Five days after Russia launched its full-scale invasion of Ukraine, a year ago this week, US-based facial recognition company Clearview AI offered the Ukrainian government free access to its technology, suggesting that it could be used to reunite families, identify Russian operatives, and fight misinformation. Soon afterward, the Ukraine government revealed it was using the technology to scan the faces of dead Russian soldiers to identify their bodies and notify their families. By December 2022, Mykhailo Fedorov, Ukraine's vice prime minister and minister of digital transformation, was tweeting a picture of himself with Clearview AI's CEO Hoan Ton-That, thanking the company for its support. Accounting for the dead and letting families know the fate of their relatives is a human rights imperative written into international treaties, protocols, and laws like the Geneva Conventions and the International Committee of the Red Cross' (ICRC) Guiding Principles for Dignified Management of the Dead. It is also tied to much deeper obligations.

I spoke last week at a conference in Cardiff on the future of work. It was organised by the law firm Dawson Gray. You can't talk about the the future of work without thinking about the future city, since the shape and structure of work is bound up more or less completely with the shape and structure of cities. Edward Glaeser's book The Triumph of the City (2012) gets a lot of love in these conversations. It's hard to find people who have a bad word to say about it. Cities are the absence of physical space between people and companies.

According to a report from Reuters, ChatGPT is listed as the author or co-author of at least 200 books on Amazon's Kindle Store. However, the number of bot-written books is likely higher than that since Amazon's policies don't require authors to disclose their use of AI. Brett Schickler published on the Kindle Store a children's book written and illustrated by AI. Although Schickler says the book has earned him less than $100 since its January release, he only spent a few hours creating it with ChatGPT prompts like "write a story about a dad teaching his son about financial literacy." Science-fiction publication Clarkesworld Magazine has temporarily halted short-story submissions after receiving a flood of articles suspected of using AI without disclosure, which was reported by PCMag.

To date, much of the algorithmic fairness literature has focused on the fairness of classification systems which are used, for example, to decide whether a person should be granted a loan or be released from prison on bail. Even in cases where such classification decisions are based on risk score models - such as in the highly influential COMPAS case [5, 11, 16] - their fairness is typically considered a function of the decisions, or classifications, made by the system. Of course, any risk score model can be turned into a classifier by selecting a probability threshold (in binary classification) or predicting the most likely outcome (in multi-class classification). Nevertheless, we argue here that it is worthwhile to distinguish between these two settings and consider the fairness of risk models independent of their downstream use, be it as the basis for a classifier or otherwise. We discuss notions of fairness for risk scores as well as their relationship to classical, classification-level notions of fairness, and we develop robust tools to empirically quantify risk score fairness. We illustrate our methodology in two case studies, one situated in the criminal justice system and one in healthcare. Why distinguish between fair models and fair decisions? In the statistical literature, it is generally considered desirable to distinguish between inference (e.g., identifying a risk score model) and subsequent decision-making (e.g., deriving a classification from a risk score model): while the former represents a purely statistical task, the latter depends on subjective

Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias. Machine Unlearning can address these by allowing post-hoc deletion of affected training data from a learned model. Achieving this task exactly is computationally expensive; consequently, recent works have proposed inexact unlearning algorithms to solve this approximately as well as evaluation methods to test the effectiveness of these algorithms. In this work, we first outline some necessary criteria for evaluation methods and show no existing evaluation satisfies them all. Then, we design a stronger black-box evaluation method called the Interclass Confusion (IC) test which adversarially manipulates data during training to detect the insufficiency of unlearning procedures. We also propose two analytically motivated baseline methods~(EU-k and CF-k) which outperform several popular inexact unlearning methods. Overall, we demonstrate how adversarial evaluation strategies can help in analyzing various unlearning phenomena which can guide the development of stronger unlearning algorithms.

Some approaches [14], [15] aim to infer global feature importance for a given data Transparency of machine learning (ML) based system, projection. Another work [16] estimates feature importance applied in the real world, is nowadays a widely accepted locally for a vicinity around a projected data point, using requirement - the importance of transparency was also recognized locally linear models. A recent paper [17] proposes to use by the policy makers and therefore made its way local feature importance explanations by computing a local into legal regulations like the EU's GDPR [1]. A popular linear approximation for each reduced dimension, extracting way of achieving transparency is by means of explanations [2] feature importances from the weight vectors. Further, saliency which then gave rise to the field of eXplainable AI (XAI) [3], map approaches such as the layer-wise relevance propagation [4]. Although a lot of different explanation methodologies (LRP) [18] could in principle be applied to a parametric for ML based systems have been developed [2], [4], it is dimensionality reduction mapping in order to obtain locally important to realize that it is still somewhat unclear what relevant features. However, these approaches do not provide exactly makes up a good explanation [5], [6]. Therefore contrasting explanations, in which we are interested here.

This work aims at hierarchical multi-label patents classification for patents disclosing technologies related to green plastics. This is an emerging field for which there is currently no classification scheme, and hence, no labeled data is available, making this task particularly challenging. We first propose a classification scheme for this technology and a way to learn a machine learning model to classify patents into the proposed classification scheme. To achieve this, we come up with a strategy to automatically assign labels to patents in order to create a labeled training dataset that can be used to learn a classification model in a supervised learning setting. Using said training dataset, we come up with two classification models, a SciBERT Neural Network (SBNN) model and a SciBERT Hierarchical Neural Network (SBHNN) model. Both models use a BERT model as a feature extractor and on top of it, a neural network as a classifier. We carry out extensive experiments and report commonly evaluation metrics for this challenging classification problem. The experiment results verify the validity of our approach and show that our model sets a very strong benchmark for this problem. We also interpret our models by visualizing the word importance given by the trained model, which indicates the model is capable to extract high-level semantic information of input documents. Finally, we highlight how our solution fulfills the evaluation criteria for the EPO CodeFest and we also outline possible directions for future work. Our code has been made available at https://github.com/epo/CF22-Green-Hands

David Wakeling, head of London-based law firm Allen & Overy's markets innovation group, first came across law-focused generative AI tool Harvey in September 2022. He approached OpenAI, the system's developer, to run a small experiment. A handful of his firm's lawyers would use the system to answer simple questions about the law, draft documents, and take first passes at messages to clients. The trial started small, Wakeling says, but soon ballooned. Around 3,500 workers across the company's 43 offices ended up using the tool, asking it around 40,000 queries in total.

The government decided Tuesday on draft legislation to screen and certify Japanese-language schools to ensure their quality by setting standards including the number of teachers and educational content. In the legislation, eyed for enforcement in April 2024 after its enactment in the current parliament session, the government also requires instructors at certified schools to obtain a new national qualification for teaching Japanese. The government's strengthened surveillance over the Japanese-language schools follows cases of questionable management, such as with one operator which was found to be allegedly making illegal job arrangements for foreign students in 2017. 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.

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