The UK should introduce new legislation to control artificial intelligence or risk falling behind the EU and the US in setting the pace for regulating the technology, MPs have said. Rishi Sunak's government was urged to act as it prepares to host a global AI safety summit at Bletchley Park, home of the Enigma codebreakers, in November. The science, innovation and technology committee said on Thursday the regulatory approach outlined in a recent government white paper risked falling behind others. "The AI white paper should be welcomed as an initial effort to engage with this complex task, but its proposed approach is already risking falling behind the pace of development of AI," the committee said in an interim report on AI governance. "This threat is made more acute by the efforts of other jurisdictions, principally the European Union and the United States, to set international standards." The EU, a trendsetter in tech regulation, is pushing ahead with the AI Act, while in the US the White House has published a blueprint for an AI bill of rights and the US senate majority leader, Chuck Schumer, has published a framework for developing AI regulations.

Shuler has been at the forefront of the debate over AI's impact on workers. AFL-CIO represents SAG-AFTRA, the actors' union that is striking in part over concerns about workers being replaced with AI-generated content. Wiley has warned about algorithmic bias and recently co-authored a letter to the Biden White House demanding it require federal contractors to ensure they are not abusing consumers' data or promoting discriminatory algorithms. Raji, whose research involves algorithmic auditing, has said evaluations are crucial to ensure technology doesn't cause false arrests, unfair hiring decisions and inaccurate medical diagnoses.

In the amendments adopted by the European Parliament on 14 June 2023 on the Artificial Intelligence Act, the EU's regulatory stance on AI Manipulation is outlined as such: "(a) the placing on the market, putting into service or use of an AI system that deploys subliminal techniques beyond a person's consciousness or purposefully manipulative or deceptive techniques, with the objective to or the effect of materially distorting a person's or a group of persons' behaviour by appreciably impairing the person's ability to make an informed decision, thereby causing the person to take a decision that that person would not have otherwise taken in a manner that causes or is likely to cause that person, another person or group of persons significant harm; The prohibition of AI system that deploys subliminal techniques referred to in the first sub-paragraph shall not apply to AI systems intended to be used for approved therapeutical purposes on the basis of specific informed consent of the individuals that are exposed to them or, where applicable, of their legal guardian; (b) the placing on the market, putting into service or use of an AI system that exploits any of the vulnerabilities of a person or a specific group of persons, including characteristics of such person's or such group's known or predicted personality traits or social or economic situation, age, physical or mental ability with the objective or to the effect of materially distorting the behaviour of that person or a person pertaining to that group in a manner that causes or is likely to cause that person or another person significant harm [1]" We argue that in the current regulatory framing, there is a lack of clarity of core concepts in the present amendments. For example, "personality traits" are mentioned six times in the latest amendments, and yet are not defined at any point in the document, or in the draft of the Act [2, 1].

Recently, ABA Learning has been proposed as a form of symbolic machine learning for drawing Assumption-Based Argumentation frameworks from background knowledge and positive and negative examples. We propose a novel method for implementing ABA Learning using Answer Set Programming as a way to help guide Rote Learning and generalisation in ABA Learning.

One of the biggest challenges of natural language generation (NLG) is the proper handling of named entities. Named entities are a common source of grammar mistakes such as wrong prepositions, wrong article handling, or incorrect entity inflection. Without factoring linguistic representation, such errors are often underrepresented when evaluating on a small set of arbitrarily picked argument values, or when translating a dataset from a linguistically simpler language, like English, to a linguistically complex language, like Russian. However, for some applications, broadly precise grammatical correctness is critical -- native speakers may find entity-related grammar errors silly, jarring, or even offensive. To enable the creation of more linguistically diverse NLG datasets, we release a Corpus of Linguistically Significant Entities (CLSE) annotated by linguist experts. The corpus includes 34 languages and covers 74 different semantic types to support various applications from airline ticketing to video games. To demonstrate one possible use of CLSE, we produce an augmented version of the Schema-Guided Dialog Dataset, SGD-CLSE. Using the CLSE's entities and a small number of human translations, we create a linguistically representative NLG evaluation benchmark in three languages: French (high-resource), Marathi (low-resource), and Russian (highly inflected language). We establish quality baselines for neural, template-based, and hybrid NLG systems and discuss the strengths and weaknesses of each approach.

Real-world software applications must constantly evolve to remain relevant. This evolution occurs when developing new applications or adapting existing ones to meet new requirements, make corrections, or incorporate future functionality. Traditional methods of software quality control involve software quality models and continuous code inspection tools. These measures focus on directly assessing the quality of the software. However, there is a strong correlation and causation between the quality of the development process and the resulting software product. Therefore, improving the development process indirectly improves the software product, too. To achieve this, effective learning from past processes is necessary, often embraced through post mortem organizational learning. While qualitative evaluation of large artifacts is common, smaller quantitative changes captured by application lifecycle management are often overlooked. In addition to software metrics, these smaller changes can reveal complex phenomena related to project culture and management. Leveraging these changes can help detect and address such complex issues. Software evolution was previously measured by the size of changes, but the lack of consensus on a reliable and versatile quantification method prevents its use as a dependable metric. Different size classifications fail to reliably describe the nature of evolution. While application lifecycle management data is rich, identifying which artifacts can model detrimental managerial practices remains uncertain. Approaches such as simulation modeling, discrete events simulation, or Bayesian networks have only limited ability to exploit continuous-time process models of such phenomena. Even worse, the accessibility and mechanistic insight into such gray- or black-box models are typically very low. To address these challenges, we suggest leveraging objectively [...]

What is it with wave metaphors? Technological determinists – people who believe that technology drives history – love them. Think of Alvin Toffler, who saw the history of civilisation as a succession of three such waves (agricultural, industrial and post-industrial). The idea is of immense power, unstoppable, moving inexorably towards us as we cower before its immensity, much as the dinosaurs must have done when they saw the mile-high tsunami heading in their direction. Mustafa Suleyman says he is not a determinist, but at times he sounds awfully like one.

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers have to put unlearning functionality into their consideration. The most straightforward method to unlearn users' contribution is to retrain the model from the initial state, which is not realistic in high throughput applications with frequent unlearning requests. Though some machine unlearning frameworks have been proposed to speed up the retraining process, they fail to match decentralized learning scenarios. In this paper, we design a decentralized unlearning framework called HDUS, which uses distilled seed models to construct erasable ensembles for all clients. Moreover, the framework is compatible with heterogeneous on-device models, representing stronger scalability in real-world applications. Extensive experiments on three real-world datasets show that our HDUS achieves state-of-the-art performance.

When using machine learning (ML) to aid decision-making, it is critical to ensure that an algorithmic decision is fair, i.e., it does not discriminate against specific individuals/groups, particularly those from underprivileged populations. Existing group fairness methods require equal group-wise measures, which however fails to consider systematic between-group differences. The confounding factors, which are non-sensitive variables but manifest systematic differences, can significantly affect fairness evaluation. To tackle this problem, we believe that a fairness measurement should be based on the comparison between counterparts (i.e., individuals who are similar to each other with respect to the task of interest) from different groups, whose group identities cannot be distinguished algorithmically by exploring confounding factors. We have developed a propensity-score-based method for identifying counterparts, which prevents fairness evaluation from comparing "oranges" with "apples". In addition, we propose a counterpart-based statistical fairness index, termed Counterpart-Fairness (CFair), to assess fairness of ML models. Various empirical studies were conducted to validate the effectiveness of CFair. We publish our code at \url{https://github.com/zhengyjo/CFair}.

The burgeoning AI industry has barrelled clean past the "move fast" portion of its development, right into the part where we "break things" -- like society! Since the release of ChatGPT last November, generative AI systems have taken the digital world by storm, finding use in everything from machine coding and industrial applications to game design and virtual entertainment. It's also quickly been adopted for illicit purposes like scaling spam email operations and creating deepfakes. That's one technological genie we're never getting back in its bottle so we'd better get working on regulating it, argues Silicon Valley–based author, entrepreneur, investor, and policy advisor, Tom Kemp, in his new book, Containing Big Tech: How to Protect Our Civil Rights, Economy, and Democracy. In the excerpt below, Kemp explains what form that regulation might take and what its enforcement would mean for consumers.