SANSAD TV & AIR SUMMARY JUNE (WEEK 2)

AIR Summary and Sansad TV

June 2nd Week 

Artificial Intelligence and its Significance

In news

A report by the Reuters Institute for the Study of Journalism found that global concerns about the use of AI in news production and misinformation are increasing.

Disclaimer: Copyright infringement not intended.

Artifical Intelligence: AI is defined as the capability of machines and systems to acquire and apply knowledge and to carry out intelligent behaviour This includes a wide range of cognitive abilities such as reasoning, common sense, abstract thinking, background knowledge, transfer learning, and the ability to differentiate between cause and effect.

Types of Artificial Intelligence

Artificial Intelligence (AI) can be categorized based on its capabilities and functionalities. Here are the main types:

1. Narrow AI (Weak AI): Narrow AI refers to systems designed to handle specific tasks.

Examples:

• Siri and Alexa: Voice assistants performing tasks like setting reminders.
• Recommendation Systems: Algorithms suggesting products on Netflix or Amazon.

2. General AI (Strong AI): General AI can understand and learn across a wide range of tasks, similar to human abilities. It is theoretical and not yet realized.

Examples:

• Hypothetical AI: A system capable of performing any intellectual task a human can do.

3. Superintelligent AI : Super intelligent AI surpasses human intelligence in all aspects. This is also theoretical and represents a future possibility.

Examples:

• Speculative AI: AI outperforming human intelligence in every domain.

Functional Types of AI

AI can also be classified based on its functionalities:

1. Reactive Machines: Reactive machines can perceive and react to specific situations without memory or learning.

Examples:

• Deep Blue: IBM's chess-playing computer.

2. Limited Memory: Limited memory AI retains past experiences or data for short-term use.

Examples:

• Self-Driving Cars: Use sensors and past data to navigate.

3. Theory of Mind: Theory of mind AI understands emotions and intentions. This is still in research.

Examples:

• Emotionally Intelligent Robots: Hypothetical robots understanding human emotions.

4. Self-Aware AI: Self-aware AI possesses self-awareness and consciousness. This type is purely theoretical.

Examples:

• Conscious Machines: Speculative AI with human-like consciousness.

AI Tools in Various Fields

AI tools are widely used across different industries to enhance efficiency, decision-making, and innovation. Here are some key AI tools in various fields:

1. Healthcare

• IBM Watson Health: Analyzes medical data to provide insights and support decision-making.
• PathAI: Uses AI to improve the accuracy of pathology diagnoses.
• Aidoc: AI-powered radiology tools for identifying anomalies in medical images.

2. Finance

• Alpaca: An AI-driven trading platform that offers commission-free trading with advanced algorithms.
• Kensho: Provides analytics and data visualization tools for financial services.
• ZestFinance: Uses machine learning to improve credit scoring and lending decisions.

3. Retail

• Amazon Rekognition: Image and video analysis to improve product recommendations and customer experience.
• Sentient Technologies: Uses AI for personalized online shopping experiences and optimizing pricing strategies.
• Shopify Kit: An AI-powered virtual assistant for managing marketing tasks and customer interactions.

4. Manufacturing

• Siemens MindSphere: An IoT operating system using AI to optimize manufacturing processes.
• Veo Robotics: AI-powered robotics systems for flexible and efficient manufacturing automation.
• SparkCognition: AI solutions for predictive maintenance and industrial safety.

5. Education

• Coursera: Uses AI to personalize learning experiences and recommend courses.
• Carnegie Learning: AI-driven platforms for personalized and adaptive learning in K-12 education.
• Gradescope: AI-assisted grading and feedback tools for educators.

6. Transportation

• Waymo: Autonomous driving technology leveraging AI for safe navigation.
• Uber AI Labs: Develops AI algorithms for ride-sharing optimization and predictive modeling.
• Optibus: AI-powered platform for optimizing public transportation schedules and routes.

7. Entertainment

• Netflix: Uses AI algorithms for content recommendation and personalized viewing experiences.
• Aiva: An AI composer that creates original music for various media.
• Replika: An AI chatbot providing companionship and entertainment through conversation.

8. Customer Service

• Zendesk: AI-driven customer support tools for improving response times and service quality.
• LivePerson: Conversational AI for enhancing customer interactions and support.
• Clara: AI-powered virtual assistant for scheduling meetings and managing communications.

9. Marketing

• HubSpot: AI tools for automating and optimizing marketing campaigns.
• Marketo: Uses AI for lead scoring, email marketing, and customer segmentation.
• Crimson Hexagon: AI-driven social media analytics and sentiment analysis tools.

10. Agriculture

• John Deere: AI and machine learning for precision agriculture and equipment optimization.
• Prospera: AI-powered solutions for monitoring crop health and optimizing yields.
• Blue River Technology: Uses AI for smart farming and weed control.

AI Initiatives in India

India has been actively working on leveraging AI across various sectors to drive innovation, economic growth, and social development. Here are some key AI initiatives by India along with their dimensions:

1. National AI Strategy

• Initiative: NITI Aayog's National Strategy for Artificial Intelligence
• Dimension: Policy and Framework
  • Focuses on positioning India as a global leader in AI.
  • Promotes AI research, innovation, and the establishment of AI centers of excellence.
  • Emphasizes ethical AI development and deployment.

2. Digital India Programme

• Initiative: Digital India
• Dimension: Digital Infrastructure and Services
  • Aims to transform India into a digitally empowered society.
  • Includes AI-driven projects to enhance digital governance and public services.
  • Focuses on improving digital literacy and connectivity.

3. AI for All

• Initiative: AI for All (A collaboration with Intel)
• Dimension: Education and Awareness
  • Promotes AI literacy among students, developers, and the general public.
  • Provides resources and training to integrate AI into school curriculums.
  • Encourages inclusive AI education, especially in rural areas.

4. Responsible AI for Youth

• Initiative: Responsible AI for Youth
• Dimension: Education and Skill Development
  • Launched by the Ministry of Electronics and IT (MeitY).
  • Aims to empower youth with AI skills through training programs and workshops.
  • Targets students from grades 8 to 12, fostering early interest in AI technologies.

5. AI Research and Development

• Initiative: Centers of Excellence (CoEs) in AI
• Dimension: Research and Innovation
  • Establishment of AI research centers in collaboration with academic institutions.
  • Focuses on advancing AI research in areas like healthcare, agriculture, and smart cities.
  • Encourages public-private partnerships to drive AI innovation.

6. AI in Healthcare

• Initiative: National Health Stack (NHS)
• Dimension: Healthcare
  • Uses AI to enhance healthcare services and management.
  • Implements AI-based solutions for disease prediction, diagnostics, and personalized treatment.
  • Aims to improve accessibility and quality of healthcare through digital health initiatives.

7. AI in Agriculture

• Initiative: AI for Agriculture
• Dimension: Agriculture
  • Utilizes AI for precision farming, crop monitoring, and yield prediction.
  • Collaborates with organizations like Microsoft to develop AI tools for farmers.
  • Focuses on improving agricultural productivity and sustainability.

8. AI in Governance

• Initiative: AI for Public Good
• Dimension: Governance and Public Services
  • Deploys AI to enhance public service delivery and governance.
  • Implements AI solutions for smart cities, traffic management, and law enforcement.
  • Aims to improve transparency, efficiency, and citizen engagement.

9. AI for Social Empowerment

• Initiative: AI for Social Empowerment Summit (RAISE 2020)
• Dimension: Social Impact
  • Organizes summits and conferences to discuss the role of AI in social empowerment.
  • Highlights AI applications in education, healthcare, agriculture, and smart mobility.
  • Encourages collaboration among government, industry, and academia for social good.

10. AI for Financial Inclusion

• Initiative: AI in Fintech
• Dimension: Financial Services
  • Uses AI to enhance financial services and promote financial inclusion.
  • Implements AI-driven credit scoring, fraud detection, and customer service solutions.
  • Aims to make financial services more accessible and efficient.

11. AI ecosystem

• Initiative: IndiaAI Mission
• Dimension: Policy and Framework

• Focuses on positioning India as a global leader in AI.
• Promotes AI research, innovation, and the establishment of AI centers of excellence.
• Emphasizes ethical AI development and deployment

Issues with Artificial Intelligence

Despite its many benefits, Artificial Intelligence (AI) also presents several significant challenges and concerns. Here are some of the primary issues associated with AI, along with examples:

1. Bias and Discrimination

AI systems can perpetuate and amplify existing biases present in the training data, leading to unfair and discriminatory outcomes.

Examples:

• Facial Recognition: AI systems, such as those used by law enforcement, have been shown to have higher error rates for people of color, leading to wrongful identifications.
• Hiring Algorithms: AI tools used for screening job applicants, like Amazon's now-abandoned recruiting tool, were found to be biased against women.

2. Privacy Concerns

AI systems often require vast amounts of data, raising significant privacy issues related to data collection, storage, and usage.

Examples:

• Social Media Platforms: AI algorithms on platforms like Facebook analyze user behavior and personal data to target ads, raising concerns about user privacy.
• Surveillance Systems: AI-driven surveillance systems can infringe on personal privacy by constantly monitoring and analyzing individual behaviors in public and private spaces.

3. Security Risks

AI can be both a target and a tool for malicious activities, posing new security challenges.

Examples:

• Deepfakes: AI-generated fake videos or audio recordings that can be used to spread misinformation or for identity theft.
• Cyber attacks: AI can be used to launch sophisticated cyber attacks, such as automated phishing or malware that adapts to defenses.

4. Job Displacement

The automation of tasks through AI can lead to job losses, particularly in industries reliant on repetitive or routine tasks.

Examples:

• Manufacturing: Automation and robotics, such as those used by car manufacturers, have reduced the need for manual labor on assembly lines.
• Customer Service: AI-powered chatbots and virtual assistants are replacing human customer service representatives in many companies.

5. Ethical Dilemmas

AI raises complex ethical questions about the extent to which machines should make decisions that affect human lives.

Examples:

• Autonomous Vehicles: Self-driving cars must be programmed to make ethical decisions in situations where accidents are unavoidable, posing questions about how to prioritize lives.
• Healthcare AI: AI systems making decisions about patient care and treatment must balance efficiency with ethical considerations, such as patient consent and autonomy.

6. Transparency and Accountability

AI systems often operate as "black boxes," making it difficult to understand how decisions are made, which complicates accountability.

Examples:

• Algorithmic Trading: Financial markets use AI algorithms that make split-second trading decisions, which can lead to significant financial losses without clear accountability.
• Credit Scoring: AI systems used for determining creditworthiness can be opaque, making it hard for individuals to understand or challenge their credit scores.

7. Dependence and Over-Reliance

Society's increasing dependence on AI systems can lead to vulnerabilities if these systems fail or are compromised.

Examples:

• Smart Home Devices: Over-reliance on AI-powered home automation can cause significant disruption if the system fails or is hacked.
• Navigation Systems: Dependence on AI-based GPS navigation can lead to problems if the system gives incorrect directions or fails to account for current conditions.

8. Environmental Impact

AI technologies, particularly those requiring significant computational power, can have a substantial environmental footprint.

Examples:

• Data Centers: AI systems, especially those used for training large models, require vast amounts of energy, contributing to carbon emissions.
• Cryptocurrency Mining: While not traditional AI, the computational processes involved in mining can be linked to AI and have significant environmental impacts due to high energy consumption

Wayforward:

Addressing the challenges posed by AI requires a combination of regulatory, technical, and ethical approaches. Here are some potential solutions along with examples:

1. Mitigating Bias and Discrimination

Efforts to reduce bias in AI systems involve diverse data collection, bias detection and correction algorithms, and inclusive AI development practices.

Examples:

• Fairness Indicators: Google has developed tools like Fairness Indicators to help developers detect and mitigate bias in their machine learning models.
• Diverse Data Sets: IBM’s AI Fairness 360 is an open-source toolkit designed to examine, report, and mitigate discrimination and bias in machine learning models.

2. Enhancing Privacy Protections

Strengthening data privacy involves implementing robust data protection regulations, developing privacy-preserving technologies, and promoting transparency in data usage.

Examples:

• GDPR Compliance: The General Data Protection Regulation (GDPR) in the European Union sets strict guidelines for data protection and privacy, encouraging companies to develop AI systems that comply with these standards.
• Differential Privacy: Apple and Google use differential privacy techniques to collect data while preserving user anonymity, reducing the risk of privacy breaches.

3. Improving Security Measures

Enhancing AI security involves developing advanced security protocols, continuous monitoring, and implementing AI to detect and counteract threats.

Examples:

• AI for Cybersecurity: Darktrace uses AI to detect and respond to cyber threats in real-time, providing advanced threat detection and mitigation.
• Secure AI Development: Microsoft’s Security Development Lifecycle (SDL) integrates security best practices and requirements into every phase of AI development to prevent vulnerabilities.

4. Addressing Job Displacement

Mitigating job displacement involves investing in education and retraining programs, encouraging the development of new job categories, and promoting human-AI collaboration.

Examples:

• Reskilling Programs: Amazon's Career Choice program offers training in high-demand fields such as healthcare, IT, and machine learning to help employees transition to new roles.
• Collaborative Robotics: Companies like Universal Robots develop collaborative robots (cobots) designed to work alongside humans, augmenting rather than replacing their roles.

5. Navigating Ethical Dilemmas

Developing ethical AI involves establishing clear guidelines, ensuring human oversight, and incorporating ethical decision-making frameworks into AI design.

Examples:

• Ethical AI Frameworks: The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems provides guidelines and standards to ensure the ethical development and deployment of AI.
• Human-in-the-Loop: In healthcare, AI systems like IBM Watson for Oncology provide recommendations that require final approval from a human doctor, ensuring ethical oversight.

6. Ensuring Transparency and Accountability

Promoting transparency and accountability involves creating explainable AI systems, establishing clear documentation, and implementing oversight mechanisms.

Examples:

• Explainable AI (XAI): DARPA’s Explainable AI program aims to create AI systems whose actions can be understood and trusted by human users, enhancing transparency.
• Algorithmic Accountability: The AI Now Institute advocates for algorithmic accountability, including public disclosure of AI use cases and impact assessments to ensure responsible deployment.

7. Reducing Over-Reliance

Reducing dependence on AI involves building robust fallback systems, promoting user awareness, and ensuring human oversight.

Examples:

• Backup Systems: Autonomous vehicle companies like Waymo develop extensive backup systems and safety protocols to ensure human control can be regained if needed.
• User Training: Organizations using AI-driven tools like CRM systems provide comprehensive training to ensure users can operate effectively without complete reliance on AI.

8. Minimizing Environmental Impact

Reducing the environmental impact of AI involves developing energy-efficient algorithms, utilizing renewable energy sources, and optimizing hardware.

Examples:

• Energy-Efficient AI: Companies like Google and DeepMind focus on developing AI models that require less computational power and energy.
• Green Data Centers: Microsoft has committed to using 100% renewable energy in its data centers by 2025, reducing the carbon footprint of its AI operations.

Citations:

https://www.nytimes.com/2024/06/17/business/ai-drugs-development-terray.html https://www.reuters.com/technology/artificial-intelligence/global-audiences-suspicious-ai-powered-newsrooms-report-finds-2024-06-16/ https://pib.gov.in/PressReleasePage.aspx?PRID=2012375 https://pib.gov.in/PressReleaseIframePage.aspx?PRID=2002657#:~:text=The%20Government%20considers%20Artificial%20Intelligence,the%20youth%20of%20the%20country.

Expansion of Pradhan Mantri Awas Yojana Scheme

In news

In June 2024, after the successful sanction of 4.21 crore houses since the launch of the Pradhan Mantri Awas Yojana (PMAY) scheme, it has been decided by the Cabinet to assist 3 crore additional rural and urban households for the construction of houses

Disclaimer: Copyright infringement not intended.

Pradhan Mantri Awas Yojana:

1. Introduction

• Launched: 2015
• Aim: To provide affordable housing for all by 2022

2. Objectives

• Ensure "Housing for All": Focuses on providing affordable housing to every family.
• Promote Urbanization: Enhances urban development with improved civic amenities.
• Encourage Sustainable and Inclusive Housing: Supports the development of housing that is environmentally sustainable and inclusive of all socio-economic groups.

3. Components

A)Pradhan Mantri Awas Yojana - Urban (PMAY-U):

1. Objectives

• Affordable Housing: Ensure every urban resident, particularly the economically weaker sections and low-income groups, has access to affordable housing.
• Urban Development: Improve urban living conditions with enhanced civic amenities and infrastructure.
• Inclusivity and Sustainability: Promote sustainable and inclusive housing developments that cater to diverse population segments.

2. Components

• In-situ Rehabilitation of Slum Dwellers
  • Objective: To transform slums into livable habitats by providing proper housing in the same location or nearby areas.
  • Mechanism: Utilizes existing land as a resource, ensuring minimal displacement and disruption to the lives of slum dwellers.
• Credit-Linked Subsidy Scheme (CLSS)
  • Objective: To make home loans affordable for economically weaker sections (EWS), lower-income groups (LIG), and middle-income groups (MIG).
  • Mechanism: Offers interest subsidies on home loans:
    • EWS/LIG: Interest subsidy of 6.5% for loan amounts up to Rs. 6 lakh.
    • MIG-I: Interest subsidy of 4% for loan amounts up to Rs. 9 lakh.
    • MIG-II: Interest subsidy of 3% for loan amounts up to Rs. 12 lakh.

• Affordable Housing in Partnership (AHP)
  • Objective: To increase the availability of affordable housing through collaboration with private sector developers.
  • Mechanism: Offers financial assistance to private developers to construct affordable housing projects, ensuring a certain percentage of houses are earmarked for EWS/LIG beneficiaries.
• Subsidy for Beneficiary-led Individual House Construction or Enhancement
  • Objective: To support individuals in constructing or enhancing their own houses.
  • Mechanism: Provides direct financial assistance to eligible beneficiaries, allowing them to build new houses or improve existing structures.

Disclaimer: Copyright infringement not intended.

3. Eligibility Criteria

• Economically Weaker Section (EWS)
  • Annual household income up to Rs. 3 lakh.
  • Eligible for all components, including CLSS, AHP, and beneficiary-led house construction.
• Lower Income Group (LIG)
  • Annual household income between Rs. 3 lakh and Rs. 6 lakh.
  • Eligible for CLSS and AHP components.
• Middle Income Group (MIG) I
  • Annual household income between Rs. 6 lakh and Rs. 12 lakh.
  • Eligible for CLSS with interest subsidy.
• Middle Income Group (MIG) II
  • Annual household income between Rs. 12 lakh and Rs. 18 lakh.
  • Eligible for CLSS with interest subsidy.

4. Key Features

• Interest Subsidy
  • CLSS: Provides significant interest rate reductions on home loans, making housing finance more accessible and affordable for the targeted groups.
• Public-Private Partnerships
  • AHP: Engages private developers in the construction of affordable housing, leveraging their expertise and resources to increase housing stock.
• Modern Construction Technologies
  • Encourages the use of innovative and sustainable construction technologies to reduce costs, improve quality, and speed up construction.
• Slum Rehabilitation
  • Focuses on utilizing existing land resources to provide new housing for slum dwellers, ensuring minimal displacement and disruption to their lives.

5. Implementation Mechanism

• Central Government
  • Policy Framework and Financial Support: Provides guidelines, sets policy direction, and allocates financial resources to support the scheme.
  • Monitoring and Evaluation: Oversees the implementation process to ensure objectives are met and funds are utilized effectively.
• State Governments and Urban Local Bodies (ULBs)
  • Implementation and Monitoring: Responsible for the on-ground execution of the scheme, including beneficiary selection, project approval, and monitoring of construction activities.
  • Customization and Adaptation: Adapt central guidelines to local contexts, ensuring relevance and effectiveness of the scheme in diverse urban settings.
• Nodal Agencies
  • National Housing Bank (NHB) and Housing and Urban Development Corporation (HUDCO)
    • Facilitate CLSS: Ensure the smooth disbursement of interest subsidies under the CLSS, supporting financial institutions and beneficiaries.
    • Capacity Building: Provide training and support to state agencies and ULBs, enhancing their capacity to implement the scheme effectively.

6. Achievements

• Housing Units Sanctioned and Constructed
  • Millions of houses sanctioned under PMAY-U, with substantial progress in construction and delivery.
  • Significant improvement in living conditions for urban poor and slum dwellers.
• Impact on Urban Development
  • Enhanced infrastructure and civic amenities in urban areas.
  • Promotion of inclusive growth and reduction of urban poverty.

B) Pradhan Mantri Awas Yojana - Gramin (PMAY-G)

1. Introduction

• Launched: 2016 (earlier known as Indira Awaas Yojana)
• Aim: To provide affordable and quality housing for all rural residents by 2022

2. Objectives

• Housing for All: Ensure every rural household has access to a pucca house with basic amenities.
• Poverty Alleviation: Improve living standards and reduce poverty by providing secure housing.
• Inclusive Growth: Promote social equity by targeting economically weaker sections in rural areas.

3. Components

• Construction of Pucca Houses
  • Objective: Replace kutcha (temporary) houses with durable pucca (permanent) houses.
  • Mechanism: Financial assistance provided to beneficiaries for constructing a new house or upgrading an existing one to a pucca house.
• Provision of Basic Amenities
  • Objective: Ensure that every house constructed has essential facilities like a toilet, LPG connection, electricity connection, and clean drinking water.
  • Mechanism: Convergence with other government schemes like Swachh Bharat Mission (SBM), Pradhan Mantri Ujjwala Yojana (PMUY), and Saubhagya scheme for comprehensive development.

4. Eligibility Criteria

• Identification of Beneficiaries
  • Criteria: Beneficiaries are selected based on the Socio-Economic and Caste Census (SECC) 2011 data.
  • Target Groups: Homeless families, families living in kutcha or dilapidated houses, and households with no adult member aged between 16 and 59 years, among others.

5. Key Features

• Financial Assistance
  • Grant Amount: Financial assistance of 1.20 lakh per unit in plain areas and Rs. 1.30 lakh per unit in hilly states, difficult areas, and Integrated Action Plan (IAP) districts.
  • Funding Pattern: The cost is shared between the Central and State Governments in a 60:40 ratio for plain areas and 90:10 for hilly and northeastern states.
• Technical Support
  • House Design: Promotion of region-specific house designs that incorporate local materials and technology, ensuring durability and cultural relevance.
  • Masonry Training: Training for rural masons to ensure quality construction and create local employment opportunities.
• Use of Technology
  • Geo-Tagging: Use of geo-tagging for monitoring the progress of house construction to ensure transparency and accountability.
  • AwaasSoft and AwaasApp: Implementation of an end-to-end e-Governance solution for better management and monitoring of the scheme.

6. Implementation Mechanism

• Central Government
  • Policy and Funding: Provides the overall policy framework, guidelines, and financial support for the scheme.
  • Monitoring and Evaluation: Oversees the implementation process to ensure effective utilization of funds and achievement of targets.
• State Governments and District Authorities
  • Beneficiary Selection and Approval: Responsible for identifying and approving beneficiaries based on SECC data.
  • Fund Disbursement: Ensure timely disbursement of financial assistance to beneficiaries and monitor the construction process.
• Gram Panchayats
  • Local Implementation: Play a crucial role in the local implementation of the scheme, including beneficiary identification, mobilization, and providing on-ground support.

Challenges to Implementation of PMAY

1.Land Availability:

• Acquiring land in urban areas is challenging due to high land prices and limited availability.
• In rural areas, land ownership disputes and unclear land titles can delay construction.

2.Financial Constraints:

• Ensuring adequate and timely allocation of funds is crucial for smooth execution.
• Economically weaker sections may struggle to contribute their share of housing costs.

3.Timely Completion:

• Bureaucratic hurdles, procedural delays, and slow disbursement of funds can hinder timely completion.
• Shortages of construction materials and skilled labor can further slow down the building process.

4.Quality of Construction:

• Ensuring that construction meets quality and safety standards is challenging.
• The lack of technical expertise among local builders and contractors can affect construction quality.

5.Beneficiary Identification and Selection:

• Identifying eligible beneficiaries accurately based on outdated or inaccurate data is difficult.
• Ensuring that all eligible beneficiaries are included while preventing ineligible individuals from benefiting is also a challenge.

6.Infrastructure and Basic Amenities:

• Providing essential services like water, electricity, and sanitation alongside housing construction is crucial.
• Integrating housing projects with broader urban planning initiatives to ensure sustainable development poses additional challenges

Way forward:

1.Streamline Land Acquisition:

• Implement reforms to simplify and expedite land acquisition processes, including resolving ownership disputes and using innovative solutions like land pooling to increase land availability for housing projects.

2.Enhance Financial Support:

• Increase budget allocations and ensure timely disbursement of funds.
• Introduce more flexible financing options and subsidies to assist economically weaker sections in meeting their share of housing costs.

3.Improve Project Management:

• Simplify bureaucratic procedures and enhance coordination between various governmental departments to reduce project delays.
• Utilize project management tools and technologies to monitor progress and address resource shortages effectively.

4.Ensure Construction Quality:

• Implement stringent quality control measures and regular inspections to ensure compliance with construction standards.
• Provide training and capacity-building programs for local builders and contractors to enhance technical expertise.

5.Accurate Beneficiary Identification:

• Regularly update the beneficiary database to reflect current needs and demographics.
• Implement robust verification mechanisms to ensure the accurate selection of beneficiaries, minimizing inclusion and exclusion errors.

6.Integrate Basic Amenities:

• Coordinate with other government schemes to provide essential services like water, electricity, and sanitation alongside housing construction.
• Align housing projects with broader urban and rural development plans to promote sustainable and inclusive growth

Citations:

https://pib.gov.in/PressNoteDetails.aspx?NoteId=151895&ModuleId=3 https://www.iasgyan.in/daily-current-affairs/pm-awas-yojana-32 https://www.iasgyan.in/daily-current-affairs/pradhan-mantri-awas-yojana-pmay-28