Watching the opening day of the US Senate hearings on AI brought to mind Marx's quip about history repeating itself, "the first time as tragedy, the second as farce". Some time ago we had the farce of the boss of Meta (neé Facebook) explaining to a senator that his company made money from advertising. This week we had the tragedy of seeing senators quizzing Sam Altman, the new acceptable face of the tech industry. Well, as one of my kids, looking up from revising O-level classics, once explained to me: "It's when you can see the disaster coming but you can't do anything to stop it." The trigger moment was when Altman declared: "We think that regulatory interventions by government will be critical to mitigate the risks of increasingly powerful models."

Its absence left open a void in Google Play and Apple's App Store, which have been quietly filling with scam apps that sucker users into paying for weekly or monthly subscriptions, according to research from security firm Sophos. The official ChatGPT app, meanwhile, is free, and an Android version is arriving soon. But just because something is free doesn't make it good. Telly TV is offering 55-inch televisions for $0 to the first 500,000 people who join its reservation list. Of course, "free" comes with a catch: The company reserves the right to collect heaps of data about your viewing habits, and the TV includes a built-in camera that can track your movements.

In February, 2019, OpenAI, a little-known artificial-intelligence company, announced that its large-language-model text generator, GPT-2, would not be released to the public "due to our concerns about malicious applications of the technology." Among the dangers, the company stated, was a potential for misleading news articles, online impersonation, and automating the production of abusive or faked social-media content and of spam and phishing content. As a consequence, Open AI proposed that "governments should consider expanding or commencing initiatives to more systematically monitor the societal impact and diffusion of AI technologies, and to measure the progression in the capabilities of such systems." This week, four years after that warning, members of the Senate Judiciary Subcommittee on Privacy, Technology, and the Law met to discuss "Oversight of A.I.: Rules for Artificial Intelligence." As has been the case with other tech hearings on the Hill, this one came after a new technology with the capacity to fundamentally alter our social and political lives was already in circulation. Like many Americans, the lawmakers became concerned about the pitfalls of large-language-model artificial intelligence in March, when OpenAI released GPT-4, the latest and most polished iteration of its text generator.

Hiroshima – The world must urgently assess the impact of generative artificial intelligence, G7 leaders said Saturday, announcing they will launch discussions this year on "responsible" use of the technology. Text generation tools such as ChatGPT, image creators and music composed using AI have sparked delight, alarm and legal battles as creators accuse them of scraping material without permission. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this FAQ.

As artificial intelligence (AI) becomes more prevalent there is a growing demand from regulators to accompany decisions made by such systems with explanations. However, a persistent gap exists between the need to execute a meaningful right to explanation vs. the ability of Machine Learning systems to deliver on such a legal requirement. The regulatory appeal towards "a right to explanation" of AI systems can be attributed to the significant role of explanations, part of the notion called reason-giving, in law. Therefore, in this work we examine reason-giving's purposes in law to analyze whether reasons provided by end-user Explainability can adequately fulfill them. We find that reason-giving's legal purposes include: (a) making a better and more just decision, (b) facilitating due-process, (c) authenticating human agency, and (d) enhancing the decision makers' authority. Using this methodology, we demonstrate end-user Explainabilty's inadequacy to fulfil reason-giving's role in law, given reason-giving's functions rely on its impact over a human decision maker. Thus, end-user Explainability fails, or is unsuitable, to fulfil the first, second and third legal function. In contrast we find that end-user Explainability excels in the fourth function, a quality which raises serious risks considering recent end-user Explainability research trends, Large Language Models' capabilities, and the ability to manipulate end-users by both humans and machines. Hence, we suggest that in some cases the right to explanation of AI systems could bring more harm than good to end users. Accordingly, this study carries some important policy ramifications, as it calls upon regulators and Machine Learning practitioners to reconsider the widespread pursuit of end-user Explainability and a right to explanation of AI systems.

Ensembling BERT models often significantly improves accuracy, but at the cost of significantly more computation and memory footprint. In this work, we propose Multi-CLS BERT, a novel ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model. Multi-CLS BERT uses multiple CLS tokens with a parameterization and objective that encourages their diversity. Thus instead of fine-tuning each BERT model in an ensemble (and running them all at test time), we need only fine-tune our single Multi-CLS BERT model (and run the one model at test time, ensembling just the multiple final CLS embeddings). To test its effectiveness, we build Multi-CLS BERT on top of a state-of-the-art pretraining method for BERT (Aroca-Ouellette and Rudzicz, 2020). In experiments on GLUE and SuperGLUE we show that our Multi-CLS BERT reliably improves both overall accuracy and confidence estimation. When only 100 training samples are available in GLUE, the Multi-CLS BERT_Base model can even outperform the corresponding BERT_Large model. We analyze the behavior of our Multi-CLS BERT, showing that it has many of the same characteristics and behavior as a typical BERT 5-way ensemble, but with nearly 4-times less computation and memory.

This paper argues that Machine Learning (ML) algorithms must be educated. ML-trained algorithms' moral decisions are ubiquitous in human society. Sometimes reverting the societal advances governments, NGOs and civil society have achieved with great effort in the last decades or are yet on the path to be achieved. While their decisions have an incommensurable impact on human societies, these algorithms are within the least educated agents known (data incomplete, un-inclusive, or biased). ML algorithms are not something separate from our human idiosyncrasy but an enactment of our most implicit prejudices and biases. Some research is devoted to "responsibility assignment" as a strategy to tackle immoral AI behaviour. Yet this paper argues that the solution for AI ethical decision-making resides in algorithm education" (as opposed to the "training") of ML. Drawing from an analogy between ML and child education for social responsibility, the paper offers clear directions for responsible and sustainable AI design, specifically with respect to how to educate algorithms to decide ethically.

Causal mediation analysis (Pearl, 2001; VanderWeele, 2015; Imai et al., 2010a) is widely applied in experimental and observational studies to investigate the mechanism underlying a treatment-outcome relationship. Causal mediation methods have been developed under the potential outcomes framework with a primary objective to decompose the total treatment effect into an indirect effect that works through a specified mediator and a direct effect that works around the mediator. While alternative definitions exist, the natural indirect and direct effects have been considered as the most relevant for studying causal mechanisms (Nguyen et al., 2021). The natural indirect effect compares potential outcomes by switching the mediator from the value it would have taken under the control condition to the value it would have taken under the treated condition, while fixing the assignment to the treated condition. The natural direct effect compares potential outcomes by switching the assignment from the control condition to the treated condition, while fixing the mediator to the value it would have taken under the control condition. Parametric regressions (e.g., Valeri and VanderWeele, 2013; Cheng et al., 2021, 2023), semiparametric methods (e.g., Tchetgen Tchetgen and Shpitser, 2012), and nonparametric methods (e.g., Kim et al., 2017) have been proposed for estimating natural mediation effects, typically assuming that all study units perfectly comply with their treatment assignments. Experimental and observational studies are often subject to treatment noncompliance, where the actual treatment received for each unit may differ from the treatment assignment (Angrist et al., 1996). The intention-to-treat (ITT) effect (Lee et al., 1991) and the principal causal effect (PCE) (Frangakis and Rubin, 2002) represent two typical estimands to quantify the impact of intervention under noncompliance. To elaborate, the ITT estimand quantifies the'pragmatic effectiveness' of the treatment under real-world conditions, by measureing the effect of treatment assignment on the outcome among the study population regardless of the actual treatment receipt.

After introducing the scope of this paper, we start by discussing what an "explanation" really is. We then move on to discuss some of the existing approaches to XAI and build a taxonomy of the most popular methods. Next, we also look at a few applications of these and other XAI techniques in four primary domains: finance, autonomous driving, healthcare and manufacturing. We end by introducing a promising discipline, "Explanation Engineering," which includes a systematic approach for designing explainability into AI systems.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Senate Majority Leader Chuck Schumer is attempting to spearhead legislation that will set parameters around the development and use of artificial intelligence. The arch-Democrat is meeting with Sens. Mike Rounds, Martin Heinrich and Todd Young as part of a bipartisan exploratory group, NPR reported Thursday. "Congress must move quickly," Schumer said Thursday from the Senate floor.