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diff --git a/doc/rfc/rfc9405.txt b/doc/rfc/rfc9405.txt new file mode 100644 index 0000000..3e3d076 --- /dev/null +++ b/doc/rfc/rfc9405.txt @@ -0,0 +1,245 @@ + + + + +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 |