Explainability in epidemiological AI systems

Introduction

Explainability in epidemiological AI systems refers to the ability to understand, interpret and examine how artificial intelligence (AI) models analyse public health data and generate predictions concerning disease outbreaks, infection patterns, mortality rates and population health risks.

Epidemiological AI systems use machine learning, predictive analytics, statistical modelling and large datasets to identify disease trends and support public health decisions. These systems may analyse hospital records, vaccination information, demographic characteristics, environmental conditions, mobility patterns and laboratory reports.

Although AI can improve disease surveillance and outbreak prediction, its decisions may affect public healthcare policies, resource allocation, individual privacy and public safety. Therefore, explainability is essential to ensure that automated predictions remain transparent, scientifically reliable and legally accountable.

Meaning and importance of explainability

Explainability means providing understandable information about how an AI system reaches a particular prediction or recommendation. It enables researchers, healthcare professionals, regulators and affected individuals to examine the factors influencing an algorithmic outcome.

For example, an epidemiological AI system may predict an increased risk of dengue transmission in a particular region. Explainability allows public health authorities to understand whether the prediction was influenced by rainfall, temperature, population density, mosquito surveillance or historical infection records.

The principal objectives include:

Improving transparency in public health decision-making.

Identifying errors and biased algorithmic predictions.

Establishing accountability for incorrect recommendations.

Supporting independent scientific verification.

Protecting individual privacy and sensitive health information.

Improving public confidence in AI-based disease surveillance.

Ensuring that healthcare decisions remain scientifically justified.

Legal framework in India

1. Article 21 of the Constitution

Article 21 protects life and personal liberty. The Supreme Court has interpreted this provision to include privacy, dignity and personal autonomy.

Epidemiological AI systems processing identifiable health information must therefore operate consistently with constitutional protections. Where automated predictions influence access to healthcare or other important decisions, appropriate safeguards against arbitrary treatment become particularly important.

2. Digital Personal Data Protection Act, 2023

The Digital Personal Data Protection Act, 2023, regulates the processing of digital personal data in India.

Epidemiological AI systems frequently process medical histories, diagnostic information and demographic records. Where such information relates to identifiable individuals, applicable data protection obligations must be considered.

The Act establishes requirements concerning lawful processing, security safeguards and individual rights. However, it does not create a comprehensive, standalone right to an explanation of every AI-generated decision.

Its application must also be considered alongside commencement notifications and applicable rules.

3. Information Technology Act, 2000

The Information Technology Act, 2000, provides a legal framework for electronic records and specified cyber-related offences.

AI-based epidemiological platforms may depend on digital health databases, cloud infrastructure and electronic surveillance systems. Legal safeguards concerning unauthorised access, misuse of information and cybersecurity are therefore relevant.

4. National Digital Health Mission framework

The Ayushman Bharat Digital Mission (ABDM) promotes digital health infrastructure and the exchange of health information through interoperable systems.

Its health-data management principles are relevant to epidemiological AI because accurate data governance, consent mechanisms and secure information exchange are necessary for trustworthy public health analytics.

5. WHO guidance on AI in healthcare

The World Health Organization's guidance on ethics and governance of artificial intelligence for health, published in 2021, emphasises transparency, accountability, inclusiveness, human autonomy and protection of individual rights.

Although it is not directly binding Indian legislation, it provides an important international policy framework for responsible epidemiological AI deployment.

Major elements of explainability

1. Algorithmic transparency

Public health authorities should understand the basic functioning of AI models, including their objectives, training methods, input variables and limitations.

Transparency allows researchers to determine whether a model relies on scientifically relevant information or inappropriate assumptions.

2. Data transparency

AI predictions depend heavily on the quality of training data. Incomplete hospital records, inaccurate diagnostic information and underrepresentation of certain populations may produce misleading results.

Authorities should maintain appropriate documentation concerning data sources, collection methods and validation procedures.

3. Explainable predictions

AI systems should provide meaningful explanations for important predictions. For example, an outbreak prediction should identify the major factors contributing to the estimated risk rather than simply displaying a numerical probability.

Techniques such as feature importance analysis, decision trees and SHAP-based explanations may help researchers understand model behaviour.

4. Bias identification

Epidemiological AI may produce inaccurate results when certain communities are underrepresented in training datasets.

Explainability helps identify whether predictions are disproportionately influenced by demographic, geographical or socioeconomic characteristics.

5. Human oversight

AI predictions should support, rather than automatically replace, qualified public health professionals.

Human experts should be able to examine algorithmic recommendations, question unusual outcomes and correct errors before important decisions are implemented.

Important case laws

1. Justice K.S. Puttaswamy v. Union of India (2017)

The Supreme Court recognised privacy as a fundamental right under Article 21 and other constitutional guarantees. The judgment is highly relevant to epidemiological AI because these systems may process sensitive health and demographic information. It supports the requirement that health-data processing must respect privacy, legality and proportionality.

2. Anuradha Bhasin v. Union of India (2020)

The Supreme Court emphasised the importance of proportionality, transparency and judicial review in restrictions affecting fundamental rights. Although the case concerned internet restrictions, its reasoning is relevant by analogy when public authorities deploy AI-based surveillance systems that may affect individual rights.

3. Maneka Gandhi v. Union of India (1978)

The Supreme Court expanded the interpretation of Article 21 and established that procedures affecting personal liberty must be fair, just and reasonable. This constitutional principle is relevant when automated epidemiological systems influence decisions affecting individuals.

4. K.S. Puttaswamy (Aadhaar) v. Union of India (2018)

The Supreme Court examined the constitutional validity of the Aadhaar framework and considered privacy, proportionality and the use of personal information. Its reasoning is relevant to large-scale health-data systems that rely on identification and digital information processing.

5. State of Maharashtra v. Praful B. Desai (2003)

The Supreme Court recognised the admissibility of video-conferencing evidence in judicial proceedings under the applicable evidentiary framework. Although it did not concern AI explainability, the decision illustrates the legal system's engagement with technological methods and the importance of examining their proper use.

These judgments do not directly establish a legal right to algorithmic explainability. They provide constitutional and technological principles relevant to transparency, privacy and accountability in epidemiological AI.

Challenges in implementing explainability

Epidemiological AI systems face several technical and legal difficulties.

First, complex machine-learning models may produce accurate predictions without providing easily understandable explanations. This creates difficulties for doctors, epidemiologists and public administrators.

Second, public health datasets may contain incomplete or inaccurate information. An apparently reasonable explanation may therefore conceal weaknesses in the underlying data.

Other challenges include:

Protection of confidential medical information.

Difficulty explaining complex neural-network models.

Lack of standardised AI audit procedures.

Discrimination arising from biased datasets.

Conflicts between commercial confidentiality and transparency.

Difficulty assigning responsibility for incorrect predictions.

Limited technical expertise within public health institutions.

Safeguards for responsible epidemiological AI

An effective legal and regulatory framework should require appropriate documentation, independent evaluation and meaningful human oversight.

Important safeguards include:

Maintaining records of training datasets and model development.

Conducting independent scientific validation.

Publishing understandable explanations of important predictions.

Regularly auditing models for discriminatory outcomes.

Protecting identifiable health information.

Establishing procedures for correcting inaccurate predictions.

Ensuring human review of high-impact decisions.

Maintaining clear institutional accountability.

Public authorities should also communicate uncertainty levels so that predictions are not incorrectly presented as established scientific facts.

Conclusion

Explainability in epidemiological AI systems is essential for ensuring transparency, scientific reliability and public accountability in modern healthcare governance. India's constitutional privacy framework, the Digital Personal Data Protection Act, 2023, and digital health governance principles provide important foundations for responsible AI deployment. Judicial decisions such as Justice K.S. Puttaswamy v. Union of India and Maneka Gandhi v. Union of India reinforce the importance of privacy, fairness and protection of individual rights. A responsible framework must combine understandable algorithmic explanations, independent scientific validation, secure data management and meaningful human oversight to ensure that epidemiological AI benefits public health without undermining fundamental rights.