WASHINGTON--Representatives Paul D. Tonko (D-NY) and Guy Reschenthaler (R-PA) have just announced the introduction of their Artificial Intelligence Education Act today, bipartisan legislation that would establish grant support within the National Science Foundation (NSF) to fund the creation of easily-accessible K-12 lesson plans for schools and educators to provide students with the tools, skills and social understanding of artificial intelligence (AI) technologies in 21st-Century Society. "The development of Artificial Intelligence has fundamentally changed the way we live and work, bringing untold potential in the fields of medical science, research and development, engineering, manufacturing and so much more," Congressman Tonko said. "By providing the resources for our children to learn about AI, we ensure that the next generation of our American workforce has the skill necessary to succeed in this rapidly growing field, thereby helping to drive innovation and economic opportunity. Our legislation will deliver the tools needed to teach AI to students across the nation. I thank Congressman Reschenthaler for his partnership as a co-lead of this bill and urge the support of my colleagues in Congress to help secure a bright future for both our students and our economy."

"Yeah, but your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should." It turns out that a $1 billion investment from Microsoft and unfettered access to a supercomputer wasn't enough to keep OpenAI's GPT-3 from being just as bigoted as Tay, the algorithm-based chat bot that became an overnight racist after being exposed to humans on social media. It's only logical to assume any AI trained on the internet – meaning trained on databases compiled by scraping publicly-available text online – would end up with insurmountable inherent biases, but it's still a sight to behold in the the full context (ie: it took approximately $4.6 million to train the latest iteration of GPT-3). What's interesting here is OpenAI's GPT-3 text generator is finally starting to trickle out to the public in the form of apps you can try out yourself. These are always fun, and we covered one about a month ago called Philosopher AI.

We all are moving towards an era of Artificial Intelligence. Earlier when face recognition was something to be amazed at it is now easily implemented using existing libraries and frameworks. Machine learning is now embedded into our lives and it is thickening its grasp with time. Earlier it was a buzzword but now it is a reality that is making our lives easier and better. So let's talk about some of the problems with Machine Learning.

Almost everything is somehow interrelated with everything else—and that should not surprise us.

Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, people. Credit ratings, standardized tests, risk assessments output only a score, but are also used implicitly for ranking. Bias in such ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. In this paper, we study a trade-off between individual fairness and group fairness in ranking. We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous individual and group fairness guarantees comparable to the lower bound we prove. Our algorithm can be used to both pre-process training data as well as post-process the output of existing ranking algorithms. Our experimental results show that our algorithm performs better than the state-of-the-art fair learning to rank and fair post-processing baselines.

Using the concept of principal stratification from the causal inference literature, we introduce a new notion of fairness, called principal fairness, for human and algorithmic decision-making. The key idea is that one should not discriminate among individuals who would be similarly affected by the decision. Unlike the existing statistical definitions of fairness, principal fairness explicitly accounts for the fact that individuals can be influenced by the decision. We introduce an axiomatic assumption that all groups are created equal once we account for relevant covariates. This assumption is motivated by a belief that protected attributes such as race and gender should not directly affect potential outcomes. Under this assumption, we show that principal fairness implies all three existing statistical fairness criteria, thereby resolving the previously recognized tradeoffs between them. Finally, we discuss how to empirically evaluate the principal fairness of a particular decision and the relationships between principal and counterfactual fairness criteria.

Recent work has identified a number of formally incompatible operational measures for the unfairness of a machine learning (ML) system. As these measures all capture intuitively desirable aspects of a fair system, choosing "the one true" measure is not possible, and instead a reasonable approach is to minimize a weighted combination of measures. However, this simply raises the question of how to choose the weights. Here, we formulate Legally Grounded Fairness Objectives (LGFO), which uses signals from the legal system to non-arbitrarily measure the social cost of a specific degree of unfairness. The LGFO is the expected damages under a putative lawsuit that might be awarded to those who were wrongly classified, in the sense that the ML system made a decision different to that which would have be made under the court's preferred measure. Notably, the two quantities necessary to compute the LGFO, the court's preferences about fairness measures, and the expected damages, are unknown but well-defined, and can be estimated by legal advice. Further, as the damages awarded by the legal system are designed to measure and compensate for the harm caused to an individual by an unfair classification, the LGFO aligns closely with society's estimate of the social cost.

Newly, the rates of energy and material consumption to augment industrial pro-duction are substantially high, thus the environmentally sustainable industrial de-velopment has emerged as the main issue of either developed or developing coun-tries. A novel approach to supply chain management is proposed to maintain economic growth along with environmentally friendly concerns for the design of the supply chain network. In this paper, a new green supply chain design approach has been suggested to maintain the financial virtue accompanying the environ-mental factors that required to be mitigated the negative effect of rapid industrial development on the environment. This approach has been suggested a multi-objective mathematical model minimizing the total costs and CO2 emissions for establishing an environmentally sustainable closed-loop supply chain. Two opti-mization methods are used namely Epsilon Constraint Method, and Genetic Al-gorithm Optimization Method. The results of the two mentioned methods have been compared and illustrated their effectiveness. The outcome of the analysis is approved to verify the accuracy of the proposed model to deal with financial and environmental issues concurrently.

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

In today's world, AI systems are used to decide who gets hired, the quality of medical treatment we receive, and whether we become a suspect in a police investigation. While these tools show great promise, they can also harm vulnerable and marginalized people, and threaten civil rights. Unchecked, unregulated and, at times, unwanted, AI systems can amplify racism, sexism, ableism, and other forms of discrimination. The Algorithmic Justice League's mission is to raise awareness about the impacts of AI, equip advocates with empirical research, build the voice and choice of the most impacted communities, and galvanize researchers, policy makers, and industry practitioners to mitigate AI harms and biases. We're building a movement to shift the AI ecosystem towards equitable and accountable AI.