According to the theory of International Political Economy (IPE), states are often incentivized to rely on rather than constrain powerful corporations. For this reason, IPE provides a useful lens to explain why efforts to govern Artificial Intelligence (AI) at the international and national levels have thus far been developed, applied, and enforced unevenly. Building on recent work that explores how AI companies engage in geopolitics, this position paper argues that some AI workers can be considered actors of geopolitics. It makes the timely case that governance alone cannot ensure responsible, ethical, or robust AI development and use, and greater attention should be paid to bottom-up interventions at the site of AI development. AI workers themselves should be situated as individual agents of change, especially when considering their potential to foster Algorithmic Collective Action (ACA). Drawing on methods of Participatory Design (PD), this paper proposes engaging AI workers as sources of knowledge, relative power, and intentionality to encourage more responsible and just AI development and create the conditions that can facilitate ACA.

AI workers said they distrust the models they work on because of a consistent emphasis on rapid turnaround time at the expense of quality. AI workers said they distrust the models they work on because of a consistent emphasis on rapid turnaround time at the expense of quality. K rista Pawloski remembers the single defining moment that shaped her opinion on the ethics of artificial intelligence . As an AI worker on Amazon Mechanical Turk - a marketplace that allows companies to hire workers to perform tasks like entering data or matching an AI prompt with its output - Pawloski spends her time moderating and assessing the quality of AI-generated text, images and videos, as well as some factchecking. Roughly two years ago, while working from home at her dining room table, she took up a job designating tweets as racist or not. When she was presented with a tweet that read "Listen to that mooncricket sing", she almost clicked on the "no" button before deciding to check the meaning of the word "mooncricket", which, to her surprise, was a racial slur against Black Americans.

Recently, an increasing number of news organizations have integrated artificial intelligence (AI) into their workflows, leading to a further influx of AI technologists and data workers into the news industry. This has initiated cross-functional collaborations between these professionals and journalists. While prior research has explored the impact of AI-related roles entering the news industry, there is a lack of studies on how cross-functional collaboration unfolds between AI professionals and journalists. Through interviews with 17 journalists, 6 AI technologists, and 3 AI workers with cross-functional experience from leading news organizations, we investigate the current practices, challenges, and opportunities for cross-functional collaboration around AI in today's news industry. We first study how journalists and AI professionals perceive existing cross-collaboration strategies. We further explore the challenges of cross-functional collaboration and provide recommendations for enhancing future cross-functional collaboration around AI in the news industry.

The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs). Therefore, the advancement of parallel intelligence-powered Crowdsensing Intelligence (CSI) is witnessed, which is currently advancing towards linguistic intelligence. In this paper, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0. It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., inter-human, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multi-agent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential industrial applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.

The size of an economy is measured by GDP (Gross Domestic Product). GDP is calculated by adding up the value of all goods and services produced within a country's borders in a given year. This includes the value of goods and services produced by both the government and the private sector. GDP is typically measured in monetary terms, using current market prices for goods and services. GDP is used as a measure of the size and strength of an economy, as well as its overall level of economic activity.

Artificial intelligence research leads to new cutting-edge technologies, but it's expensive. Big Tech companies, which are powered by AI and have deep pockets, often take on this work -- but that gives them the power to censor or impede research that casts them in an unfavorable light, according to Timnit Gebru, a computer scientist, co-founder of the nonprofit organization Black in AI and the former co-leader of Google's Ethical AI team. The situation imperils both the rights of AI workers at those companies and the quality of research that is shared with the public, said Gebru, speaking at the recent EmTech MIT conference hosted by MIT Technology Review. "It's all the incentive structures that are not in place for you to challenge the status quo," she said. Gebru was forced out at Google last December (Gebru said she was fired, while Google said she resigned) after co-writing a paper about the risks of large AI language models, such as environmental impacts and the difficulty in finding embedded biases.

Artificial intelligence (AI) has crossed a threshold. "In the past five years, AI has made the leap from something that mostly happens in research labs or other highly controlled settings to something that's out in society affecting people's lives," says Michael Littman, chair of the One Hundred Year Study on Artificial Intelligence, hosted at Stanford. It's easy to see what he's talking about: The technology's impact can be seen introducing automation, driving efficiency gains and enhancing productivity, creating new jobs, and reducing risks associated with cyber-threats and fraud. During the pandemic, AI enabled more effective testing for Covid-19 and faster vaccine development, and helped manage grocery supply chains and tailor lessons for individual students affected by remote schooling. As AI expands into more and more facets of our lives, there is also more scrutiny on who's developing it.

VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture. The 2020 LinkedIn U.S. Emerging Jobs Report identified the top 15 jobs over the previous five years and emphasized that "artificial intelligence and data science roles continue to proliferate across nearly every industry." Artificial intelligence specialist (No. 1) showed 74% annual growth, and data scientist (No. 3) and data engineer (No. 8) followed with 37% and 33% annual growth. But the problem is: we don't have enough skilled talent to fill these jobs, and it's a national imperative that we change the way we imagine, educate, recruit and upskill our technical workforce. We must abandon the flawed idea that AI jobs are only for people with master's degrees or PhDs with decades of experience.

The Republic has the highest ratio of artificial intelligence (AI) talent in the European Union, according to a new study from LinkedIn. The report comes as the recruitment website is itself planning to hire 20 additional AI specialists locally with the promise of more jobs in the area in the future. The study reveals that half of all Europe's AI workers are based in just three countries: the United Kingdom, France, and Germany – with the UK leading by a significant margin. But, on a per capita basis, Ireland is far and away the top nation for AI talent, followed by Finland, Cyprus, Luxembourg and Sweden. LinkedIn attributed Ireland's leading position to a number of factors, including the high number of tech companies that have established centres on data analytics, machine learning and big data locally.

"AI will fundamentally change the way we live and work. Therefore, we need to get it right and develop this technology in a way, which ensures the trust and security of our citizens while benefitting our economy," a commission spokesperson told EUobserver. In the EU, the largest and most well-established companies are likely to become first adopters of AI technologies, such as automotive companies in Germany or finance firms in the UK. However, the LinkedIn findings suggest that the current market ecosystem for AI in Europe is uneven across both gender and demographic lines. The EU Commission president-elect Ursula von der Leyen has promised that during the first three months in office, the college of commissioners will put forward legislation for a "coordinated approach on the human and ethical implication of AI".