Ethical and Moral Education: Teachers traverse complicated ethical and moral quandaries with students, fostering deliberate contemplation and responsible decision making in ways that AI cannot.

Several significant factors and tactics must be taken into account:

Fair Resources and Funding:

1. We have to make sure schools in underprivileged communities have enough resources by allocating funds according to the requirements of the students.
2. Give students access to learning resources, textbooks, and technology that accommodate a range of learning styles and aptitudes.
3. Establish procedures that give priority to allocating resources to schools where the percentage of disadvantaged students is higher.

Assistance with Disabilities and Special Needs:

1. Make sure schools have the resources, facilities, tools, and personnel, needed to assist students with special needs or impairments.
2. Create individualized education plans and make sure assistive technology and accommodations are available.

Curriculum that is Inclusive and Diverse:

1. Provide language, arts, and humanities courses that celebrate cultural diversity and foster understanding.
2. Offer pupils opportunity to study about social justice, equity, and human rights problems.

Teacher Training and Professional Development:

1. Provide continual teacher training in inclusive teaching approaches and solutions for diverse classrooms.

Accessible Physical and Digital Learning Environments:

1. Ensure that all school buildings and facilities are accessible to students with physical disabilities, including ramps, elevators, and accessible restrooms along with digital learning platforms for everyone.

Feature Scaling is important in Machine Learning due to the following reasons:

Equal Feature Influence: Scaling guarantees that all features contribute equally to model training by putting them into a comparable numeric range. This avoids models from being biased toward bigger scale features, which would otherwise dominate the learning process.

Enhanced Algorithm Performance: Scaling features improves the performance of several machine learning algorithms, such as SVMs, KNN, and neural networks. It increases convergence rates in optimization processes such as gradient descent, resulting in faster and more reliable model training.

Accurate Distance Calculations: To compute distances accurately, algorithms that use distance measures, such as KNN and clustering, require scaled features. Unscaled features with greater ranges may confuse distance computations and impact clustering or classification results.

Stable Gradient Descent: Gradient-based optimization methods perform better on scaled data. Uniformly scaled features aid gradient descent algorithms in navigating to the ideal solution without oscillating or taking wasted steps.

Effective Regularization: To avoid overfitting, techniques like L1 and L2 regularization penalize large coefficients. Scaling guarantees that regularization penalties are paid evenly to all features.