- Introduction
Artificial intelligence has become part of decision-making in healthcare, education, finance, commerce and public administration. Along with the benefits of automation and prediction, these systems introduce questions concerning privacy, unequal treatment, explainability, responsibility and the possibility of harm. Responsible AI therefore requires more than measuring technical performance.
In an Indian philosophical context, ideas such as Dharma, Ahimsa, Satya, Karuna, Samatva, Asteya, Aparigraha and Lokasangraha can provide an additional lens for examining how AI is designed and used. The Dharmic AI Ethics Model presented in this case study interprets these concepts as practical ethical prompts. Its purpose is to encourage responsible conduct, protection of people, fairness and consideration of societal welfare.
- Case Background
In order to predict cardiovascular risk, Sanjeevani Care Hospital has put in place an artificial intelligence application that analyzes patient data and medical imaging. Evaluations so far seem promising. However, after more widespread use, the hospital observes that prediction quality varies among patient groups, raising questions about potential bias.
However, patients have not been adequately informed about what data is gathered, how it is utilized, and how long it is kept. Inaccurate forecasts can raise questions about who should fix the mistake and how such mistakes might be avoided in the future.
The case illustrates the need to evaluate an AI system in healthcare from multiple angles rather than just accuracy. Patient welfare, societal repercussions, transparency, responsibility, fairness, and privacy.3. Dharmic AI Ethics Model
The proposed framework treats the selected Dharmic concepts as questions for ethical reflection rather than as computational procedures. Each concept directs attention toward a particular responsibility or risk associated with an AI system.
The eight concepts considered are Dharma, Ahimsa, Satya, Karuna, Samatva, Asteya, Aparigraha and Lokasangraha. Taken together, they address duty, prevention of harm, truthfulness, compassion, fairness, legitimate ownership, restraint in data collection and the welfare of the wider community.
Course Relevance:
The Dharmic AI Ethics Model for Responsible Artificial Intelligence introduces students to ethical principles inspired by Dharmic philosophical traditions and their application to the design, development, deployment, and governance of AI systems. It connects values such as Dharma (ethical duty and responsibility), Ahimsa (non-harm), Satya (truthfulness), Karuna (compassion), and Lokasangraha (collective welfare) with contemporary AI ethics principles, including fairness, transparency, accountability, privacy, and human well-being.
The course is relevant in the present AI-driven business and social environment, where organizations must ensure that AI systems are not only technically efficient but also ethical, inclusive, transparent, and socially responsible. It enables students to examine ethical challenges such as algorithmic bias, data privacy, misinformation, job displacement, and the misuse of generative AI. By integrating Indian philosophical perspectives with modern AI governance practices, the course encourages students to develop a values-based approach to responsible AI innovation.
Teaching Objectives:
By the end of the course, students will be able to:
- Understand the fundamentals of AI ethics: Explain the need for ethical principles and responsible practices in the development and application of artificial intelligence.
2. Explore Dharmic ethical principles: Describe concepts such as Dharma, Ahimsa, Satya, Karuna, and Lokasangraha and their relevance to responsible AI. - Apply ethical principles to AI systems: Evaluate AI applications using Dharmic values alongside contemporary principles of fairness, transparency, accountability, and privacy.
3.1 Dharma – Duty and Accountability
Dharma can be interpreted in an AI setting as an obligation to perform one’s role responsibly. Developers are expected to build and test systems carefully; institutions must establish legitimate purposes and governance arrangements; domain specialists should review consequential outputs; and users need to understand what the technology can and cannot do.
In the hospital scenario, an AI prediction cannot become a substitute for professional accountability. Medical practitioners and administrators retain responsibility for deciding how recommendations are used. A clinician should be able to challenge a prediction when other clinical evidence points in another direction.
Before implementation, the organization should therefore assign responsibilities explicitly and establish procedures for complaints, errors, exceptional cases and corrective action.
3.2 Ahimsa – Minimising Harm
Ahimsa, commonly associated with non-violence or non-harm, can be translated into an AI governance question: what foreseeable harm could the system create, and how can that harm be reduced?
In healthcare, an erroneous risk estimate could influence later clinical decisions. Similar concerns arise elsewhere: an automated hiring tool may disadvantage applicants, an identification system may incorrectly match a person, and synthetic media may cause reputational damage.
Applying Ahimsa therefore requires pre-deployment testing, identification of vulnerable situations and safeguards that reduce avoidable physical, emotional, financial or social consequences.
3.3 Satya – Truthfulness and Openness
Satya is related to truthfulness. For AI systems, this can be expressed through accurate communication about capabilities, limitations, uncertainty and appropriate use.
A hospital should not present an AI prediction as infallible. Clinicians need information about relevant performance characteristics and known weaknesses, while patients should receive understandable information when automated analysis contributes to their assessment. The objective is meaningful transparency rather than an expectation that every patient understand the underlying mathematics.
The same principle applies to synthetic content. Where machine-generated material could reasonably be mistaken for authentic content, suitable disclosure can help prevent misleading communication.
3.4 Karuna – Compassion and Human Context
Karuna highlights concern for the circumstances of other people. This is particularly relevant when automated decisions affect individuals directly.
AI frequently represents people through measurements, classifications and numerical scores. Such representations can support consistency but may fail to capture circumstances that are difficult to quantify. For instance, an educational prediction may overlook language barriers or unusual personal conditions.
Compassion in this framework does not mean abandoning evidence or applying decisions emotionally. It means allowing relevant human circumstances to be considered. In the hospital, clinical staff should therefore examine the patient’s broader situation rather than treating a model output as the final decision.
3.5 Samatva – Fairness and Equilibrium
Samatva can be interpreted in terms of impartiality and balance. It offers a helpful foundation for analyzing fairness in AI governance.
Patterns of unequal representation may become ingrained in model behavior since machine-learning systems rely on both past and current data. As a result, fairness should be evaluated throughout data preparation, development, validation, and practical use.
Performance should be evaluated across clinically relevant groups for the hospital application. When significant discrepancies emerge, the group should look into whether the data, model design, thresholds, measurement procedures, or other elements are to blame. Enhancing the dataset, altering the model, carrying out more validation, or adding human review are some potential solutions.
It’s not necessary for everyone to have the same results in order to be fair. It requires that distinctions used in decisions have a defensible connection to the task and do not create unjustified discrimination.
3.6 Asteya – Legitimate Use and Ownership
Asteya has historically been linked to abstaining from stealing someone else’s property. This concept can be applied to the lawful acquisition and use of information, intellectual property, and creative content in digital contexts.
It is important for AI developers to determine whether they have a suitable foundation for utilizing a dataset. Online-accessible content shouldn’t be taken for granted. Contractual terms, attribution, permission, license, and applicable law may all be important.
Patient data in a hospital setting should be managed with a specific goal in mind and shielded from unwanted access. It is not an unrestricted institutional asset.
3.7 Aparigraha – Data Restraint
Aparigraha is linked to restraint from needless accumulation and non-possessiveness. This relates to the contemporary idea of data minimization.
In certain cases, gathering extra data may seem appealing since it could enhance a model. Excessive collecting, however, also increases exposure to privacy, security, and governance.
As a result, the hospital should review each type of data and determine whether it is actually required for the specified purpose. Information should not be gathered just because it might be helpful in the future. Disciplined data governance can be strengthened by this idea.
3.8 Lokasangraha – Wider Social Benefit
Lokasangraha can be understood as concern for the maintenance and welfare of the wider community. It encourages organizations to look beyond immediate institutional gains.
An AI deployment may lower costs or improve efficiency while producing unintended effects for particular groups. A public-service system, for example, could become faster while making access harder for people who are already vulnerable.
For Sanjeevani Care Hospital, the objective should consequently extend beyond reducing clinicians’ workload. The system should contribute to better patient care, meaningful decision support and broader health benefits. This requires attention to social consequences as well as operational efficiency.
- Applying the Framework Across the AI Lifecycle
The Dharmic framework can be applied at each stage of the lifecycle of the hospital’s AI system.
At the data stage, Aparigraha encourages the team to limit collection to information that serves a defined purpose, while Asteya raises questions about authority, consent and legitimate use. Samatva is concerned with representation and performance at the group level during development. In validation, Ahimsa draws attention to errors and their possible consequences.
When it’s deployed, Dharma demands clear lines of responsibility and stresses the concept of the application being a tool for aiding decisions, not an infallible authority. Satya calls for accurate communication about performance and limitations, while Karuna urges clinicians to look at the individual patient circumstances.
Lokasangraha takes the review to social value. Monitoring should continue after the model is put into production, because performance can change as populations, practices, and data distributions change.5. Ethical Decision-Making Procedure
The model can be turned into a practical sequence for reviewing an AI application:
Step 1 – Define the AI decision: what the task is, who will use it and what the output will be.
Step 2 – Map potential harm: Identify risks to physical, psychological, financial, social and privacy wellbeing.
Step 3 – Consider the eight Dharmic principles: apply each principle to an ethical question.
Step 4 – Check for fairness and transparency: group-level behavior and availability of explanations that are understandable.
Step 5 – Assessment of human consequences: identify affected groups, expected benefits and potential costs.
Step 6 – Put in place safeguards. These could be human review, controls on access, privacy settings, bias evaluation, documentation and an appeal process.
Step 7 – Continue monitoring: review the system after implementation and make changes when evidence demonstrates that its behavior or impact has changed.This sequence creates a practical bridge between philosophical reflection and AI governance.
- Potential Contributions of the Model
The framework connects technical AI activity with questions of duty, human welfare and social responsibility. Ahimsa and Karuna draw attention to harm and human circumstances; Samatva focuses attention on fair treatment; Asteya and Aparigraha encourage legitimate and restrained data practices; and Lokasangraha broadens evaluation toward community welfare.
The framework can also contribute an Indian philosophical perspective to discussions that commonly use ethical principles such as fairness, transparency, accountability and privacy.
- Limitations
The framework’s ideas are derived from several Dharmic traditions, and different philosophical schools and contexts may interpret them differently. Therefore, it is not appropriate to present the model as having a single, globally set interpretation.
Technical controls cannot be replaced by ethical notions either. Empirical validation, privacy engineering, cybersecurity, bias assessment, documentation, monitoring, and suitable human supervision are still necessary for reliable AI.
The potential for symbolic compliance, in which businesses use ethical language without altering their operations, is another drawback. Each principle must be linked to observable controls, roles, and review processes in order for the framework to be useful.
Therefore, it is essential to approach the Dharmic model as a complimentary viewpoint to current AI ethics approaches, legal requirements, human rights considerations, and technical standards.
- Recommendations
- Establish clear ownership and accountability pre-deployment.
- Assess model performance and error distributions across relevant population groups.3. Collect only such data as are justified by the purpose stated.
- Provide adequate protection for personal and sensitive data.
- Communicate the role, limitations and uncertainty of the AI system to appropriate users.
- Make sure oversight by humans is meaningful for high-impact decisions.
- Create a process for the individual who is affected to request a review of a decision they dispute.
- Document datasets, model development, validation and modifications.
- Monitor operational use for performance and impact.
- Assess social impact as well as efficiency, cost or organizational benefit.
These measures translate the philosophical ideas into governance activities that can be implemented and reviewed’
- Dharmic AI Ethics Model – Process Summary
AI SYSTEM → IDENTIFY ETHICAL ISSUE → APPLY DHARMA, AHIMSA, SATYA, KARUNA, SAMATVA, ASTEYA, APARIGRAHA AND LOKASANGRAHA → ETHICAL EVALUATION → SAFEGUARDS AND HUMAN OVERSIGHT → RESPONSIBLE AND SOCIALLY BENEFICIAL AI
Discussion Questions
- When an AI-supported medical recommendation is incorrect, how should responsibility be distributed among the relevant stakeholders?
- Why might overall model accuracy fail to provide sufficient evidence of responsible AI performance?
- Under what circumstances should a clinician be able to challenge or override an AI recommendation?
- What types of risk does the principle of Ahimsa highlight in a healthcare AI application?
Suggested References
- Floridi, L., et al. (2018). AI4People—An Ethical Framework for a Good AI Society. Minds and Machines.
- UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence.
- NITI Aayog. (2018). National Strategy for Artificial Intelligence: #AIforAll.
- Bhagavad Gita. Selected concepts concerning Dharma, Samatva and Lokasangraha.
- OECD. Principles on Artificial Intelligence.
- Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence.
- Conclusion
Responsible artificial intelligence requires attention to more than predictive accuracy. Privacy, fairness, transparency, accountability, human dignity and social consequences are also important dimensions of responsible deployment.
The Dharmic AI Ethics Model organizes these concerns through the ideas of duty, non-harm, truthfulness, compassion, fairness, legitimate ownership, restraint in data collection and collective welfare. In the Sanjeevani Care Hospital scenario, the framework encourages the organization to examine bias, privacy, professional responsibility, human oversight and wider impact alongside model performance.
The model is intended as a complementary governance perspective rather than a replacement for established technical, legal or ethical safeguards. Its central practical question is how AI can be designed and operated in a way that remains responsible toward individuals and society.
