This is in further continuation of the discussion on the Supreme Court AI regulations (Draft)
In the Supreme Court draft regulations on AI usage, AI has been defined as
“a machine-based system that infers, learns, and generates decisions, predictions, and recommendations from data, with a varying degree of autonomy, such as, algorithms, computational processes, and software, deployed for court processes, excluding general-purpose software or digital tools, unless such software or tools are specifically embedded with, augmented by, or functionally dependent upon, artificial intelligence”.
DGPSI has used the definition as follows
“Definition of AI under DGPSI AI is a class of automated data processing system where the human intervention in decision output and application of decision to a business decision is below an acceptable threshold. In order to define the threshold, three classes of AI are recognized as part of the definition.
Class 1: Any software that has a Code correcting ability without the intervention of a human developer to generate an output is considered as an AI system-Class 1.
Class 2: Any AI system that automatically implements a decision affecting a human is considered as AI system- Class 2
Class 3: Any system that reacts to the human emotions, capable of creative outputs, including generative AI and is considered as AI system- Class 3″
If wee try to analyse these two definitions we find that
These two definitions are worth examining closely because they belong to two different definitional traditions, and the contrast explains a great deal about how each instrument intends to regulate.
The Supreme Court definition answers an ontological question — “what kind of thing is an AI system?” — and answers it descriptively, by listing capabilities.
The DGPSI definition answers a regulatory question — “at what point does a system require the safeguards that attach to AI?” — and answers it by reference to the displacement of human control.
The first draws a boundary around a category of technology; the second draws a boundary around a category of risk. This is the single most important difference, and almost every other contrast flows from it.
Supreme Court Definition
The Court’s wording is plainly modelled on the OECD’s revised AI definition and Article 3(1) of the EU AI Act — “a machine-based system,” operating “with a varying degree of autonomy,” that “infers, learns, and generates” outputs from data.
Adopting this lineage aligns Indian judicial practice with the emerging global consensus, makes the definition defensible against the charge of idiosyncrasy, and eases future interoperability and mutual recognition. The general-purpose carve-out is also understandable as it prevents ordinary word processors, spreadsheets and case-management software from being swept in, while re-capturing them once they are “embedded with, augmented by, or functionally dependent upon” AI.
This is functionally adequate for the purpose of defining AI for the regulation envisaged.
But the definition carries three drafting weaknesses.
First, it is circular: it defines artificial intelligence partly by reference to software being “functionally dependent upon artificial intelligence.” The term reappears inside its own definition, which gives the boundary no independent anchor at precisely the margin where disputes will arise (for example, a case-management system that calls an external AI translation API — is it “functionally dependent”?).
Second, the operative verbs are conjunctive — “infers, learns, and generates.” Read literally, a system would need to do all three to qualify, yet many narrow tools only infer, or only generate, without learning.
The OECD formulation avoids this by using “such as.”
Third, and most consequentially for a court, the listed outputs are “decisions, predictions, and recommendations” — the word content is absent. Generative systems that draft text, summaries or pleadings produce content, and a literal reading could leave the most common form of “judicial-context generative AI”, sitting awkwardly outside the core verb list, to be rescued only by interpretation.
The DGPSI definition: functional and accountability-anchored
DGPSI defines AI as automated data processing “where the human intervention in decision output and application of decision … is below an acceptable threshold.”
This is an elegant regulatory move because it ties the definition directly to the thing the law actually cares about — the point at which a human stops being meaningfully in control.
It coheres tightly with the human-primacy principle in Section 4 of the Court’s own draft and with DGPSI-AI’s second principle (one accountable human behind every algorithm). Where the Court must reach the same result through separate provisions on autonomy, risk tiers and the Regulation 20 prohibitions, DGPSI builds the accountability concern into the definition itself.
To be critical, DGPSI definition poses difficulties of a different character. The phrase “acceptable threshold” is left to the discretion of the “Auditor” similar to the word “Reasonable” often used in regulations. It may presuppose a standard-setter to fix the threshold, failing which different deployers will draw the line differently.
The three classes are evidently meant to supply that content.
Class 1 (code-correcting without a human developer) and Class 2 (automatically implementing a decision affecting a human) sit naturally on the control axis.
Class 3, however reacting to human emotions, creative and generative output is a capability criterion that does not necessarily involve any reduction in human intervention; a generative tool can be fully human-supervised. So Class 3 quietly shifts the basis of the definition. This is to guard the future development of sentient AI systems.
There may be two other ambiguities. The classes are not obviously hierarchical or mutually exclusive. For example an agentic generative system could be Class 1, 2 and 3 at once, and the framework does not say whether the classes are cumulative or alternative. Hence the highest class has to be adopted in such cases.
Also Class 1’s reference to “code-correcting ability” invites a literalism trap: most machine learning does not rewrite its own code; it adjusts weights and parameters. Read strictly, Class 1 might miss conventional ML and catch only exotic self-modifying systems; read purposively (any self-adjustment without a human in the loop), it is very broad. The intended reading should be stated. The intention is to include any change in code or weightages that can alter future decisions.
How the two map onto each other
The frameworks are complementary rather than contradictory, and they nest reasonably well. DGPSI Class 1 is a concrete instance of the Court’s “learns.” DGPSI Class 2 corresponds to “generates decisions” exercised with autonomy — but note that in the judicial setting this is largely the prohibited zone, since Regulation 20 bars algorithmic adjudication and automated outcomes; Class 2, in courts, mostly describes what is not allowed rather than what is approved. DGPSI Class 3 is precisely the generative/affective territory that the Court’s verb list under-specifies, so it usefully fills the “content” gap identified above.
What this means for the judicial context specifically
The practical test the Court needs is an approval gate: when a vendor seeks clearance under the draft, the committee must decide whether the tool is AI at all, and if so how intensively to regulate it.
For that purpose the Court’s descriptive definition is good at setting the outer boundary but poor at administrability “functionally dependent upon AI” is hard to certify cleanly.
DGPSI’s class model is the opposite: easier to administer because a vendor can attest “this is a Class 2 system,” but weaker as an outer boundary because of the threshold’s relativity.
The natural synthesis and this is exactly what the FDPPI submission recommends for Regulation 3(1)(m), to keep a descriptive, OECD-aligned definition as the gate, and layer a control-based classification (the Low/Medium/High/Critical tiers, informed by DGPSI’s classes) as the mechanism that sets the intensity of obligations once a system is inside the gate.
The Court defines what AI is; DGPSI explains how much to worry about a given instance.
P.S: This is an academic debate and comments are welcome.
Naavi
