Section 4 – Conceptual Methods of Classification

(1) These methods as designated in clause (i) of sub-section (1) of Section 3 categorize artificial intelligence technologies through a conceptual assessment of their utilisation, development, maintenance, and proliferation to examine & recognise their inherent purpose. This classification is further categorised as –

(i) Issue-to-Issue Concept Classification (IICC) as described in sub-section (2)

(ii) Ethics-Based Concept Classification (EBCC) as described in in sub-section (3)

(iii) Phenomena-Based Concept Classification (PBCC) as described in in sub-section (4)

(iv) Anthropomorphism-Based Concept Classification (ABCC) as described in in sub-section (5)

(2) Issue-to-Issue Concept Classification (IICC) involves the method to determine the inherent purpose of artificial intelligence technologies on a case-to-case basis, to examine & recognise their inherent purpose on the basis of these factors of assessment:

(i) Utilisation: Assessing the specific use cases and applications of the AI technology in various domains.

(ii) Development: Evaluating the design, training, and deployment processes of the AI technology.

(iii) Maintenance: Examining the ongoing support, updates, and modifications made to the AI technology.

(iv) Proliferation: Analysing the dissemination and adoption of the AI technology across different sectors and user groups.

Illustrations

(1) An AI system designed for medical diagnostics is classified based on its purpose to enhance patient outcomes. For instance, if an AI software assists doctors in diagnosing diseases more accurately, it is classified under medical AI applications.

(2) An AI system for financial trading is classified based on its purpose to optimize investment strategies. For example, if an AI-driven algorithm analyses market data to recommend stock trades, it is classified under financial AI applications.

(3) Ethics-Based Concept Classification (EBCC) involves the method of recognising the ethics-based relationship of artificial intelligence technologies in sector-specific & sector-neutral contexts, to examine & recognise their inherent purpose on the basis of these factors:

(i) Utilisation: Evaluating how AI technology impacts ethical principles during its use in specific sectors or across multiple domains.

(ii) Development: Assessing whether ethical considerations were integrated during the design, training, and deployment phases of the AI technology.

(iii) Maintenance: Examining how ethical responsibilities are upheld during updates and modifications to the AI system.

(iv) Proliferation: Analyzing how the widespread adoption of the AI system affects ethical standards across sectors and user groups.

Illustration

An AI for social media content moderation is assessed based on fairness and bias prevention. For example, if an AI filters hate speech and misinformation on social media platforms, it is classified under content moderation AI with an emphasis on ensuring unbiased and fair treatment of all users’ content.

(4) Phenomena-Based Concept Classification (PBCC) involves the method of addressing rights-based issues associated with the use and dissemination of artificial intelligence technologies to examine & recognise their inherent purpose on the basis of these factors:

(i) Utilisation: Assessing how the AI system affects individual or collective rights during its use in various domains.

(ii) Development: Evaluating whether evaluates whether AI systems incorporate protections for rights recognized under Indian law during their design, training, and deployment phases, considering legal constitutional, and commercial rights.

(iii) Maintenance: Reviewing how ongoing support and updates to the AI system protect user rights.

(iv) Proliferation: Analysing the rights-based implications of AI technology dissemination and adoption across different sectors and user groups.

Illustrations

(1) An AI system that analyses personal data for targeted advertising is classified based on its compliance with data protection rights. For example, an AI that personalizes ads based on user behaviour is classified under advertising AI with data privacy considerations.

(2) An AI used in autonomous vehicles is classified based on its implications for road safety and user rights. For instance, an AI that controls self-driving cars is classified under automotive AI with a focus on safety and user rights.

(5) Anthropomorphism-Based Concept Classification (ABCC) involves the method of evaluating scenarios where AI systems ordinarily simulate, imitate, replicate, or emulate human attributes, which include:

(i) Autonomy: The ability to operate and make decisions independently, based on a set of corresponding scenarios including but not limited to:

• Simulation: AI systems model autonomous decision-making processes using computational methods;

• Imitation: AI systems learn from and reproduce human-like autonomous behaviours;

• Replication: AI systems accurately reproduce specific human-like autonomous functions;

• Emulation: AI systems replicate and potentially enhance human-like autonomy;

Illustration

An AI-powered drone delivery system that navigates through urban environments, avoiding obstacles and adapting its route based on real-time traffic conditions to efficiently deliver packages without human intervention.

(ii) Perception: The ability to interpret and understand sensory information from the environment, based on a set of corresponding scenarios including but not limited to:

• Simulation: AI systems model human-like perception using computational methods;

• Imitation: AI systems learn from and reproduce specific human-like perceptual processes;

• Replication: AI systems accurately reproduce specific human-like perceptual abilities;

Illustration

A service robot in a hotel uses computer vision and natural language processing to recognize and greet guests by name, interpret their facial expressions and tone of voice to gauge emotions, and respond appropriately to verbal requests.

(iii) Reasoning: The ability to process information, draw conclusions, and solve problems, based on a set of corresponding scenarios including but not limited to:

• Simulation: AI systems model human-like reasoning using computational methods;

• Imitation: AI systems learn from and reproduce specific human reasoning patterns;

• Replication: AI systems accurately reproduce specific human-like reasoning abilities;

• Emulation: AI systems surpass specific human-like reasoning abilities;

Illustration

A medical diagnosis AI system analyses a patient’s symptoms, medical history, test results and imaging scans. It uses this information to generate a list of probable diagnoses, suggest additional tests to rule out possibilities, and recommend an optimal treatment plan.

(iv) Interaction: The ability to communicate and engage with humans or other AI systems, based on a set of corresponding scenarios including but not limited to:

• Simulation: AI systems model human-like interaction using computational methods;

• Imitation: AI systems learn from and reproduce specific human interaction patterns;

• Replication: AI systems accurately reproduce specific human-like interaction abilities;

• Emulation: AI systems enhance human-like interaction;

Illustration

An AI-powered virtual assistant engages in natural conversations with users, understanding context and nuance. It asks clarifying questions when needed, provides relevant information or executes tasks, and even interjects with suggestions or prompts.

(v) Adaptation: The ability to learn from experiences and adjust behaviour accordingly, based on a set of corresponding scenarios including but not limited to:

• Simulation: AI systems model human-like adaptation using computational methods.

• Imitation: AI systems learn from and reproduce human adaptation behaviours.

• Replication: AI systems reproduce human-like adaptation abilities, recognizing the inherent complexity.

• Emulation: AI systems surpass human-like adaptation as an aspirational goal.

Illustration

An AI system for stock trading continuously analyses market trends, world events, and the performance of its own trades. It identifies patterns and correlations, learning which strategies work best in different scenarios. The AI optimizes its trading algorithms and adapts its approach based on accumulated experience, demonstrating adaptive abilities.

(vi) Creativity: The ability to generate novel ideas, solutions, or outputs, based on a set of corresponding scenarios including but not limited to:

• Simulation: AI systems model human-like creativity using computational methods;

• Imitation: AI systems learn from and reproduce human creative processes;

• Replication: AI systems accurately reproduce human-like creative abilities, acknowledging the complexity involved;

• Emulation: AI systems enhance human-like creativity as a forward-looking objective;

Illustration

An AI music composition tool creates an original symphony. Given a theme and emotional tone, it generates unique melodies, harmonies and instrumentation. It iterates and refines the composition based on aesthetic evaluation models, ultimately producing a piece that is distinct from existing music in its training data.