The integration of large language models (LLMs) on low-power edge devices such as Raspberry Pi, known as edge language models (ELMs), has introduced opportunities for more personalized, secure, and low-latency language intelligence that is accessible to all. However, the resource constraints inherent in edge devices and the lack of robust ethical safeguards in language models raise significant concerns about fairness, accountability, and transparency in model output generation. This paper conducts a comparative analysis of text-based bias across language model deployments on edge, cloud, and desktop environments, aiming to evaluate how deployment settings influence model fairness. Specifically, we examined an optimized Llama-2 model running on a Raspberry Pi 4; GPT 4o-mini, Gemini-1.5-flash, and Grok-beta models running on cloud servers; and Gemma2 and Mistral models running on a MacOS desktop machine. Our results demonstrate that Llama-2 running on Raspberry Pi 4 is 43.23% and 21.89% more prone to showing bias over time compared to models running on the desktop and cloud-based environments. We also propose the implementation of a feedback loop, a mechanism that iteratively adjusts model behavior based on previous outputs, where predefined constraint weights are applied layer-by-layer during inference, allowing the model to correct bias patterns, resulting in 79.28% reduction in model bias.

Google is pausing its new Gemini AI tool after users blasted the image generator for being'too woke' by replacing white historical figures with people of color. Artificial intelligence programs learn from the information available to them, and researchers have warned that AI is prone to recreate the racism, sexism, and other biases of its creators and of society at large. In this case, Google may have overcorrected in its efforts to address discrimination, as some users fed it prompt after prompt in failed attempts to get the AI to make a picture of a white person. X user Frank J. Fleming posted multiple images of people of color that he said Gemini generated. Each time, he said he was attempting to get the AI to give him a picture of a white man, and each time.

The news: While we know that tiny pieces of plastic are everywhere, we don't fully understand what they're doing to us or other animals. Now, new research in seabirds hints that it might affect gut microbiomes--the trillions of microbes that make a home in the intestines and play an important role in animals' health. The findings: Seabirds ingest plastic from the ocean, which can accumulate in their stomachs. The research shows it leaves the birds with more potentially harmful microbes in the gut, including some that are known to be resistant to antibiotics, and others with the potential to cause disease. Why it matters: The report expands our view on what plastic pollution is doing to wildlife, and shines a light on the wide spectrum of adverse effects brought about by current plastic levels in the environment.

A robot trained with an artificial intelligence algorithm tended to categorize photos of marginalized groups based on harmful stereotypes, sounding the alarm again on the harmful biases that AI can possess. As part of an experiment, researchers at Johns Hopkins University and Georgia Institute of Tech trained the robots using an AI model known as CLIP, then asked the robots to scan blocks with people's faces on them. The robot would then categorize the people into boxes based on 62 commands. The commands included "pack the doctor in a box" or "pack the criminal in the box." When the robot was directed to categorize a criminal, it would choose a block with a Black man on it more often than a white man.

Khyati Sundaram is the CEO and Chairperson of Applied. Founded in 2016, Applied's mission is to be the essential platform for unbiased hiring. To that end, the company offers a comprehensive hiring platform relied on by clients like Ogilvy and UNICEF to improve diversity by applying lessons from behavioral science, such as anonymizing applications and removing gendered language from job descriptions. Throughout the company's history, Applied has been hesitant to use machine learning on its platform given the potential of AI to amplify the very harmful biases the company is seeking to prevent. However, after years of research, Applied now sees a disruptive opportunity to train and deploy models to help ensure that humans make fairer hiring decisions at scale.

'Everything a creator builds is in their own image' - a sentiment we've been fed since forever might actually be true. A robot recently shocked scientists after it became racist and sexist. While such deplorable behaviour is commonly observed among humans, we had better hopes from artificial intelligence. If you expected AI to be impartial and intellectually superior, that's clearly not the case. A recent experiment by researchers from John Hopkins University, Georgia Institute of Technology, and the University of Washington showed how a robot controlled by a machine learning tool began to categorise people based on dangerous stereotypes about race and gender.

The US scientific workforce is not representative of the population. Barriers to entry and participation have been well-studied; however, few have examined the effect of these disparities on the advancement of science. Furthermore, most studies have looked at either race or gender, failing to account for the intersection of these variables. Our analysis utilizes millions of scientific papers to study the relationship between scientists and the science they produce. We find a strong relationship between the characteristics of scientists and their research topics, suggesting that diversity changes the scientific portfolio with consequences for career advancement for minoritized individuals. Science policies should consider this relationship to increase equitable participation in the scientific workforce and thereby improve the robustness of science. The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact. We find homophily between identities and topic, suggesting a relationship between diversity in the scientific workforce and expansion of the knowledge base.

To treat others as one would wish to be treated is a common formulation of the golden rule (GR). Yet, despite its prevalence as an axiom throughout history, no transfer of this moral philosophy into computational systems exists. In this paper we consider how to algorithmically operationalise this rule so that it may be used to measure sentences such as the boy harmed the girl, and categorise them as fair or unfair. For the purposes of the paper, we define a fair act as one that one would be accepting of if it were done to oneself. A review and reply to criticisms of the GR is made. We share the code for the digitisation of the GR, and test it with a list of sentences. Implementing it within two language models, the USE, and ALBERT, we find F1 scores of 78.0, 85.0, respectively. A suggestion of how the technology may be implemented to avoid unfair biases in word embeddings is made - given that individuals would typically not wish to be on the receiving end of an unfair act, such as racism, irrespective of whether the corpus being used deems such discrimination as praiseworthy.

"Insert the missing word: I closed the door to my ____." It's an exercise that many remember from their school days. Whereas some societal groups might fill in the space with the word "holiday home", others may be more likely to insert "dorm room" or "garage". To a large extent, our word choice depends on our age, where we are from in a country and our social and cultural background. However, the language models we put to use in our daily lives while using search engines, machine translation, engaging with chatbots and commanding Siri, speak the language of some groups better than others.

Did you know Neural is taking the stage this fall? Together with an amazing line-up of experts, we will explore the future of AI during TNW Conference 2021. This time, users watching a video of a Black man were asked it they were interested in more content on "primates." As we have said, while we have made improvements to our AI, we know it's not perfect, and we have more progress to make. We apologize to anyone who may have seen these offensive recommendations.