Kant’s categorical imperative: The Kant’s categorical imperative is desired by the philosopher “Immanuel Kant” and he defined as that an “imperative” is a something that a person have to do. For example, suppose a person wants to stop for being a thirsty then he has to drink, so in that ways we can says it is an “imperative”. And this will also know as the categorical /direct imperative. Kant’s categorical imperative states that one should behave in such a way that one would want the behavior to be a universal law...

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Chapter 3.3, Problem 1EGDQ

a)

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Kant’s categorical imperative:

The Kant’s categorical imperative is desired by the philosopher “Immanuel Kant” and he defined as that an “imperative” is a something that a person have to do.

For example, suppose a person wants to stop for being a thirsty then he has to drink, so in that ways we can says it is an “imperative”. And this will also know as the categorical /direct imperative.

Kant’s categorical imperative states that one should behave in such a way that one would want the behavior to be a universal law...

b)

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Utilitarianism perspective:

Utilitarianism is nothing but a moral theory that states that the greatest act is the one that increases utility.

According to utilitarian perspective, using bots on dating site is not ethical as it brings benefits only to the company.

Customers are harmed financially, because they pay for the service provided by the company...

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here is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…

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