A sitting is a meeting of parliament members. While in the virtual environment, you will need to install the specific Gensim1 version needed for theCompassapproach. Inotherinstances,thebeginning of the line that specifies the speaker consists of the role of the parliament member, for example "SPEAKEROFTHEPARLIAMENT" (meaning the member of parliament presiding), followed, but not always, by the actual full name of the person in parenthesis. Theidisa unique number we assigned to each file. Themainchallenge of translating the files from Greek to English was the conversion of the Greek alphabetic numeralstoindo-arabicnumerals.

Current AI systems minimize risk by enforcing ideological neutrality, yet this may introduce automation bias by suppressing cognitive engagement in human decision-making. We conducted randomized trials with 2,500 participants to test whether culturally biased AI enhances human decision-making. Participants interacted with politically diverse GPT-4o variants on information evaluation tasks. Partisan AI assistants enhanced human performance, increased engagement, and reduced evaluative bias compared to non-biased counterparts, with amplified benefits when participants encountered opposing views. These gains carried a trust penalty: participants underappreciated biased AI and overcredited neutral systems. Exposing participants to two AIs whose biases flanked human perspectives closed the perception-performance gap. These findings complicate conventional wisdom about AI neutrality, suggesting that strategic integration of diverse cultural biases may foster improved and resilient human decision-making.

The dataset includes 1,280,918 speech fragments of Greek parliament members in debate order exported from 5,355 parliamentary sitting record files, with a total volume of 2.12 GB. The speeches extend chronologically from July 1989 up to July 2020. Table 1 shows the contents of the dataset. The names of the speakers are provided in the format "last_name patronym first_name (nickname)". In cases with more than one first or last names, the names that belong to the same category (first or last) are connected with a dash, e.g., "merk-ouri stamatiou amalia-maria (melina)". A parliamentary period is defined as the time span between one general election and the next. A parliamentary period includes multiple parliamentary sessions. A session is a time span of usually 10 months within a parliamentary period during which the parliament can convene and function as stipulated by the constitution.

This week in tech news, Elon Musk and Donald Trump are back at it, warring over the passage of the president's sweeping tax bill and the Tesla CEO's threat to create a third political party. Whether the richest person in the world is successful in those efforts will largely depend on the recruitment of another star politician. In other news, we want to know if you use generative artificial intelligence to write your personal messages – in what circumstances, and how often? Email tech.editorial@theguardian.com to let us know. Elon Musk and Donald Trump have reignited their feud after the passage of the president's sweeping tax bill on 3 July.

The advent of the COVID-19 pandemic has undoubtedly affected the political scene worldwide and the introduction of new terminology and public opinions regarding the virus has further polarized partisan stances. Using a collection of tweets gathered from leading American political figures online (Republican and Democratic), we explored the partisan differences in approach, response, and attitude towards handling the international crisis. Implementation of the bag-of-words, bigram, and TF-IDF models was used to identify and analyze keywords, topics, and overall sentiments from each party. Results suggest that Democrats are more concerned with the casualties of the pandemic, and give more medical precautions and recommendations to the public whereas Republicans are more invested in political responsibilities such as keeping the public updated through media and carefully watching the progress of the virus. We propose a systematic approach to predict and distinguish a tweet's political stance (left or right leaning) based on its COVID-19 related terms using different classification algorithms on different language models.

With the increasing prevalence of artificial intelligence, careful evaluation of inherent biases needs to be conducted to form the basis for alleviating the effects these predispositions can have on users. Large language models (LLMs) are predominantly used by many as a primary source of information for various topics. LLMs frequently make factual errors, fabricate data (hallucinations), or present biases, exposing users to misinformation and influencing opinions. Educating users on their risks is key to responsible use, as bias, unlike hallucinations, cannot be caught through data verification. We quantify the political bias of popular LLMs in the context of the recent vote of the German Bundestag using the score produced by the Wahl-O-Mat. This metric measures the alignment between an individual's political views and the positions of German political parties. We compare the models' alignment scores to identify factors influencing their political preferences. Doing so, we discover a bias toward left-leaning parties, most dominant in larger LLMs. Also, we find that the language we use to communicate with the models affects their political views. Additionally, we analyze the influence of a model's origin and release date and compare the results to the outcome of the recent vote of the Bundestag. Our results imply that LLMs are prone to exhibiting political bias. Large corporations with the necessary means to develop LLMs, thus, knowingly or unknowingly, have a responsibility to contain these biases, as they can influence each voter's decision-making process and inform public opinion in general and at scale.

Examining the alignment of large language models (LLMs) has become increasingly important, particularly when these systems fail to operate as intended. This study explores the challenge of aligning LLMs with human intentions and values, with specific focus on their political inclinations. Previous research has highlighted LLMs' propensity to display political leanings, and their ability to mimic certain political parties' stances on various issues. However, the extent and conditions under which LLMs deviate from empirical positions have not been thoroughly examined. To address this gap, our study systematically investigates the factors contributing to LLMs' deviations from empirical positions on political issues, aiming to quantify these deviations and identify the conditions that cause them. Drawing on cognitive science findings related to representativeness heuristics -- where individuals readily recall the representative attribute of a target group in a way that leads to exaggerated beliefs -- we scrutinize LLM responses through this heuristics lens. We conduct experiments to determine how LLMs exhibit stereotypes by inflating judgments in favor of specific political parties. Our results indicate that while LLMs can mimic certain political parties' positions, they often exaggerate these positions more than human respondents do. Notably, LLMs tend to overemphasize representativeness to a greater extent than humans. This study highlights the susceptibility of LLMs to representativeness heuristics, suggeseting potential vulnerabilities to political stereotypes. We propose prompt-based mitigation strategies that demonstrate effectiveness in reducing the influence of representativeness in LLM responses.

From fake images designed to cause fears of an immigrant "invasion" to other demonisation campaigns targeted at leaders such as Emmanuel Macron, far-right parties and activists across western Europe are at the forefront of the political weaponisation of generative artificial intelligence technology. This year's European parliamentary elections were the launchpad for a rollout of AI-generated campaigning by the European far right, experts say, which has continued to proliferate since. This month, the issue reached the independent oversight board of Mark Zuckerberg's Meta when the body opened an investigation into anti-immigration content on Facebook. The inquiry by the oversight board will look at a post from a German account featuring an AI-generated image emblazoned with anti-immigrant rhetoric. It is part of a wave of AI-made rightwing content on social media networks.