Dr Asress Gikay argues that an outright ban on police use of live facial recognition technology would be a mistake. UK police are being accused of breaking ethical standards by using live facial recognition technology to help fight crime. A recent report by the University of Cambridge into trials of the technology by forces in London and South Wales was particularly concerned about the "lack of robust redress" for anyone suffering harm. It spoke of the need to "protect human rights and improve accountability" before facial recognition is used more widely. The Cambridge team wants a broad ban on police using the technology, and they are not alone.

Companies should think twice before deploying AI-powered emotional analysis systems prone to systemic biases and other snafus, the UK's Information Commissioner's Office (ICO) warned this week. Organizations face investigation if they press on and use this sub-par technology that puts people at risk, the watchdog added. Machine-learning algorithms purporting to predict a person's moods and reactions use computer vision to track gazes, facial movements, and audio processing to gauge inflection and overall sentiment. As one might imagine, it's not necessarily accurate or fair, and there may be privacy problems handling data for training and inference.

Artificial intelligence (AI) is typically portrayed in two diametrically opposite ways; it is either the great hope for business technology, or AI is an existential threat to the way we live. As is often the case, the middle way is closer to the truth. Other than some mooted regulations from the European Union, AI, just like social media, is growing so rapidly that the technology industry itself will need to pioneer the ethical standards that enable AI but also protect civil liberties. With its research and development history, Intel has been one of the first big tech firms to define the foundations of ethical AI. Launched last month, the Intel Responsible AI charter sets out the chip giant's perspective on responsible AI and the four pillars the firm says the technology industry can use to ensure AI succeeds not only for technologists but also for wider society.

Elon Musk has a lot going on right now. In the week since he bought Twitter for $44 billion, he fired its upper management and laid off thousands of employees, declared himself CEO and sole director, and used tweets and memes to publicly thrash out a dubious business plan that involves charging users $8 a month for benefits that include a blue check mark. All that is just Musk's new side gig. The world's richest man is perhaps now its busiest, revamping Twitter while also serving as CEO of electric-auto maker Tesla; rocket maker SpaceX; the Boring Company, which digs tunnels for transit; and Neuralink, which is testing brain implants meant to eventually connect humans with computers. How does Musk manage and lead all those complex projects at the same time?

OpenAI's DALL-E produced this image when prompted with the title of this article ("The Biggest ... [ ] Opportunity In Generative AI Is Language, Not Images"). The buzz around generative AI today is deafening. Generative AI refers to artificial intelligence that can generate novel content, rather than simply analyzing or acting on existing data. No topic in the world of technology is attracting more attention and hype right now. The white-hot epicenter of today's generative AI craze has been text-to-image AI. Text-to-image AI models generate detailed original images based on simple written inputs. The most well-known of these models include Stable Diffusion, Midjourney and OpenAI's DALL-E.

With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during the overall interaction with an item, from its selection to its usage. Thus, they cannot assess their impact on the user's experience. To address this issue, we propose a novel justification approach that uses service models to (i) extract experience data from reviews concerning all the stages of interaction with items, at different granularity levels, and (ii) organize the justification of recommendations around those stages. In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results. The participants evaluated the Perceived User Awareness Support provided by our service-based justification models higher than the one offered by the baselines. Moreover, our models received higher Interface Adequacy and Satisfaction evaluations by users having different levels of Curiosity or low Need for Cognition (NfC). Differently, high NfC participants preferred a direct inspection of item reviews. These findings encourage the adoption of service models to justify recommender systems results but suggest the investigation of personalization strategies to suit diverse interaction needs.

Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.

Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content. Besides this approach, counter narratives have emerged as an effective tool employed by NGOs to respond to online hate on social media platforms. For this reason, Natural Language Generation is currently being studied as a way to automatize counter narrative writing. However, the existing resources necessary to train NLG models are limited to 2-turn interactions (a hate speech and a counter narrative as response), while in real life, interactions can consist of multiple turns. In this paper, we present a hybrid approach for dialogical data collection, which combines the intervention of human expert annotators over machine generated dialogues obtained using 19 different configurations. The result of this work is DIALOCONAN, the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator, covering 6 targets of hate.

Post-processing ensemble prediction systems can improve the reliability of weather forecasting, especially for extreme event prediction. In recent years, different machine learning models have been developed to improve the quality of weather post-processing. However, these models require a comprehensive dataset of weather simulations to produce high-accuracy results, which comes at a high computational cost to generate. This paper introduces the ENS-10 dataset, consisting of ten ensemble members spanning 20 years (1998-2017). The ensemble members are generated by perturbing numerical weather simulations to capture the chaotic behavior of the Earth. To represent the three-dimensional state of the atmosphere, ENS-10 provides the most relevant atmospheric variables at 11 distinct pressure levels and the surface at 0.5-degree resolution for forecast lead times T=0, 24, and 48 hours (two data points per week). We propose the ENS-10 prediction correction task for improving the forecast quality at a 48-hour lead time through ensemble post-processing. We provide a set of baselines and compare their skill at correcting the predictions of three important atmospheric variables. Moreover, we measure the baselines' skill at improving predictions of extreme weather events using our dataset. The ENS-10 dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Almost every confidential investment memorandum (CIM) for a tech-driven enterprise includes the company's mention of artificial intelligence (AI) or machine learning (ML) capabilities. But as with other investment buzzwords -- such as "subscription revenue" -- there is a tendency to use AI or ML to suggest complex, business-enabling, proprietary technology and processes to distinguish the offering as differentiated or technologically superior. This is often to garner higher valuation. We've all heard examples of AI failures that make for good headlines and provide interesting cautionary tales.