To the uninitiated, a tiny stain on several yards of car seat upholstery or a minuscule gas bubble on the surface of an industrial oil pipe might seem like an insignificant imperfection. But factory inspectors are always on the lookout for these sorts of defects, because they can create serious slowdowns in time-sensitive manufacturing production schedules. Cameras and computer vision software have been used to spot product flaws in manufacturing facilities for decades, but today companies including Landing AI and Mariner are helping take defect detection to the next level with AI software. Rather than offering off-the-shelf AI, these companies are betting that manufacturers want highly customized algorithmic models to monitor for product defects. And they have another selling point that flies in the face of what we know about most big data-hungry AI systems: Their models work using very small datasets.

In May 2021, Twitter, a platform notorious for abuse and hot-headedness, rolled out a "prompts" feature that suggests users think twice before sending a tweet. The following month, Facebook announced AI "conflict alerts" for groups, so that admins can take action where there may be "contentious or unhealthy conversations taking place." Amazon's Halo, launched in 2020, is a fitness band that monitors the tone of your voice. Wellness is no longer just the tracking of a heartbeat or the counting of steps, but the way we come across to those around us. Algorithmic therapeutic tools are being developed to predict and prevent negative behavior.

Hate speech is a major issue in social networks due to the high volume of data generated daily. Recent works demonstrate the usefulness of machine learning (ML) in dealing with the nuances required to distinguish between hateful posts from just sarcasm or offensive language. Many ML solutions for hate speech detection have been proposed by either changing how features are extracted from the text or the classification algorithm employed. However, most works consider only one type of feature extraction and classification algorithm. This work argues that a combination of multiple feature extraction techniques and different classification models is needed. We propose a framework to analyze the relationship between multiple feature extraction and classification techniques to understand how they complement each other. The framework is used to select a subset of complementary techniques to compose a robust multiple classifiers system (MCS) for hate speech detection. The experimental study considering four hate speech classification datasets demonstrates that the proposed framework is a promising methodology for analyzing and designing high-performing MCS for this task. MCS system obtained using the proposed framework significantly outperforms the combination of all models and the homogeneous and heterogeneous selection heuristics, demonstrating the importance of having a proper selection scheme. Source code, figures, and dataset splits can be found in the GitHub repository: https://github.com/Menelau/Hate-Speech-MCS.

Machine learning research typically starts with a fixed data set created early in the process. The focus of the experiments is finding a model and training procedure that result in the best possible performance in terms of some selected evaluation metric. This paper explores how changes in a data set influence the measured performance of a model. Using three publicly available data sets from the legal domain, we investigate how changes to their size, the train/test splits, and the human labelling accuracy impact the performance of a trained deep learning classifier. We assess the overall performance (weighted average) as well as the per-class performance. The observed effects are surprisingly pronounced, especially when the per-class performance is considered. We investigate how "semantic homogeneity" of a class, i.e., the proximity of sentences in a semantic embedding space, influences the difficulty of its classification. The presented results have far reaching implications for efforts related to data collection and curation in the field of AI & Law. The results also indicate that enhancements to a data set could be considered, alongside the advancement of the ML models, as an additional path for increasing classification performance on various tasks in AI & Law. Finally, we discuss the need for an established methodology to assess the potential effects of data set properties.

The Spanish AI Agency will be responsible for the development, supervision, and monitoring of projects within the framework of the National AI Strategy, as well as the projects promoted by the European Union – in particular those related to the regulatory development of artificial intelligence and its potential uses. Although the specific competences of the Spanish AI Agency have not yet been specified (since the creation of the body must be approved by law), we will keep a close eye on these developments, considering the high penalties foreseen under the AI Regulation and the supervisory powers that could be granted to this new authority.

A top technology adviser to the judiciary has proposed the creation of a new institute of legal innovation that would spot gaps in the law thrown up by technologies such as crypto assets and AI, and promote the greater use of English law in global business contracts. Professor Richard Susskind, technology adviser to the Lord Chief Justice and a director of think-tank LegalUK, believes an independent body, along the lines of the Alan Turing Institute, which pioneers research into artificial intelligence, would highlight areas of law that had failed to keep up with innovation. The institute would also promote English law to global companies as the law of choice to govern transactions in new areas such as blockchain. The proposal comes as some lawyers are concerned that England's legal sector, which employs 365,000 people, could lose out to rival centres such as Singapore and Dubai if its legislation fails to keep pace with advancing tech.

Big tech companies are facing the biggest expansion in potential technology regulation in a generation. And while the jury is out on whether all that sound and fury will signify anything, for the first time there are signs that the big-tech backlash could have a substantive impact. New laws under consideration in Europe, Asia and the U.S. could put sharp limits on how big tech companies can treat smaller competitors and restrict their use of artificial intelligence like facial recognition. Some proposals could ban common practices such as companies giving their own products a boost in their own rankings, something that could have an operational impact, executives and analysts say.

Artificial Intelligence (AI) algorithms are widely employed by businesses, governments, and other organizations in order to make decisions that have far-reaching impacts on individuals and society. Their decisions might influence everyone, everywhere, and anytime, offering solutions to problems faced in different disciplines or in daily life, but at the same time entailing risks like being denied a job or a medical treatment. The discriminative impact of AI-based decision-making to certain population groups has been already observed in a variety of cases. For instance, the COMPAS system for predicting the risk of re-offending was found to predict higher risk values for black defendants (and lower for white ones) than their actual risk (Angwin, Larson, Mattu, & Kirchner, 2016) (racial-bias). In another case, Google's Ads tool for targeted advertising was found to serve significantly fewer ads for high paid jobs to women than to men (Datta, Tschantz, & Datta, 2015) (gender-bias).

Did you miss a session from the Future of Work Summit? There is little doubt that AI is changing the business landscape and providing competitive advantages to those that embrace it. It is time, however, to move beyond the simple implementation of AI and to ensure that AI is being done in a safe and ethical manner. This is called responsible AI and will serve not only as a protection against negative consequences, but also as a competitive advantage in and of itself. Responsible AI is a governance framework that covers ethical, legal, safety, privacy, and accountability concerns.

Bias in AI systems can result in significant losses to companies, according to a new survey by an enterprise AI company. More than one in three companies (36 percent) revealed they had suffered losses due to AI bias in one or several algorithms, noted the DataRobot survey of over 350 U.S. and U.K. technologists, including CIOs, IT directors, IT managers, data scientists and development leads who use or plan to use AI. Of the companies damaged by AI bias, more than half lost revenue (62 percent) or customers (61 percent), while nearly half lost employees (43 percent) and over a third incurred legal fees from litigation (35 percent), according to the research, which was conducted in collaboration with the World Economic Forum and global academic leaders. Bias in AI systems can result in significant losses to companies, according to a new survey by an enterprise AI company. More than one in three companies (36 percent) revealed they had suffered losses due to AI bias in one or several algorithms, noted the DataRobot survey of over 350 U.S. and U.K. technologists, including CIOs, IT directors, IT managers, data scientists and development leads who use or plan to use AI.