Recent work introduced deep kernel processes as an entirely kernel-based alternative to NNs (Aitchison et al. 2020).

When your mailbag brims with 25,000 letters and emails every day, deciding which to answer first is daunting. When lurking within are pleas for help from some of the country's most vulnerable people, the stakes only get higher. That is the challenge facing the Department for Work and Pensions (DWP) as correspondence floods in from benefit applicants and claimants – of which there are more than 20 million, including pensioners, in the UK. The DWP thinks it may have found a solution in using artificial intelligence to read it all first – including handwritten missives. Human reading used to take weeks and could leave the most vulnerable people waiting for too long for help.

Ministers have shut down or dropped at least half a dozen artificial intelligence prototypes intended for the welfare system, the Guardian has learned, in a sign of the headwinds facing Keir Starmer's effort to increase government efficiency. Pilots of AI technology to enhance staff training, improve the service in jobcentres, speed up disability benefit payments and modernise communication systems are not being taken forward, freedom of information (FoI) requests reveal. Officials have internally admitted that ensuring AI systems are "scalable, reliable [and] thoroughly tested" are key challenges and say there have been many "frustrations and false starts". Not all trials would be expected to make it into regular use, but two of those now scrapped had been highlighted by the Department for Work and Pensions (DWP) in its latest annual report as examples of how it had "successfully tested multiple generative AI proofs of concept". A-cubed was intended to help staff steer jobseekers into work.

An artificial intelligence system used by the UK government to detect welfare fraud is showing bias according to people's age, disability, marital status and nationality, the Guardian can reveal. An internal assessment of a machine-learning programme used to vet thousands of claims for universal credit payments across England found it incorrectly selected people from some groups more than others when recommending whom to investigate for possible fraud. The admission was made in documents released under the Freedom of Information Act by the Department for Work and Pensions (DWP). The "statistically significant outcome disparity" emerged in a "fairness analysis" of the automated system for universal credit advances carried out in February this year. The emergence of the bias comes after the DWP this summer claimed the AI system "does not present any immediate concerns of discrimination, unfair treatment or detrimental impact on customers". This assurance came in part because the final decision on whether a person gets a welfare payment is still made by a human, and officials believe the continued use of the system – which is attempting to help cut an estimated 8bn a year lost in fraud and error – is "reasonable and proportionate".

The focus of this paper is to increase our understanding of the Longest Processing Time First (LPT) heuristic. LPT is a classical heuristic for the fundamental problem of uniform machine scheduling. For different machine speeds, LPT was first considered by Gonzalez et al (SIAM J. Computing, 1977). Since then, extensive work has been done to improve the approximation factor of the LPT heuristic. However, all known implementations of the LPT heuristic take $O(mn)$ time, where $m$ is the number of machines and $n$ is the number of jobs. In this work, we come up with the first near-linear time implementation for LPT. Specifically, the running time is $O((n+m)(\log^2{m}+\log{n}))$. Somewhat surprisingly, the result is obtained by mapping the problem to dynamic maintenance of lower envelope of lines, which has been well studied in the computational geometry community. Our second contribution is to analyze the performance of LPT for the Drones Warehouse Problem (DWP), which is a natural generalization of the uniform machine scheduling problem motivated by drone-based parcel delivery from a warehouse. In this problem, a warehouse has multiple drones and wants to deliver parcels to several customers. Each drone picks a parcel from the warehouse, delivers it, and returns to the warehouse (where it can also get charged). The speeds and battery lives of the drones could be different, and due to the limited battery life, each drone has a bounded range in which it can deliver parcels. The goal is to assign parcels to the drones so that the time taken to deliver all the parcels is minimized. We prove that the natural approach of solving this problem via the LPT heuristic has an approximation factor of $\phi$, where $\phi \approx 1.62$ is the golden ratio.

Kate Osamor, the Labour MP for Edmonton, recently received an email from a charity about a constituent of hers who had had her benefits suspended apparently without reason. "For well over a year now she has been trying to contact DWP [the Department for Work and Pensions] and find out more about the reason for the suspension of her UC [Universal Credit], but neither she nor our casework team have got anywhere," the email said. "It remains unclear why DWP has suspended the claim, never mind whether this had any merit … she has been unable to pay rent for 18 months and is consequently facing eviction proceedings." Osamor has been dealing with dozens of such cases in recent years, often involving Bulgarian nationals. She believes they have been victims of a semi-automated system that uses an algorithm to flag up potential benefits fraud before referring those cases to humans to make a final decision on whether to suspend people's claims.

Deep kernel processes are a recently introduced class of deep Bayesian models that have the flexibility of neural networks, but work entirely with Gram matrices. They operate by alternately sampling a Gram matrix from a distribution over positive semi-definite matrices, and applying a deterministic transformation. When the distribution is chosen to be Wishart, the model is called a deep Wishart process (DWP). This particular model is of interest because its prior is equivalent to a deep Gaussian process (DGP) prior, but at the same time it is invariant to rotational symmetries, leading to a simpler posterior distribution. Practical inference in the DWP was made possible in recent work ("A variational approximate posterior for the deep Wishart process" Ober and Aitchison 2021a) where the authors used a generalisation of the Bartlett decomposition of the Wishart distribution as the variational approximate posterior. However, predictive performance in that paper was less impressive than one might expect, with the DWP only beating a DGP on a few of the UCI datasets used for comparison. In this paper, we show that further generalising their distribution to allow linear combinations of rows and columns in the Bartlett decomposition results in better predictive performance, while incurring negligible additional computation cost.

Malicious attackers can generate targeted adversarial examples by imposing tiny noises, forcing neural networks to produce specific incorrect outputs. With cross-model transferability, network models remain vulnerable even in black-box settings. Recent studies have shown the effectiveness of ensemble-based methods in generating transferable adversarial examples. To further enhance transferability, model augmentation methods aim to produce more networks participating in the ensemble. However, existing model augmentation methods are only proven effective in untargeted attacks. In this work, we propose Diversified Weight Pruning (DWP), a novel model augmentation technique for generating transferable targeted attacks. DWP leverages the weight pruning method commonly used in model compression. Compared with prior work, DWP protects necessary connections and ensures the diversity of the pruned models simultaneously, which we show are crucial for targeted transferability. Experiments on the ImageNet-compatible dataset under various and more challenging scenarios confirm the effectiveness: transferring to adversarially trained models, Non-CNN architectures, and Google Cloud Vision. The results show that our proposed DWP improves the targeted attack success rates with up to $10.1$%, $6.6$%, and $7.0$% on the combination of state-of-the-art methods, respectively. The source code will be made available after acceptance.

The UK's Department for Work and Pensions (DWP) is accelerating its use of automated systems to handle claims for benefits, in a move some fear will disadvantage welfare recipients. The DWP has hired nearly 1,000 new IT staff in the last 18 months, and increased spending to about £8m (US$10.3m) This new'virtual workforce' is to take over some of the jobs of humans. According to an investigation by The Guardian, which has unearthed further detail on the DWP's plans, one recent tender document requested help to build "systems that… can automatically carry out tasks without human intervention". As well as contracts with a number of multinationals, the department is working with UiPath, a New York-based firm co-founded by Daniel Dines, the world's first "bot billionaire", whose machine learning software is being deployed by the DWP to check benefit claims and detect fraud.