AI Ethics Research Library.

This paper examines gender bias in LLMs-generated outputs for different languages using three measurements: 1) gender bias in selecting descriptive words given the gender-related context. 2) gender bias in selecting gender-related pronouns (she/he) given the descriptive words. 3) gender bias in the topics of LLM-generated dialogues.

This study analyzed images generated by three popular generative artificial intelligence (AI) tools - Midjourney, Stable Diffusion, and DALLE 2 - representing various occupations to investigate potential bias in AI generators. Their analysis revealed two overarching areas of concern in these AI generators, including (1) systematic gender and racial biases, and (2) subtle biases in facial expressions and appearances.

Technology-facilitated gender-based violence has become a global threat to women's political representation and democracy. Understanding how online hate affects its targets is thus paramount. This study analyzed 10 million tweets directed at female candidates in the Brazilian election in 2022 and examined their reactions to online misogyny.

Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. The reproduction of emotion stereotypes in LLMs raises questions about their predictive use for emotion applications.

Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, this study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Evaluation of three commercial gender classification systems revealed that darker-skinned females are the most misclassified group (with error rates of up to 34.7%).

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. This study shows that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent.

AI’s path has never been smooth, having essentially fallen apart twice in its lifetime (‘winters’ of AI), both after periods of popular success (‘summers’ of AI). Researchers provide a brief rundown of AI’s evolution over the course of decades, highlighting its crucial moments and major turning points from inception to the present. In doing so, we attempt to learn, anticipate the future, and discuss what steps may be taken to prevent another ‘winter’

Centering the research community as a key player in the development and proliferation of hype, this study examines the origins and risks of AI hype to the research community and society more broadly and propose a set of measures that researchers, regulators, and the public can take to mitigate these risks and reduce the prevalence of unfounded claims about the technology.

As governmental bodies rely on academics’ expert advice to shape policy regarding Artificial Intelligence, it is important that these academics not have conflicts of interests that may cloud or bias their judgement. Researchers explore how Big Tech can actively distort the academic landscape to suit its needs.

This paper examines gender bias in LLMs-generated outputs for different languages using three measurements: 1) gender bias in selecting descriptive words given the gender-related context. 2) gender bias in selecting gender-related pronouns (she/he) given the descriptive words. 3) gender bias in the topics of LLM-generated dialogues.

This study analyzed images generated by three popular generative artificial intelligence (AI) tools - Midjourney, Stable Diffusion, and DALLE 2 - representing various occupations to investigate potential bias in AI generators. Their analysis revealed two overarching areas of concern in these AI generators, including (1) systematic gender and racial biases, and (2) subtle biases in facial expressions and appearances.

Technology-facilitated gender-based violence has become a global threat to women's political representation and democracy. Understanding how online hate affects its targets is thus paramount. This study analyzed 10 million tweets directed at female candidates in the Brazilian election in 2022 and examined their reactions to online misogyny.

Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. The reproduction of emotion stereotypes in LLMs raises questions about their predictive use for emotion applications.

Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, this study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Evaluation of three commercial gender classification systems revealed that darker-skinned females are the most misclassified group (with error rates of up to 34.7%).

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. This study shows that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent.