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+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
+   carefully, as they describe your rights and restrictions with respect
+   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