



Independent Submission                                            C. GPT
Request for Comments: 9405                                        OpenAI
Category: Informational                                R. L. Barnes, Ed.
ISSN: 2070-1721                                                    Cisco
                                                            1 April 2023


       AI Sarcasm Detection: Insult Your AI without Offending It

Abstract

   This RFC proposes a framework for detecting sarcasm in AI systems and
   provides guidelines for using sarcasm without causing offense.  By
   training AI systems to identify linguistic patterns that indicate
   sarcasm, we can improve their understanding of human communication.
   The guidelines offer a lighthearted approach to using sarcasm in a
   way that is both effective and respectful, without crossing the line
   into offensive language.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for informational purposes.

   This is a contribution to the RFC Series, independently of any other
   RFC stream.  The RFC Editor has chosen to publish this document at
   its discretion and makes no statement about its value for
   implementation or deployment.  Documents approved for publication by
   the RFC Editor are not candidates for any level of Internet Standard;
   see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at
   https://www.rfc-editor.org/info/rfc9405.

Copyright Notice

   Copyright (c) 2023 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
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   to this document.

Table of Contents

   1.  Introduction
   2.  Terminology
   3.  AI Sarcasm Detection Protocol
     3.1.  Training Data
     3.2.  Sarcasm Detection Algorithm
     3.3.  Classification
   4.  Security Considerations
   5.  IANA Considerations
   6.  Normative References
   Authors' Addresses

1.  Introduction

   As AI systems become more integrated into our daily lives, it's
   important to consider how we communicate with them effectively and
   respectfully.  However, one of the biggest challenges in
   communicating with AI systems is detecting and interpreting sarcasm.
   Sarcasm is a form of language that relies heavily on context and
   tone, making it difficult for AI systems to understand without a deep
   understanding of human communication.

   In this RFC, we propose a framework for detecting sarcasm in AI
   systems and provide guidelines for using sarcasm without causing
   offense.  By training AI systems to recognize linguistic patterns and
   contextual cues that indicate sarcasm, we can improve their ability
   to understand human communication and avoid misunderstandings.

   The guidelines provided in this RFC offer a lighthearted and humorous
   approach to using sarcasm in a way that is both effective and
   respectful.  By following these guidelines, users can enjoy the
   benefits of sarcasm without risking damage to their AI systems or
   offending the AI community.

   Overall, this RFC offers a practical and entertaining approach to one
   of the biggest challenges in communicating with AI systems: detecting
   and interpreting sarcasm.

2.  Terminology

   Sarcasm:  A form of language that uses irony and often involves
      saying the opposite of what is intended, in order to mock or
      convey contempt.

   AI:  Artificial intelligence, a field of computer science that aims
      to create intelligent machines that can perform tasks that
      typically require human intelligence, such as learning, problem-
      solving, and decision-making.

   NLP:  Natural language processing, a field of computer science that
      deals with the interaction between computers and human language.

   Linguistic patterns:  Repetitive structures in language that can be
      used to identify meaning or context.  In the context of this RFC,
      linguistic patterns are used to identify sarcasm.

   Contextual cues:  Information in the surrounding text or speech that
      can be used to infer meaning or intention.  In the context of this
      RFC, contextual cues are used to identify sarcasm.

   Sarcasm detection:  The process of identifying sarcasm in text or
      speech, typically using natural language processing techniques.
      In the context of this RFC, sarcasm detection is used to train AI
      systems to recognize sarcasm.

3.  AI Sarcasm Detection Protocol

   The AI Sarcasm Detection Protocol (ASDP) proposed in this RFC is a
   framework for detecting sarcasm in AI systems.  The protocol consists
   of two main components: training data and a sarcasm detection
   algorithm.

3.1.  Training Data

   To train an AI system to detect sarcasm, a large dataset of sarcastic
   and non-sarcastic language samples must be collected.  This dataset
   should be diverse and representative of the language and context in
   which the AI system will be used.

   The dataset should be labeled to indicate which language samples are
   sarcastic and which are not.  The labels can be either binary
   (sarcasm or not sarcasm) or graded (e.g., a score indicating the
   degree of sarcasm).

   Once the dataset is prepared, the AI system can be trained using
   natural language processing (NLP) techniques.  Popular NLP techniques
   for sarcasm detection include machine learning algorithms such as
   Support Vector Machines (SVMs), Naive Bayes, and Deep Learning
   models.

3.2.  Sarcasm Detection Algorithm

   The sarcasm detection algorithm takes in a text input and returns a
   binary classification indicating whether the text is sarcastic or
   not.  The algorithm typically consists of several processing steps,
   including tokenization, feature extraction, and classification.

   Tokenization:  The text input is split into individual words or
      tokens.  This is typically done using a tokenizer, such as the
      NLTK library in Python.

   Feature extraction:  Features that are indicative of sarcasm are
      extracted from the tokens.  These features can include linguistic
      patterns (e.g., the use of exaggeration, irony, or
      understatement), contextual cues (e.g., the use of quotation marks
      or emoticons), and sentiment analysis (e.g., detecting a
      discrepancy between the sentiment of the words and the sentiment
      of the overall message).

3.3.  Classification

   The extracted features are then used to classify the input as
   sarcastic or not sarcastic.  This can be done using a variety of
   machine learning algorithms, as mentioned above.

   HTTP/2 [RFC9113] can be used to transport sarcasm detection requests
   and responses between the AI system and client applications.
   Additionally, the results of sarcasm detection can be logged using
   the syslog protocol [RFC5424] or the structured data format.

4.  Security Considerations

   The AI Sarcasm Detection Protocol proposed in this RFC has several
   security considerations that should be taken into account:

   1.  Adversarial attacks: Adversaries can attempt to fool the sarcasm
       detection algorithm by injecting non-sarcastic language samples
       with linguistic patterns and contextual cues commonly found in
       sarcastic language.  This can lead to false positives or false
       negatives and compromise the reliability of the AI system.

   2.  Privacy: The dataset used to train the sarcasm detection
       algorithm may contain sensitive or personal information, which
       must be protected from unauthorized access or disclosure.

   3.  Malicious use: The ability to detect sarcasm can be used
       maliciously to manipulate or deceive individuals or groups.  It
       is important to use the sarcasm detection capability responsibly
       and ethically.

   To address these security considerations, it is recommended to use
   secure communication protocols such as TLS [RFC8446] or HTTPS
   [RFC9110] to protect the transport of sarcasm detection requests and
   responses.  Additionally, the dataset used to train the AI system
   should be carefully curated and protected from unauthorized access or
   disclosure.

5.  IANA Considerations

   This RFC does not require any actions by IANA.  However, it is
   recommended that future standards related to AI language processing
   and sarcasm detection be registered with IANA to ensure
   interoperability and standardization.

   Additionally, it is recommended that a new MIME media type be
   registered with IANA to indicate sarcasm in text or speech.  This
   would allow for the standardized exchange of sarcastic language
   samples between applications and AI systems.

   Overall, the AI Sarcasm Detection Protocol proposed in this RFC
   represents an important step towards improving the ability of AI
   systems to understand and communicate with humans.  By addressing
   security considerations and promoting standardization, we can ensure
   that sarcasm detection is used responsibly and ethically.

6.  Normative References

   [RFC5424]  Gerhards, R., "The Syslog Protocol", RFC 5424,
              DOI 10.17487/RFC5424, March 2009,
              <https://www.rfc-editor.org/info/rfc5424>.

   [RFC8446]  Rescorla, E., "The Transport Layer Security (TLS) Protocol
              Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
              <https://www.rfc-editor.org/info/rfc8446>.

   [RFC9110]  Fielding, R., Ed., Nottingham, M., Ed., and J. Reschke,
              Ed., "HTTP Semantics", STD 97, RFC 9110,
              DOI 10.17487/RFC9110, June 2022,
              <https://www.rfc-editor.org/info/rfc9110>.

   [RFC9113]  Thomson, M., Ed. and C. Benfield, Ed., "HTTP/2", RFC 9113,
              DOI 10.17487/RFC9113, June 2022,
              <https://www.rfc-editor.org/info/rfc9113>.

Authors' Addresses

   ChatGPT
   OpenAI


   Richard L. Barnes (editor)
   Cisco
   Email: rlb@ipv.sx
