Call for Expression of Interest on Responsible AI


The Ministry of Electronics and Information Technology (MeitY) is committed to foster transparency, accountability, and fairness in AI practices. As AI integration grows, India aims to invest in agile mechanisms for indigenous tools and assessment frameworks, contextualized to its socio-economic realities. Under the pillar of ‘Responsible AI’ of National Program on Artificial Intelligence (NPAI), IndiaAI – IBD, under Digital India Corporation, launches an initiative for research project-based funding. IndiaAI – IBD, under Digital India Corporation, provides Grant-in-aid support for 10 such “Responsible AI” themed projects promoting fair, transparent, and ethical AI practices.

Call For Expression of Interest on Responsible AI

MeitY in collaboration with MyGov invites and encourages organizations to explore and submit proposals on Responsible AI themes for building tools and frameworks.

Following is a list of Responsible AI themes that can be explored by organizations, in collaboration with other partners, to build tools and frameworks that promote the just and ethical development and deployment of AI across different sectors:


1.1. Machine Unlearning

Machine unlearning algorithms play a crucial role in rectifying inaccuracies, biases, and outdated information that can inadvertently become ingrained in machine learning models. These algorithms address the issue of models learning from incorrect, irrelevant, or harmful data, which can lead to faulty decisions in various applications. By facilitating the removal of undesirable learned behaviors, machine unlearning algorithms contribute to the development of more accurate, reliable, and fair AI systems across diverse domains.

1.2. Synthetic Data Generation

The imperative for developing synthetic data generation tools arises from the persistent challenges posed by limited, biased, or privacy-sensitive real-world datasets in various domains of machine learning and artificial intelligence. These tools create fabricated data instances that mimic the characteristics of genuine data, enabling machine learning models to train more effectively and robustly. By filling data gaps, mitigating privacy concerns, and promoting equitable representation, synthetic data generation tools play a pivotal role in advancing the capabilities of AI systems. The developer must ensure that the data synthetically generated is correct and in alignment with the rest of data so that false biases are not generated in AI algorithm during training.

1.3. Algorithm Fairness Tools

Algorithm fairness tools ensure that automated systems and decision-making algorithms treat all individuals fairly and without bias. Algorithms sometimes inadvertently discriminate against certain groups due to biases in data or design. Fairness tools provide a systematic way to assess, measure, and mitigate these biases, promoting ethical and equitable outcomes. These tools often provide quantitative metrics and visualizations to analyze bias in different aspects, such as race, gender, or other protected attributes. They can highlight disparities in predictions and outcomes. Examples of Algorithm Fairness Tools include IBM's AI Fairness 360, Google's What-If Tool, and Fairlearn by Microsoft.

1.4. AI Bias Mitigating Strategies

The need for AI bias mitigating strategies stems from the realization that artificial intelligence systems are increasingly integrated into various aspects of society, influencing decisions that impact individuals' lives. To ensure fairness, equity, and accountability, it is imperative to employ strategies that identify, analyze, and rectify biases within AI algorithms. Mitigation strategies can involve pre-processing data to remove bias, adjusting algorithms to account for fairness, or post-processing predictions to re-calibrate outcomes. Examples of AI Bias Mitigating Strategies include re-sampling data, re-weighting samples, adversarial training, and others that focus on reducing bias in predictions.

1.5. Ethical AI Frameworks

Ethical AI frameworks provide a structured approach to ensure that AI systems respect fundamental human values, uphold fairness, transparency, and accountability, and avoid perpetuating biases or discrimination. These frameworks encourage developers, researchers, and organizations to consider the broader societal implications of their AI creations and make informed decisions to minimize potential harm. Prominent ethical AI frameworks include the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems and the Ethics Guidelines for Trustworthy AI by the European Commission.

1.6. Privacy Enhancing Strategies

Privacy-enhancing strategies are essential in Responsible AI to address the growing concerns about data privacy and the potential misuse of personal information. They encompass a range of techniques, including data minimization, anonymization, differential privacy, and privacy-preserving machine learning. These approaches aim to reduce the risk of re-identification, unauthorized access, and data leakage while still allowing organizations to harness the power of AI for analysis, predictions, and decision-making.

1.7. Explainable AI (XAI) Frameworks

XAI frameworks provide methods and tools to make AI models more interpretable and transparent. They encompass techniques such as model visualization, feature importance analysis, and generating human-understandable explanations for AI predictions. These frameworks help users, including data scientists, regulators, and end-users, gain insights into the inner workings of complex AI models. By revealing the factors influencing model decisions, XAI enhances accountability and facilitates model debugging and improvement.

1.8. AI Ethical Certifications

AI Ethical Certifications are a formal assessment and recognition process that validates that AI systems, services, or organizations adhere to established ethical principles and guidelines in their development and deployment. By undergoing AI Ethical Certification, entities demonstrate their dedication to building and using AI in ways that prioritize ethical considerations, fostering trust among stakeholders and ensuring that AI technologies align with societal values and standards. These certifications assess aspects such as fairness, transparency, accountability, and the protection of privacy.

1.9. AI Governance Testing Frameworks

An AI governance testing framework is a structured approach for evaluating and ensuring compliance with governance policies, ethical guidelines, and regulatory requirements in the development and deployment of artificial intelligence systems. This framework helps organizations assess whether their AI initiatives align with responsible and ethical practices. A prominent example is A.I. Verify, which is the world’s first AI Governance Testing Framework and Toolkit for companies that wish to demonstrate responsible AI in an objective and verifiable manner.

1.10. Algorithmic Auditing Tools

Algorithmic auditing is a process of evaluating and scrutinizing the impact and behavior of algorithms and machine learning models, particularly in applications where these algorithms make decisions that affect individuals or communities. The primary goals of algorithmic auditing are to ensure fairness, transparency, and accountability in algorithmic decision-making and to mitigate potential biases and ethical concerns.

Who can apply?

2.1. Academic/R&D Organizations

The Academic/R&D Organizations should have the pre-existing Lab infrastructure (Lab having Workstations, Servers, Project Staff etc.) for project implementation and for training of students under the programme, along with relevant research publication. The Chief Investigator/Co-Chief Investigator should be regular faculty with relevant experience in the field of Responsible Artificial Intelligence.

All Institutions falling in the following categories would be eligible to participate in the programme and receive funding:

  1. Indian Institutes of Technologies (IITs)
  2. National Institutes of Technologies (NITs)
  3. Indian Institutes of Information Technology (IIITs)
  4. Indian Institutes of Science Education and Research (IISERs)
  5. Central Universities/Deemed Universities under Central/State Government
  6. Colleges/Institutions of National Importance/Eminence
  7. R&D Organizations/Institutions (Having B.Tech /MTech/PhD courses)
  8. Private Universities/ Private Deemed Universities/Private Colleges**

** Private Academic Institutions are also eligible for participation in the programme subject to meeting the additional eligibility criteria below:

Criteria for Private Academic Institutions

Private Institutions that offer undergraduate/postgraduate/diploma/certificate programs in Artificial Intelligence are eligible to apply. Institutions should be approved by AICTE and/or the Institution should be accredited by NAAC (National Assessment and Accreditation Council of UGC).

The proposals received from the Academic Institutions will be subjected to the scrutiny and evaluation by the Evaluation Committee and other proposal Review Committees for approval.

2.2. Start-Ups and Companies

  1. Startup should adhere to the extant norms as notified by DIPP and be in operations for at least 2 years.
  2. The entity must have at least 51% shareholding with Indian citizens or persons of Indian origin. The applicant's entity should not be a subsidiary company of any foreign corporation.
  3. Startup should have demonstrable expertise in the field of AI
  4. Indian Company/Foreign Company should adhere to the definition under the Companies Act, as applicable. The company should be in operations for at least 5 years and have demonstrable experience in the field of AI

Selection Process

The following steps outline the approach through which this initiative may be realized, ensuring its successful execution and alignment with the overarching objectives:

1. Application Process

Applicants will be required to submit the Expression of Interest (EOI) in the format attached in Performa. Applicants are free to apply for more than one project, however, if they wish to do so, they will be required to rank their project preference clearly.

2. Evaluation of EOIs

A committee of experts will evaluate the EOIs. The evaluation will focus on the capability, track record, and capacity of the institutions in different aspects of AI, among others.

Evaluation Criteria

1. Project Alignment with Objectives:

  1. How well does the proposed project align with the overarching goal of promoting fair and ethical AI utilization?
  2. Does the proposal clearly state how the project will contribute to addressing the identified AI challenges in these areas?

2. Innovation and Impact:

  1. Is the proposed project innovative and likely to make a significant contribution to Responsible AI?
  2. Are there novel approaches, methodologies, or technologies proposed that can advance the field of Responsible AI?

3. Collaborative Approach:

  1. Does the proposal demonstrate effective collaboration between academic institutions, industry, civil society, and other partners?
  2. Is there evidence of prior successful collaboration between the proposed partners?

4. Feasibility and Resources:

  1. Is the proposed project technically feasible within the 2-year timeframe?
  2. Does the proposal outline a realistic budget?
  3. Is the proposed budget reasonable and well-justified in relation to the expected project outcomes and impact?

5. Evaluation and Metrics:

  1. Are clear and measurable project milestones and outcomes defined?
  2. Are there quantitative and qualitative metrics to assess project impact and effectiveness?

Submission Guidelines

  1. The total length of the Performa should not exceed 15-20 pages
  2. Applicants are encouraged to jointly apply with a partner organization. Please note that the maximum duration for the project is 2 years.
  3. Provide information on all aspects mentioned in the template. Incomplete applications will be rejected.
  4. Any information found incorrect will lead to disqualification of the application.
  5. Applicants should read and adhere to the Terms and Conditions.
  6. In case of any questions, please feel free to reach out at pmu[dot]etech[at]meity[dot]gov[dot]in

Proposals submitted in other research areas may not be considered in this Call for proposals.

The submission must only be in PDF Format. Click here to download the PERFORMA

Terms and conditions

  1. The grant is for undertaking the specific project as approved by IndiAI and shall be subject to the following conditions:
    1. The grant shall be spent for the project within the specified time
    2. Any portion of the grant which is not ultimately required for expenditure for the approved purposes shall be duly surrendered to IndiaAI.
  2. For a project being executed by IndiaAI grant, Application by grantee institution for any other financial assistance or receipt of grant/loan from any other Agency/Ministry/Department for the same project should have the prior permission/approval of IndiaAI.
  3. The grantee institution is not allowed to entrust the implementation of this project for which grant-in-aid is received, to another institution and to divert the grant-in-aid received from IndiaAI as assistance to the later institution.
  4. The investigator(s) should not enter into collaboration with a foreign party (individual/academic institution/ industry) in execution of this project without prior approval of IndiaAI.
  5. The Grantee institution should indemnify IndiaAI from any legal and/or financial incumbrance arising out of any infringement of IPR/ licensing of IPR/ technology transfer/ commercialization.
  6. Any dispute on any matter related to the implementation of the project, the decision of the Secretary, MeitY, or CEO, IndiaAI shall be final and binding on the grantee institution.
  7. MeitY or IndiaAI reserves the right to modify these terms and conditions governing the grant-in-aid from time to time reflecting the directions of the Government of India.
  8. It is mandatory for all participating institutions/organizations to make available basic infrastructure such as Workstations/Servers (in working condition), lab staff etc. during the entire project duration. No separate hardware platforms like workstations, servers, laptops, etc. would be provided under the project.
  9. MeitY or IndiaAI reserves the right to ask for the furnishing of all Supporting Documents (like AICTE Recognition Certificate, NBA (National Board of Accreditation), NAAC (National Assessment and Accreditation Council of UGC), Startup Certificate, etc.) based on the category as and when required.

For detailed Terms and Conditions please click here.


Start Date: 22nd December, 2023
Last Date 04th February, 2024