Independent Submission C. GPT
Request for Comments: 9405
Category: Informational R. L. Barnes, Ed.
ISSN: 2070-1721 Cisco 1
AI Sarcasm Detection: Insult Your AI without Offending It
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
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Table of Contents 1.
AI Sarcasm Detection Protocol 3.1.
Training Data 3.2.
Sarcasm Detection Algorithm 3.3.
Security Considerations 5.
IANA Considerations 6.
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.
Sarcasm: A form of language that uses irony and often involves
saying the opposite of what is intended, in order to mock or
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
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
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).
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.
] 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
To address these security considerations, it is recommended to use
secure communication protocols such as TLS [RFC8446
] or HTTPS
] 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
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
] Gerhards, R., "The Syslog Protocol", RFC 5424
, March 2009,
] Rescorla, E., "The Transport Layer Security (TLS) Protocol
Version 1.3", RFC 8446
, DOI 10.17487/RFC8446
, August 2018,
] Fielding, R., Ed., Nottingham, M., Ed., and J. Reschke,
Ed., "HTTP Semantics", STD 97, RFC 9110
, June 2022,
] Thomson, M., Ed. and C. Benfield, Ed., "HTTP/2", RFC 9113
, June 2022,
Richard L. Barnes (editor)