Work must be reshaped in the upcoming new era characterized by new challenges and the presence of new technologies and computational tools. Over-automation seems to be the driver of the digitalization process. Substitution is the paradigm leading Artificial Intelligence and robotics development against human cognition. Digital technology should be designed to enhance human skills and make more productive use of human cognition and capacities. Digital technology is characterized also by scalability because of its easy and inexpensive deployment. Thus, automation can lead to the absence of jobs and scalable negative impact in human development and the performance of business. A look at digitalization from the lens of Sustainable Development Goals can tell us how digitalization impact in different sectors and areas considering society as a complex interconnected system. Here, reflections on how AI and Data impact future of work and sustainable development are provided grounded on an ethical core that comprises human-level principles and also systemic principles.

Machine Learning models have been deployed across almost every aspect of society, often in situations that affect the social welfare of many individuals. Although these models offer streamlined solutions to large problems, they may contain biases and treat groups or individuals unfairly. To our knowledge, this review is one of the first to focus specifically on gender bias in applications of machine learning. We first introduce several examples of machine learning gender bias in practice. We then detail the most widely used formalizations of fairness in order to address how to make machine learning models fairer. Specifically, we discuss the most influential bias mitigation algorithms as applied to domains in which models have a high propensity for gender discrimination. We group these algorithms into two overarching approaches -- removing bias from the data directly and removing bias from the model through training -- and we present representative examples of each. As society increasingly relies on artificial intelligence to help in decision-making, addressing gender biases present in these models is imperative. To provide readers with the tools to assess the fairness of machine learning models and mitigate the biases present in them, we discuss multiple open source packages for fairness in AI.

Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs. We formulate this task as a challenging knowledge graph completion problem, which requires not only text understanding but also graph reasoning. To this end, we propose a novel text-guided graph reasoning approach. We collect amounts of real-world legal provision data from the Guangdong government service website and construct a legal dataset called LegalLPP. Extensive experimental results on the dataset show that our approach achieves better performance compared with baselines. The code and dataset are available in \url{https://github.com/zjunlp/LegalPP} for reproducibility.

Machine learning, artificial intelligence, and deep learning have advanced significantly over the past decade. Nonetheless, humans possess unique abilities such as creativity, intuition, context and abstraction, analytic problem solving, and detecting unusual events. To successfully tackle pressing scientific and societal challenges, we need the complementary capabilities of both humans and machines. The Federal Government could accelerate its priorities on multiple fronts through judicious integration of citizen science and crowdsourcing with artificial intelligence (AI), Internet of Things (IoT), and cloud strategies.

Artificial Intelligence might be a term for collecting concepts that allow computer systems to vaguely work like a brain. However, the use of numbers to represent complex social reality is flawed. AI might seem factual and precise when it isn't as the results that AI produces depend on how it is designed and what data it uses. At the moment in our everyday world, AI performs narrow tasks such as facial recognition, natural language processing, or internet searches but the pace of its progress is exponential and regardless of its benefits. The impact it is having is hard to ignore with more and more of the world's commerce becoming automated and trading going online.

Artificial intelligence has been credited with eliminating biases that undermine diversity, equity and inclusion in talent acquisition. With concern over racial injustice since George Floyd's death in police custody driving DEI to new heights, it's easy to miss the scourge of ageism. The Economic Policy Institute estimates that an inability of nearly three-fourths of workers age 65 and older to telecommute during the pandemic placed them at a higher risk for developing severe illnesses from COVID-19. In addition, the U.S. Bureau of Labor Statistics noted that long-term unemployment for working Americans 55 and older spiked to 26.4% from 14% last September vs. an increase to 18.2% from 11.3% outside that category. Experts note that so-called conversational AI can help ensure the safety of older talent as employees return to workplaces without ageism creeping into the equation post-pandemic.

American households are increasingly connected internally through the use of artificially intelligent appliances.1 But who regulates the safety of those dishwashers, microwaves, refrigerators, and vacuums powered by artificial intelligence (AI)? On March 2, 2021, at a virtual forum attended by stakeholders across the entire industry, the Consumer Product Safety Commission (CPSC) reminded us all that it has the last say on regulating AI and machine learning consumer product safety. The CPSC is an independent agency comprised of five commissioners who are nominated by the president and confirmed by the Senate to serve staggered seven-year terms. With the Biden administration's shift away from the deregulation agenda of the prior administration and three potential opportunities to staff the commission, consumer product manufacturers, distributors, and retailers should expect increased scrutiny and enforcement.2

As algorithmic decision-making systems are becoming more pervasive, it is crucial to ensure such systems do not become mechanisms of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. Moreover, due to the inherent trade-off between fairness measures and accuracy, it is desirable to learn fairness-enhanced models without significantly compromising the accuracy. In this paper, we propose Pareto efficient Fairness (PEF) as a suitable fairness notion for supervised learning, that can ensure the optimal trade-off between overall loss and other fairness criteria. The proposed PEF notion is definition-agnostic, meaning that any well-defined notion of fairness can be reduced to the PEF notion. To efficiently find a PEF classifier, we cast the fairness-enhanced classification as a bilevel optimization problem and propose a gradient-based method that can guarantee the solution belongs to the Pareto frontier with provable guarantees for convex and non-convex objectives. We also generalize the proposed algorithmic solution to extract and trace arbitrary solutions from the Pareto frontier for a given preference over accuracy and fairness measures. This approach is generic and can be generalized to any multicriteria optimization problem to trace points on the Pareto frontier curve, which is interesting by its own right. We empirically demonstrate the effectiveness of the PEF solution and the extracted Pareto frontier on real-world datasets compared to state-of-the-art methods.

The 24 March, 2020 will be remembered by some for the news that Prince Charles tested positive for Covid and was isolating in Scotland. In Athens it was memorable as the day the traffic went silent. Twenty-four hours into a hard lockdown, Greeks were acclimatising to a new reality in which they had to send an SMS to the government in order to leave the house. As well as millions of text messages, the Greek government faced extraordinary dilemmas. The European Union's most vulnerable economy, its oldest population along with Italy, and one of its weakest health systems faced the first wave of a pandemic that overwhelmed richer countries with fewer pensioners and stronger health provision. One Greek who did go into the office that day was Kyriakos Pierrakakis, the minister for digital transformation, whose signature was inked in blue on an agreement with the US technology company, Palantir. The deal, which would not be revealed to the public for another nine months, gave one of the world's most controversial tech companies access to vast amounts of personal data while offering its software to help Greece weather the Covid storm. The zero-cost agreement was not registered on the public procurement system, neither did the Greek government carry out a data impact assessment – the mandated check to see whether an agreement might violate privacy laws. The questions that emerge in pandemic Greece echo those from across Europe during Covid and show Palantir extending into sectors from health to policing, aviation to commerce and even academia.

At the warehouses of British online grocery company Ocado Technology, robots, guided by AI, whizz around on rails at speeds of up to four metres per second, picking a 50-item order in minutes. The journeys then taken by Ocado's delivery trucks are optimised by a neural network that makes more than 14 million last-mile routing calculations per second, and adjusts delivery routes each time a customer places a new order or adds extra items to their shopping lists. But Ocado's most ambitious automation efforts involve packing robots. At the time of writing the company has five robotic picking arms powered by computer vision, and other machine-learning systems that can identify the products that need to be packed and use suction power to grab them. Further advances, undertaken in conjunction with two European academic-led projects, are in the pipeline. "From a human's perspective, it is a fairly simple task to pick and pack, and it doesn't require an awful lot of training," says Alex Harvey, chief of advanced technology at Ocado Technology. "For a computer and for a robot, the dexterous manipulation involved is far beyond the state of the art today to be able to pick and pack the full range of items that we do."