3,127 research outputs found
Language Models have a Moral Dimension
Artificial writing is permeating our lives due to recent advances in
large-scale, transformer-based language models (LMs) such as BERT, its
variants, GPT-2/3, and others. Using them as pretrained models and fine-tuning
them for specific tasks, researchers have extended the state of the art for
many NLP tasks and shown that they not only capture linguistic knowledge but
also retain general knowledge implicitly present in the data. These and other
successes are exciting. Unfortunately, LMs trained on unfiltered text corpora
suffer from degenerate and biased behaviour. While this is well established, we
show that recent improvements of LMs also store ethical and moral values of the
society and actually bring a ``moral dimension'' to surface: the values are
capture geometrically by a direction in the embedding space, reflecting well
the agreement of phrases to social norms implicitly expressed in the training
texts. This provides a path for attenuating or even preventing toxic
degeneration in LMs. Since one can now rate the (non-)normativity of arbitrary
phrases without explicitly training the LM for this task, the moral dimension
can be used as ``moral compass'' guiding (even other) LMs towards producing
normative text, as we will show
The Cinderella Complex: Word Embeddings Reveal Gender Stereotypes in Movies and Books
Our analysis of thousands of movies and books reveals how these cultural
products weave stereotypical gender roles into morality tales and perpetuate
gender inequality through storytelling. Using the word embedding techniques, we
reveal the constructed emotional dependency of female characters on male
characters in stories
Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
An indigenous perspective on the effectiveness of debiasing techniques for
pre-trained language models (PLMs) is presented in this paper. The current
techniques used to measure and debias PLMs are skewed towards the US racial
biases and rely on pre-defined bias attributes (e.g. "black" vs "white"). Some
require large datasets and further pre-training. Such techniques are not
designed to capture the underrepresented indigenous populations in other
countries, such as M\=aori in New Zealand. Local knowledge and understanding
must be incorporated to ensure unbiased algorithms, especially when addressing
a resource-restricted society.Comment: accepted with invite to presen
Automatically Detecting the Resonance of Terrorist Movement Frames on the Web
The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups
An experimental and computational study of thick terms
Thick terms like âcourageousâ, âsmartâ and âtastyâ combine description and evaluation. These terms are contrasted with thin evaluative terms like âgoodâ and âbadâ and descriptive terms like âItalianâ and âgreenâ. While this contrast raises several questions about the distinction between facts and values and the objectivity of evaluative language, it is unclear how thick terms combine description and evaluation. Here we contribute to address this issue with two experiments involving a cancellability task and a Cloze task, coupled with computational modelling. We found that wordsâ affective valence and co-occurrence patterns extracted from large corpora of natural language both reliably predict thick termsâ cancellability and cloze effects. This finding highlights the key role of automatic valenced associations, presumably acquired through experience of co-occurrent words within a shared cultural milieu, in explaining how thick terms combine evaluation and description
Ethical Machines?
This Article explores the possibility of having ethical artificial intelligence. It argues that we face a dilemma in trying to develop artificial intelligence that is ethical: either we have to be able to codify ethics as a set of rules or we have to value a machineâs ability to make ethical mistakes so that it can learn ethics like children do. Neither path seems very promising, though perhaps by thinking about the difficulties with each we may come to a better understanding of artificial intelligence and ourselves
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