Washington – The United States on Friday imposed extensive human rights-related sanctions on dozens of people and entities tied to China, Myanmar, North Korea and Bangladesh, and added Chinese artificial intelligence company SenseTime Group to an investment blacklist. Canada and the United Kingdom joined the United States in imposing sanctions related to human rights abuses in Myanmar, while Washington also imposed the first new sanctions on North Korea under President Joe Biden's administration and targeted Myanmar military entities, among others, in action marking Human Rights Day. "Our actions today, particularly those in partnership with the United Kingdom and Canada, send a message that democracies around the world will act against those who abuse the power of the state to inflict suffering and repression," Deputy Treasury Secretary Wally Adeyemo said in a statement. China's embassy in Washington denounced the U.S. move as "serious interference in China's internal affairs" and a "severe violation of basic norms governing international relations." Embassy spokesman Liu Pengyu said it would do "grave harm to China-U.S. relations" and urged Washington to rescind the decision.

Retrosynthetic planning problem is to analyze a complex molecule and give a synthetic route using simple building blocks. The huge number of chemical reactions leads to a combinatorial explosion of possibilities, and even the experienced chemists could not select the most promising transformations. The current approaches rely on human-defined or machine-trained score functions which have limited chemical knowledge or use expensive estimation methods such as rollout to guide the search. In this paper, we propose {\tt MCTS}, a novel MCTS-based retrosynthetic planning approach, to deal with retrosynthetic planning problem. Instead of exploiting rollout, we build an Experience Guidance Network to learn knowledge from synthetic experiences during the search. Experiments on benchmark USPTO datasets show that, our {\tt MCTS} gains significant improvement over state-of-the-art approaches both in efficiency and effectiveness.

Zero-shot cross-lingual transfer is an important feature in modern NLP models and architectures to support low-resource languages. In this work, We study zero-shot cross-lingual transfer from English to French and German under Multi-Label Text Classification, where we train a classifier using English training set, and we test using French and German test sets. We extend EURLEX57K dataset, the English dataset for topic classification of legal documents, with French and German official translation. We investigate the effect of using some training techniques, namely Gradual Unfreezing and Language Model finetuning, on the quality of zero-shot cross-lingual transfer. We find that Language model finetuning of multi-lingual pre-trained model (M-DistilBERT, M-BERT) leads to 32.0-34.94%, 76.15-87.54% relative improvement on French and German test sets correspondingly. Also, Gradual unfreezing of pre-trained model's layers during training results in relative improvement of 38-45% for French and 58-70% for German. Compared to training a model in Joint Training scheme using English, French and German training sets, zero-shot BERT-based classification model reaches 86% of the performance achieved by jointly-trained BERT-based classification model.

In the wake of the #MeToo and Black Lives Matter movements, HR leaders are increasingly being tasked with addressing the inequities that exist in their workplaces. This may involve brutal self-reflection and a willingness to admit that their companies are far from the equitable places they once thought they were. On a positive note, experts say, employers appear to be taking diversity, equity and inclusion efforts seriously--and they're getting some help from tech. For instance, some HR technology providers are creating AI-based solutions that shine a light on employers' often decades-old management practices that can allow them to overlook an all-white senior management team and C-suite or to ignore instances where minorities are leaving hostile workplaces. The role of technology should not be underestimated and can be a great enabler of DE&I, says Kay Formanek, founder and CEO of Diversity and Performance, a diversity education company.

The United States Treasury slapped sanctions on 25 individuals and entities on Friday, citing human rights abuses, and blacklisted a Chinese maker of artificial intelligence (AI) facial recognition software, citing its role in the repression of Muslim Uighurs and other religious and ethnic minorities in Xinjiang. In addition to China, Friday's raft of sanctions targeted people and entities linked to human rights abuses in Myanmar, North Korea and Bangladesh. Canada and the United Kingdom joined the US in announcing sanctions over repression in Myanmar. "On International Human Rights Day, Treasury is using its tools to expose and hold accountable perpetrators of serious human rights abuse," said Deputy Secretary of the Treasury Wally Adeyemo in a statement posted on the department's website. Treasury added AI firm SenseTime Group Limited to a list of Chinese blacklisted firms for developing facial recognition programmes "that can determine a target's ethnicity, with a particular focus on identifying ethnic Uyghurs".

The accelerated growth in ecommerce and online marketplaces has led to a surge in fraudulent behavior online perpetrated by bots and bad actors alike. A strategic and effective approach to online fraud detection will be needed in order to tackle increasingly sophisticated threats to online retailers. These market shifts come at a time of significant regulatory change. Across the globe, new legislation is coming into force that alters the balance of responsibility in fraud prevention between users, brands, and the platforms that promote them digitally. For example, the EU Digital Services Act and US Shop Safe Act will require online platforms to take greater responsibility for the content on their websites, a responsibility that was traditionally the domain of brands and users to monitor and report.

Covid-19 created cascades of shortages, disruptions, and problems that rolled downhill and landed in the most vulnerable neighborhoods. In these neighborhoods, it's often nonprofit organizations that provide services to members of the community. While the pandemic accelerated the need for digital transformation throughout the economy, the nonprofit sector was not immune to the need for nearly overnight innovation. As experts on the use of technology for social good, we've observed the many ways that nonprofits have been adopting "smart tech" to further social change in the wake of the pandemic, which we chronicle in our upcoming book, The Smart Nonprofit. We use "smart tech" as an umbrella term for advanced digital technologies that make decisions for people.

In a world of daily emerging scientific inquisition and discovery, the prolific launch of machine learning across industries comes to little surprise for those familiar with the potential of ML. Neither so should the congruent expansion of ethics-focused research that emerged as a response to issues of bias and unfairness that stemmed from those very same applications. Fairness research, which focuses on techniques to combat algorithmic bias, is now more supported than ever before. A large portion of fairness research has gone to producing tools that machine learning practitioners can use to audit for bias while designing their algorithms. Nonetheless, there is a lack of application of these fairness solutions in practice. This systematic review provides an in-depth summary of the algorithmic bias issues that have been defined and the fairness solution space that has been proposed. Moreover, this review provides an in-depth breakdown of the caveats to the solution space that have arisen since their release and a taxonomy of needs that have been proposed by machine learning practitioners, fairness researchers, and institutional stakeholders. These needs have been organized and addressed to the parties most influential to their implementation, which includes fairness researchers, organizations that produce ML algorithms, and the machine learning practitioners themselves. These findings can be used in the future to bridge the gap between practitioners and fairness experts and inform the creation of usable fair ML toolkits.

In this paper, we present a method of building strong, explainable classifiers in the form of Boolean search rules. We developed an interactive environment called CASE (Computer Assisted Semantic Exploration) which exploits word co-occurrence to guide human annotators in selection of relevant search terms. The system seamlessly facilitates iterative evaluation and improvement of the classification rules. The process enables the human annotators to leverage the benefits of statistical information while incorporating their expert intuition into the creation of such rules. We evaluate classifiers created with our CASE system on 4 datasets, and compare the results to machine learning methods, including SKOPE rules, Random forest, Support Vector Machine, and fastText classifiers. The results drive the discussion on trade-offs between superior compactness, simplicity, and intuitiveness of the Boolean search rules versus the better performance of state-of-the-art machine learning models for text classification.

The inclusion of intermittent and renewable energy sources has increased the importance of demand forecasting in power systems. Smart meters can play a critical role in demand forecasting due to the measurement granularity they provide. Despite their virtue, smart meters used for forecasting face some constraints as consumers' privacy concerns, reluctance of utilities and vendors to share data with competitors or third parties, and regulatory constraints. This paper examines a collaborative machine learning method, federated learning extended with privacy preserving techniques for short-term demand forecasting using smart meter data as a solution to the previous constraints. The combination of privacy preserving techniques and federated learning enables to ensure consumers' confidentiality concerning both their data, the models generated using it (Differential Privacy), and the communication mean (Secure Aggregation). To evaluate this paper's collaborative secure federated learning setting, we explore current literature to select the baseline for our simulations and evaluation. We simulate and evaluate several scenarios that explore how traditional centralized approaches could be projected in the direction of a decentralized, collaborative and private system. The results obtained over the evaluations provided decent performance and in a privacy setting using differential privacy almost perfect privacy budgets (1.39,$10e^{-5}$) and (2.01,$10e^{-5}$) with a negligible performance compromise.