The MCC released the revised brochure for NEET PG 2024 counselling at the official website, mcc.nic.in, on Wednesday. Details of Armed Forces Medical Services counselling and eligibility conditions have been added to the Information Bulletin of NEET PG Counseling 2024.

MCC released the counselling schedule for NEET PG on 1st November after a considerable delay. Last date for application in NEET PG Round I counselling is on 17th November. Meanwhile, on 19th November, petitions are likely to be placed in the Supreme Court challenging National Board of Examinations in Medical Sciences or NBEMS.

According to the June 26, 2024 letter, “It has been informed by the office of DGAFMS that from this year, the counselling for Priority III, IV and V candidates into AFMS PG Teaching Institutes will be conducted by MCC of DGHS.”

All candidates registering under priority 5 will provisionally be allotted seats strictly on merit-cum-choice and seat availability basis. They will also undergo a medical examination to be carried out at their provisionally allotted AFMS institute. Admission will be given only for candidates passed as fit.

MCC has also made some important information available in relation to cancellation of candidature and certificates for offering admissions through the counselling process.

According to the AFMS counseling 2024 policy, the list of eligibility priority 3 and 4 candidates will be given to MCC by DGAFMS after scanning the applications.

NEET PG 2024: AFMS counseling

The main AFMS PG Teaching Institutes are entrusted with the task of training in-service medical officers from the Armed Forces Medical Services to qualify for a specialist. The civil doctors desirous of joining MD, MS, or DNB in any AFMS institute are liable to be commissioned SSC to serve for 5 years in AFMS after qualifying the respective PG exams also, according to the existing norm.

The remaining seats in these institutes will be opened for eligible candidates after fulfilling the AFMS requirements through the following priority system.

• Priority 1: AFMS officers detailed on Advance Specialist Course or PG courses.
• Priority 2: Foreign students sponsored by Government and AFMS officers granted Study Leave.
• Priority 3: Medical Officers sponsored by Paramilitary Organization and other Government of India organizations.
• Priority 4: SSC AFMS Ex-officers within 3 years of their release from service.
• Priority 5: Civil candidates.

Conclusion

With the NEET PG 2024 counselling process now underway, candidates are advised to review the revised guidelines and meet all necessary deadlines to secure their admission. The updated policies for AFMS counseling, which detail priority-based eligibility for in-service Armed Forces medical officers and civil candidates, ensure a smooth and structured approach to seat allocation. Staying informed about each step and following official announcements from MCC will help candidates navigate this critical phase in their medical career paths.

The study has called for participatory approaches in the development and governance of Artificial Intelligence in India and abroad.

This study sought to establish the need and importance of a participatory approach to AI Governance while grounding it in real-world use cases, through an interdisciplinary collaboration, say sources from IIT Madras.

This model highlights the importance of involving relevant stakeholders in shaping the design, implementation, and oversight of AI systems, say sources from IIT Madras.

Researchers

Researchers from the Centre for Responsible AI (CeRAI) under Wadhwani School of Data Science and AI at IIT Madras and Vidhi Legal, a leading think-tank on legal and tech policy, between technologists, lawyers and policy researchers conducted this study in two parts.

Findings

Their findings were published in a Pre-Print Paper in ‘arXiv’, an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, and computer science, among many others. The Papers can be viewed using the following links – https://arxiv.org/abs/2407.13100 and https://arxiv.org/abs/2407.13103

Need for the study

Prof. B. Ravindran, Head, Wadhwani School of Data Science and Artificial Intelligence (WSAI), IIT Madras highlighted the need for studies.

The widespread adoption of AI technologies in the public and private sectors has resulted in them significantly impacting the lives of people in new and unexpected ways, said Prof Ravindran of WSAI at IIT Madras.

In this context, it becomes important to inquire how their design, development and deployment takes place. This study found that persons who will be impacted by the deployment of these systems have little to no say in how they are developed, he said.

Seeing this as a major gap, this research study advances the premise that a participatory approach is beneficial to building and using more responsible, safe, and human-centric AI systems, he said.

By ensuring that diverse communities are included in AI development, we can create systems that better serve everyone, particularly those who have been historically underrepresented, he said.

Increasing transparency and accountability in AI systems fosters public trust, making it easier for these technologies to gain widespread acceptance. Further, by involving a wide range of stakeholders, we can reduce risks like bias, privacy violations, and lack of explainability, making AI systems safer and more reliable, he said.

Value

Shehnaz Ahmed, Lead, Law and Technology, Vidhi Centre for Legal Policy, said, “Increasingly, there is a recognition of the value of participatory approaches in AI development and governance.

However, the lack of a clear framework for implementing these principles limits their adoption. This report addresses critical challenges by offering a sector-agnostic framework that answers key questions such as how to identify stakeholders, involve them throughout the AI lifecycle, and effectively integrate their feedback, she said.

The findings demonstrate how participatory processes can enhance AI solutions, particularly in areas like facial-recognition technology and healthcare. Embracing a participatory approach is the pathway to making AI truly human-centric, a core aspiration of the IndiaAI mission, she said.

Recommendations

The recommendations for Implementing Participatory AI include:

Ø Adopt a Participatory Approach to AI Governance: Engage stakeholders throughout the entire AI lifecycle—from design to deployment and beyond—to ensure that AI systems are both high-quality and fair.

Ø Establish Clear Mechanisms for Stakeholder Identification: Develop robust processes for identifying relevant stakeholders, guided by criteria like power, legitimacy, urgency, and potential for harm. The “decision sieve” model is a valuable tool in this process.

Ø Develop Effective Methods for Collating and Translating Stakeholder Input: It is crucial to create clear procedures for collecting, analyzing, and turning stakeholder feedback into actionable steps. Techniques like voting and consensus-building can be used but it is important to be aware of their limitations and potential biases.

Ø Address Ethical Considerations Throughout the AI Lifecycle: Involve ethicists and social scientists from the beginning of AI development to ensure that fairness, bias mitigation, and accountability are prioritized at every stage.

Ø Prioritize Human Oversight and Control: Even as AI systems become more advanced, it is essential to keep humans in control, especially in sensitive areas like law enforcement and healthcare.’.

Papers

In this First Paper, the authors investigated various issues that have cropped up in the recent past when it comes to AI governance and explored viable solutions. By analyzing how beneficial a participatory approach has been in other domains, they proposed a framework that integrates these aspects, say sources from IIT Madras.

The Second Paper analysed two use cases of AI solutions and their governance, with one of them being a largely deployed solution in Facial Recognition Technologies which has been widely discussed and well documented, while the other is a possible future application of a relatively newer AI solution in a critical domain, say sources from IIT Madras.

The lack of transparency in how these technologies are deployed raises serious privacy concerns and risks of misuse by law enforcement.

Engaging stakeholders like civil society groups, undertrials, and legal experts can help ensure that FRT systems are deployed in ways that are fair, transparent, and respectful of individual rights, say sources from IIT Madras.

Large Language Models (LLMs) in Healthcare: In healthcare, the stakes are even higher. LLMs can sometimes generate inaccurate or fabricated information, posing significant risks when used in medical decision-making.

Furthermore, if LLMs are trained on biased data, they could exacerbate healthcare disparities. The opacity of these models’ decision-making processes further complicates matters, making it difficult to trust their outputs.

Involving doctors, patients, legal teams, and developers in the development and deployment of LLMs can lead to systems that are not only more accurate but also more equitable and transparent, say sources from IIT Madras.

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