EU opens antitrust investigation into Google's AI practices The European Commission is looking into Google's lack of compensation for publishers and YouTube creators. Google is no stranger to scrutiny from government bodies such as the US Federal Trade Commission (FTC) the UK Competition and Markets Authority (CMA), and the European Commission . Now it can add another probe to its list: The European Commission has opened an antitrust investigation into the company surrounding the content used for its AI tools. Namely, the Commission is looking into two things, starting with whether Google used web publisher's content for its AI Overview and AI Mode services -- without appropriate compensation or the option to refuse the use of their materials. The Commission will investigate to what extent the generation of AI Overviews and AI Mode by Google is based on web publishers' content without appropriate compensation for that, and without the possibility for publishers to refuse without losing access to Google Search, the EU executive body stated in its announcement.

With the ever-growing adoption of artificial intelligence (AI), AI-based software and its negative impact on the environment are no longer negligible, and studying and mitigating this impact has become a critical area of research. However, it is currently unclear which role environmental sustainability plays during AI adoption in industry and how AI regulations influence Green AI practices and decision-making in industry. We therefore aim to investigate the Green AI perception and management of industry practitioners. To this end, we conducted a total of 11 interviews with participants from 10 different organizations that adopted AI-based software. The interviews explored three main themes: AI adoption, current efforts in mitigating the negative environmental impact of AI, and the influence of the EU AI Act and the Corporate Sustainability Reporting Directive (CSRD). Our findings indicate that 9 of 11 participants prioritized business efficiency during AI adoption, with minimal consideration of environmental sustainability. Monitoring and mitigation of AI's environmental impact were very limited. Only one participant monitored negative environmental effects. Regarding applied mitigation practices, six participants reported no actions, with the others sporadically mentioning techniques like prompt engineering, relying on smaller models, or not overusing AI. Awareness and compliance with the EU AI Act are low, with only one participant reporting on its influence, while the CSRD drove sustainability reporting efforts primarily in larger companies. All in all, our findings reflect a lack of urgency and priority for sustainable AI among these companies. We suggest that current regulations are not very effective, which has implications for policymakers. Additionally, there is a need to raise industry awareness, but also to provide user-friendly techniques and tools for Green AI practices.

Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual implementation of algorithmic transparency by organizations. In this work, we test an approach for addressing the challenge by creating transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency. Over several years, we created an open-source educational workshop on algorithmic transparency and advocacy. We delivered the workshop to professionals across two separate domains to improve their algorithmic transparency literacy and willingness to advocate for change. In the weeks following the workshop, participants applied what they learned, such as speaking up for algorithmic transparency at an organization-wide AI strategy meeting. We also make two broader observations: first, advocacy is not a monolith and can be broken down into different levels. Second, individuals' willingness for advocacy is affected by their professional field. For example, news and media professionals may be more likely to advocate for algorithmic transparency than those working at technology start-ups.

Scientific research organizations that are developing and deploying Artificial Intelligence (AI) systems are at the intersection of technological progress and ethical considerations. The push for Responsible AI (RAI) in such institutions underscores the increasing emphasis on integrating ethical considerations within AI design and development, championing core values like fairness, accountability, and transparency. For scientific research organizations, prioritizing these practices is paramount not just for mitigating biases and ensuring inclusivity, but also for fostering trust in AI systems among both users and broader stakeholders. In this paper, we explore the practices at a research organization concerning RAI practices, aiming to assess the awareness and preparedness regarding the ethical risks inherent in AI design and development. We have adopted a mixed-method research approach, utilising a comprehensive survey combined with follow-up in-depth interviews with selected participants from AI-related projects. Our results have revealed certain knowledge gaps concerning ethical, responsible, and inclusive AI, with limitations in awareness of the available AI ethics frameworks. This revealed an overarching underestimation of the ethical risks that AI technologies can present, especially when implemented without proper guidelines and governance. Our findings reveal the need for a holistic and multi-tiered strategy to uplift capabilities and better support science research teams for responsible, ethical, and inclusive AI development and deployment.

Sword is a leader in data insights, digital transformation, and technology services with a substantial reputation in software development, complex business IT projects and mission critical operations. With over 2,500 Technology, Digital & Software specialists working globally, we unlock solutions to the most critical business technology challenges. Working within our Professional Service Business Unit as a Data Engineer, this position will suit someone who can provide architectural and data engineering subject matter expertise in our client projects. You will join a highly experienced team in our Data & AI practice who are supporting Sword's clients on their journey to become data driven organisations. Sword offers career paths in rapidly evolving technology spaces including Data & AI, Modern Managed Services, Information Management, Digital Services, Content Services, and Modern Workplace Transformation.

AI has been hitting the headlines recently, with generative AI, in particular, generating a great deal of interest. Two tools - the large language model chatbot ChatGPT and image generator Dall-E - have caused a big stir since launching as public betas in recent months. These can be thought of as the current cutting-edge, public-facing applications of AI. However, as they are both free to use, their creator – AI research organization OpenAI – has been open about the fact that in order to be sustainable, they will have to start making money at some point. When it comes to commercializing AI technology today, businesses are generally following one of two strategies.

Ethical AI will need careful planting of many ecosystems. Ethical AI has been a concern of AI leaders, and practitioners for many years, but finally it seems, global jurisdictions are starting to move from policy formulation and stakeholder engagement to putting some teeth into drafting legal bills or acts. Expect many new laws to pass in 2023, tightening up citizen privacy and creating risk frameworks and audit requirements for data bias, privacy and security risks. At the same time, regulators are going to have to evolve an entire global ecosystem to ensure AI audits are effectively conducted and many questions loom as to who will validate certifications for AI audit practices and will we over burden AI innovations like we have done in so many other regulated operating practices that the risk and costs of non-conformance inhibit's innovation and capital funding? Finding a balance will be key.

AI has fueled efficiencies across industries for years. It's old news by now, but as I've said before, that's a good thing. Conversations about AI sound much different today than they did 10 years ago. Instead of wondering whether AI will help businesses grow or increase bottom lines, the proliferation of the technology has pushed AI conversations in more meaningful and complex directions. One area I'm particularly interested in is data privacy and biases in AI models.

As enterprises move beyond the pilot stage to scaling and operationalizing artificial intelligence, one tech giant is changing the way its AI operations are organized within the company. Meta (Facebook's parent) announced in early June that it would decentralize AI at the company, distributing ownership of it into Meta's product groups, according to CTO Andrew Bosworth. "We believe that this will accelerate the adoption of important new technology across the company while allowing us to push the envelope," Bosworth wrote in his post announcing the change. The announcement signals a shakeup of how AI is organized at Meta, with the VP of AI Jerome Pesenti leaving the company and other changes such as the consolidation of several separate AI teams. The changes at Meta beg the question for other forward-thinking enterprises across industries: 'Is Meta's AI reorg the example to follow? How should we think about structuring our own artificial intelligence research and operations?' Often, enterprise organizations get their start with AI as an initiative driven by a single business unit.

This article is part of our coverage of the latest in AI research. Artificial intelligence research lab OpenAI made headlines again, this time with DALL-E 2, a machine learning model that can generate stunning images from text descriptions. DALL-E 2 builds on the success of its predecessor DALL-E and improves the quality and resolution of the output images thanks to advanced deep learning techniques. The announcement of DALL-E 2 was accompanied by a social media campaign by OpenAI's engineers and its CEO, Sam Altman, who shared wonderful photos created by the generative machine learning model on Twitter. DALL-E 2 shows how far the AI research community has come toward harnessing the power of deep learning and addressing some of its limits.