Automatic Speech Recognition (ASR) systems now mediate countless human-technology interactions, yet research on their fairness implications remains surprisingly limited. This paper examines ASR bias through a philosophical lens, arguing that systematic misrecognition of certain speech varieties constitutes more than a technical limitation -- it represents a form of disrespect that compounds historical injustices against marginalized linguistic communities. We distinguish between morally neutral classification (discriminate1) and harmful discrimination (discriminate2), demonstrating how ASR systems can inadvertently transform the former into the latter when they consistently misrecognize non-standard dialects. We identify three unique ethical dimensions of speech technologies that differentiate ASR bias from other algorithmic fairness concerns: the temporal burden placed on speakers of non-standard varieties ("temporal taxation"), the disruption of conversational flow when systems misrecognize speech, and the fundamental connection between speech patterns and personal/cultural identity. These factors create asymmetric power relationships that existing technical fairness metrics fail to capture. The paper analyzes the tension between linguistic standardization and pluralism in ASR development, arguing that current approaches often embed and reinforce problematic language ideologies. We conclude that addressing ASR bias requires more than technical interventions; it demands recognition of diverse speech varieties as legitimate forms of expression worthy of technological accommodation. This philosophical reframing offers new pathways for developing ASR systems that respect linguistic diversity and speaker autonomy.

Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology. This paper investigates GNNs derived from diverse neural flows, concentrating on their connection to various stability notions such as BIBO stability, Lyapunov stability, structural stability, and conservative stability. We argue that Lyapunov stability, despite its common use, does not necessarily ensure adversarial robustness. Inspired by physics principles, we advocate for the use of conservative Hamiltonian neural flows to construct GNNs that are robust to adversarial attacks. The adversarial robustness of different neural flow GNNs is empirically compared on several benchmark datasets under a variety of adversarial attacks.

Principle Engraving: In the third stage, we fine-tune the original LLM (the base model) on the self-aligned responses, generated by the LLM itself through prompting, while pruning the principles and demonstrations for the fine-tuned model.

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com If you want to succeed in science, it helps to have good ideas, to be good at experiments, and so forth. But what you really need is a knack for a good acronym. If you can come up with a string of words that describes your project, and also abbreviates to form a word, you're golden.

BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research.

Artificial intelligence (AI) is advancing quickly and is being adopted in most industries. Using AI to draft an email message or check your grammar is typically not a cause for concern, but using it to make decisions that affect people's lives is another matter. When constitutional rights are involved, as in the justice system, transparency is paramount. During the Biden-Harris administration, Executive Order 14110 directed agencies to develop guidelines for acceptable uses and regulation of AI. Some of these uses, like summarizing and notetaking, will occur across the government.

Within a week of his inauguration, President Trump issued an executive order to develop "The Iron Dome for America" (rebranded the "Golden Dome" a month later.) The eruption of a revived conflict between Israel and Iran in June has only strengthened the case for an American version of the Iron Dome in the eyes of the administration. Trump has often expressed admiration for Israel's Iron Dome, an air defense system that can intercept short-range rockets and artillery over the small nation and that is funded in part by the United States. But in the complicated security landscape confronting the world today, is spectacle the same as safety? This story is from our forthcoming print issue, which is all about security.

Experts have warned of a'frightening expansion' of'Orwellian' technology as the government expands the use of live facial recognition across the country. Ten vans equipped with facial recognition cameras will be deployed across seven police forces – Greater Manchester, West Yorkshire, Bedfordshire, Surrey, Sussex, Thames Valley and Hampshire. The Home Office maintains that this technology will only be used to catch'high–harm' offenders with rules to ensure'safeguards and oversight'. According to the government, the technology has already been used to make 580 arrests in London over the last year, including 52 registered sex offenders. However, rights groups have raised concerns that the unprecedented rollout of this surveillance technology risks becoming overly intrusive.