Human Heuristics for AI-Generated Language Are Flawed
- URL: http://arxiv.org/abs/2206.07271v3
- Date: Mon, 7 Nov 2022 09:47:29 GMT
- Title: Human Heuristics for AI-Generated Language Are Flawed
- Authors: Maurice Jakesch, Jeffrey Hancock, Mor Naaman
- Abstract summary: We study whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI.
We experimentally demonstrate that these wordings make human judgment of AI-generated language predictable and manipulable.
We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI.
- Score: 8.465228064780744
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Human communication is increasingly intermixed with language generated by AI.
Across chat, email, and social media, AI systems produce smart replies,
autocompletes, and translations. AI-generated language is often not identified
as such but presented as language written by humans, raising concerns about
novel forms of deception and manipulation. Here, we study how humans discern
whether verbal self-presentations, one of the most personal and consequential
forms of language, were generated by AI. In six experiments, participants (N =
4,600) were unable to detect self-presentations generated by state-of-the-art
AI language models in professional, hospitality, and dating contexts. A
computational analysis of language features shows that human judgments of
AI-generated language are handicapped by intuitive but flawed heuristics such
as associating first-person pronouns, spontaneous wording, or family topics
with human-written language. We experimentally demonstrate that these
heuristics make human judgment of AI-generated language predictable and
manipulable, allowing AI systems to produce language perceived as more human
than human. We discuss solutions, such as AI accents, to reduce the deceptive
potential of language generated by AI, limiting the subversion of human
intuition.
Related papers
- Distributed agency in second language learning and teaching through generative AI [0.0]
ChatGPT can provide informal second language practice through chats in written or voice forms.
Instructors can use AI to build learning and assessment materials in a variety of media.
arXiv Detail & Related papers (2024-03-29T14:55:40Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - The Generative AI Paradox: "What It Can Create, It May Not Understand" [81.89252713236746]
Recent wave of generative AI has sparked excitement and concern over potentially superhuman levels of artificial intelligence.
At the same time, models still show basic errors in understanding that would not be expected even in non-expert humans.
This presents us with an apparent paradox: how do we reconcile seemingly superhuman capabilities with the persistence of errors that few humans would make?
arXiv Detail & Related papers (2023-10-31T18:07:07Z) - Human or Machine? Turing Tests for Vision and Language [22.110556671410624]
We systematically benchmark current AIs in their abilities to imitate humans.
Experiments involved testing 769 human agents, 24 state-of-the-art AI agents, 896 human judges, and 8 AI judges.
Results reveal that current AIs are not far from being able to impersonate human judges across different genders, ages, and educational levels.
arXiv Detail & Related papers (2022-11-23T16:16:52Z) - What Artificial Neural Networks Can Tell Us About Human Language
Acquisition [47.761188531404066]
Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language.
To increase the relevance of learnability results from computational models, we need to train model learners without significant advantages over humans.
arXiv Detail & Related papers (2022-08-17T00:12:37Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Building Human-like Communicative Intelligence: A Grounded Perspective [1.0152838128195465]
After making astounding progress in language learning, AI systems seem to approach the ceiling that does not reflect important aspects of human communicative capacities.
This paper suggests that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI.
I propose a list of concrete, implementable components for building "grounded" linguistic intelligence.
arXiv Detail & Related papers (2022-01-02T01:43:24Z) - Words of Wisdom: Representational Harms in Learning From AI
Communication [9.998078491879143]
We contend that all language, including all AI communication, encodes information about the identity of the human or humans who contributed to crafting the language.
With AI communication, however, the user may index identity information that does not match the source.
This can lead to representational harms if language associated with one cultural group is presented as "standard" or "neutral"
arXiv Detail & Related papers (2021-11-16T15:59:49Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z) - Human Evaluation of Interpretability: The Case of AI-Generated Music
Knowledge [19.508678969335882]
We focus on evaluating AI-discovered knowledge/rules in the arts and humanities.
We present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects.
arXiv Detail & Related papers (2020-04-15T06:03:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.