Government enlists AI tools to flag'destructive' content Increasing number of Russians face court over social media posts Activists fear AI will be used to stifle dissent The company and law firm names shown above are generated automatically based on the text of the article. We are improving this feature as we continue to test and develop in beta. We welcome feedback, which you can provide using the feedback tab on the right of the page. TBILISI, Nov 30 (Thomson Reuters Foundation) - A woman posing in a thong outside a church; a single mother who berated Russian lawmakers and President Vladimir Putin; a saxophonist who criticised World War Two commemorations. They are among thousands of Russians who have faced court over their social media posts in the past year - a number digital rights groups say could soon turn into a deluge as authorities use artificial intelligence (AI) to police the web.

The topic of AI fairness metrics is as important to society as it is confusing. Confusing it is due to a number of reasons: terminological proliferation, abundance of formulae, and last not least the impression that everyone else seems to know what they're talking about. This text hopes to counteract some of that confusion by starting from a common-sense approach of contrasting two basic positions: On the one hand, the assumption that dataset features may be taken as reflecting the underlying concepts ML practitioners are interested in; on the other, that there inevitably is a gap between concept and measurement, a gap that may be bigger or smaller depending on what is being measured. In contrasting these fundamental views, we bring together concepts from ML, legal science, and political philosophy.

Civil society has been poring over the detail of the European Commission's proposal for a risk-based framework for regulating applications of artificial intelligence which was proposed by the EU's executive back in April. The verdict of over a hundred civil society organizations is that the draft legislation falls far short of protecting fundamental rights from AI-fuelled harms like scaled discrimination and blackbox bias -- and they've published a call for major revisions. "We specifically recognise that AI systems exacerbate structural imbalances of power, with harms often falling on the most marginalised in society. As such, this collective statement sets out the call of 11[5] civil society organisations towards an Artificial Intelligence Act that foregrounds fundamental rights," they write, going on to identify nine "goals" (each with a variety of suggested revisions) in the full statement of recommendations. The Commission, which drafted the legislation, billed the AI regulation as a framework for "trustworthy", "human-centric" artificial intelligence.

ISLAMABAD: A meeting on broadening of tax base was informed that the Federal Board of Revenue (FBR) has provided 14 million records of financial transactions of citizens to the National Database and Registration Authority (NADRA) to compute indicative income and tax liability of non-filers by use of artificial intelligence. The meeting was presided over by Adviser to the Prime Minister on Finance Shaukat Tarin on Monday. The FBR chairman and his team gave a detailed presentation on the progress on readiness for potential taxpayer outreach initiative to boost the revenue growth and resource mobilisation. The FBR chairman apprised the adviser that steps have been initiated for compilation of data, with the support of the NADRA, which would be available to potential and current taxpayers in a presentable and comprehensible manner through a web portal. According to Business Recorder, the 14 million financial records included property transactions, vehicle purchases, registration of cars with provincial excise departments, buying/selling of movable and immovable properties, utility bills, foreign travels, and other heavy expenditures.

Clearview AI is back in hot water, this time from the UK's Information Commissioner's Office (ICO). The controversial facial recognition giant has caught the attention of global privacy regulators and campaigners for its practice of scraping personal photos from the web for its system without explicit consent. Clearview AI is expected to have scraped over 10 billion photos. "Common law has never recognised a right to privacy for your face," Clearview AI lawyer Tor Ekeland once argued. The UK's ICO launched a joint probe with the Office of the Australian Information Commissioner (OAIC) into Cleaview AI's practices. Earlier this month, Australia's Information Commissioner Angelene Falk determined that "the act of uploading an image to a social media site does not unambiguously indicate agreement to collection of that image by an unknown third party for commercial purposes."

There is an increasing demand for financial crime mitigation and regulatory compliance solutions that take care of the future demands of the financial services industry. Adapting the AML processes and platforms to combat the changing ways of criminal activities has become even more critical as financial crimes grow using banking channels. Given the need for disruptive technological enhancements, the global regulatory bodies are recommending automation enhanced by machine learning. In the latest report by FATF that outlines the improvements financial institutions must make in their respective jurisdictions, it is evident that they have paid close attention to transaction monitoring. Since global digital transaction volumes have grown so much in the last decade, transaction monitoring costs are dramatically increasing as well. This also burdens compliance officers since investigating the number of suspicious transactions takes a toll.

Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector. Since the electrification of transport is happening simultaneously, autonomous marine vessels can reduce environmental impact, lower costs, and increase efficiency. Although close monitoring is still required to ensure safety, the ultimate goal is full autonomy. One major milestone is to develop a control system that is versatile enough to handle any weather and encounter that is also robust and reliable. Additionally, the control system must adhere to the International Regulations for Preventing Collisions at Sea (COLREGs) for successful interaction with human sailors. Since the COLREGs were written for the human mind to interpret, they are written in ambiguous prose and therefore not machine-readable or verifiable. Due to these challenges and the wide variety of situations to be tackled, classical model-based approaches prove complicated to implement and computationally heavy. Within machine learning (ML), deep reinforcement learning (DRL) has shown great potential for a wide range of applications. The model-free and self-learning properties of DRL make it a promising candidate for autonomous vessels. In this work, a subset of the COLREGs is incorporated into a DRL-based path following and obstacle avoidance system using collision risk theory. The resulting autonomous agent dynamically interpolates between path following and COLREG-compliant collision avoidance in the training scenario, isolated encounter situations, and AIS-based simulations of real-world scenarios.

AVEVA launched Vision AI Assistant 2021, its new image classification-based analytics tool. This artificial intelligence (AI) solution, which can be integrated into AVEVA System Platform and Operations Management Interface and AVEVA Insight, helps enable its customers to use video and images from existing general-purpose cameras and convert them into user-friendly information and timely alerts. Designed for low latency industrial environments, Vision AI Assistant leverages deep learning to train and deploy 24x7 artificial intelligence models to enhance situational awareness to help operators increase their focus on the task at hand without continuously monitoring live camera feeds. Vision AI Assistant helps enable organizations to improve operations, quality, and maintenance capabilities across industrial enterprises by using cameras to determine visual anomalies and assess quality, all while more vigorously monitoring production, optimizing efficiencies and improving sustainability in the process. Standard digital cameras can be used with Vision AI Assistant to better ensure quality control, such as, for example, by analyzing the quality of water treatment fluids, by gauging if a hopper is full or empty, or by identifying defective products on a production line.

UK data protection legislation does not stop the effective use of technology to fight crime. But to enjoy public trust and confidence in their products, technology providers must ensure people's legal protections are respected and complied with.

The United Nations organization said developments in artificial intelligence should abide by the rule of law, avoid harm, and ensure that when harm happens, accountability and redress mechanisms are available for those affected. Audrey Azoulay, Director-General of UNESCO, presented Thursday the first ever global standard on the ethics of artificial intelligence (AI) adopted by the member states of UNESCO at the General Conference. This historical text defines the common values and principles which will guide the construction of the necessary legal infrastructure to ensure the healthy development of AI. AI is pervasive, and enables many of our daily routines: booking flights, steering driverless cars, and personalising our morning news feeds. AI also supports the decision-making of governments and the private sector. AI technologies are delivering remarkable results in highly specialized fields such as cancer screening and building inclusive environments for people with disabilities.