Since 2017, when Canada became the first country to adopt a national AI strategy, at least 60 countries have adopted some form of policy for artificial intelligence (AI). The prospect of an estimated boost of 16 percent, or US$13 trillion, to global output by 2030 has led to an unprecedented race to promote AI uptake across industry, consumer markets, and government services. Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025. At the same time, the work on developing global standards for AI has led to significant developments in various international bodies. These encompass both technical aspects of AI (in standards development organizations (SDOs) such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE) among others) and the ethical and policy dimensions of responsible AI.

Researchers at the Allen Institute for AI created Ask Delphi to make ethical judgments — but it turned out to be awfully bigoted and racist instead.

Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses calls for specific methods to understand and generate such documents. In this paper, we introduce the task of clause recommendation, asa first step to aid and accelerate the author-ing of contract documents. We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context. We pretrain BERT on an existing library of clauses with two additional tasks and use it for our prediction and recommendation. We experiment with classification methods and similarity-based heuristics for clause relevance prediction, and generation-based methods for clause recommendation, and evaluate the results from various methods on several clause types. We provide analyses on the results, and further outline the advantages and limitations of the various methods for this line of research.

We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. At each time step, a decision maker accepts or rejects the given applicant using a pre-trained supervised learning model until all the vacant positions are filled. In this paper, we discuss whether the fairness notions (e.g., equal opportunity, statistical parity, etc.) that are commonly used in classification problems are suitable for the sequential selection problems. In particular, we show that even with a pre-trained model that satisfies the common fairness notions, the selection outcomes may still be biased against certain demographic groups. This observation implies that the fairness notions used in classification problems are not suitable for a selection problem where the applicants compete for a limited number of positions. We introduce a new fairness notion, ``Equal Selection (ES),'' suitable for sequential selection problems and propose a post-processing approach to satisfy the ES fairness notion. We also consider a setting where the applicants have privacy concerns, and the decision maker only has access to the noisy version of sensitive attributes. In this setting, we can show that the perfect ES fairness can still be attained under certain conditions.

'Gutfeld!' panel weighs in on China's response to the statement This is a rush transcript of "Gutfeld" on October 22, 2021. This copy may not be in its final form and may be updated. Bad things are happening, but it's OK because we're all in this together. What did we get from Joe? An incoherent jumble of memories and confused looks. What the hell was that? JOE BIDEN, PRESIDENT OF THE UNITED STATES: Forty percent of all products coming into the United States of America on the West Coast go through Los Angeles and -- what am I doing here? COOPER: Do you have plans to visit the southern border? BIDEN: I've been there before and I haven't -- I mean, I know it well. I guess I should go down. But what you see is wages are actually up. I have the freedom to kill you. My guess is you'll start to see gas prices come down as we get by -- and going into the winter. I mean, excuse me, and then next year in 2022. I must tell you, I don't have a near-term answer. Well, that was the opposite of comforting. It seems his only strategy is to deflect from our current misery to promising more misery. Angelo Negri was from memory ranch. And she came up to me one day when I was -- when they just had announced that I had flown one million some X number of miles on Air Force aircraft. And asked, she comes up and I'm getting in the car and he goes, Joey baby, what do you do?

The Council of Europe and the Hungarian presidency of its Committee of Ministers are holding an online international conference on 26 October to discuss the challenges governments face to regulate artificial intelligence (AI) in a coordinated manner. Under the theme "Current and Future Challenges of Coordinated Policies on AI Regulation", the event will showcase various AI governance models and examine the interplay between national policies and the work of the Council of Europe and other organisations. One of the main contributions of the Council of Europe in this field is the work of the intergovernmental AI expert body CAHAI, which is examining the development of an international legal framework for the development, design and application of artificial intelligence based on the Council of Europe's standards on human rights, democracy and the rule. Representatives of international organisations, national policy experts, IT companies, civil society and academia will discuss the way to improve AI policymaking at the global, regional and national level. They will also examine case studies on best practices of AI governance and discuss issues such as the possible long-term societal effects of AI and the sustainable development of AI applications.

With new opportunities, risks, and threats to prosperity and security at stake, the promise and peril associated with this foundational technology are too vast for any single actor to manage alone. As a result, cooperation is inherently needed to equally mitigate international security risks, as well as to capitalise on the technology's potential to transform enterprise functions, mission support, and operations. The continued ability of the Alliance to deter and defend against any potential adversary and to respond effectively to emerging crises will hinge on its ability to maintain its technological edge. Militarily, futureproofing the comparative advantage of Allied forces will depend on a common policy basis and digital backbone to ensure interoperability and accordance with international law. With the fusion of human, information, and physical elements increasingly determining decisive advantage in the battlespace, interoperability becomes all the more essential.

Can artificially intelligent systems follow rules? The answer might seem an obvious `yes', in the sense that all (current) AI strictly acts in accordance with programming code constructed from highly formalized and well-defined rulesets. But here I refer to the kinds of rules expressed in human language that are the basis of laws, regulations, codes of conduct, ethical guidelines, and so on. The ability to follow such rules, and to reason about them, is not nearly as clear-cut as it seems on first analysis. Real-world rules are unavoidably rife with open-textured terms, which imbue rules with a possibly infinite set of possible interpretations. Narrowing down this set requires a complex reasoning process that is not yet within the scope of contemporary AI. This poses a serious problem for autonomous AI: If one cannot reason about open-textured terms, then one cannot reason about (or in accordance with) real-world rules. And if one cannot reason about real-world rules, then one cannot: follow human laws, comply with regulations, act in accordance with written agreements, or even obey mission-specific commands that are anything more than trivial. But before tackling these problems, we must first answer a more fundamental question: Given an open-textured rule, what is its correct interpretation? Or more precisely: How should our artificially intelligent systems determine which interpretation to consider correct? In this essay, I defend the following answer: Rule-following AI should act in accordance with the interpretation best supported by minimally defeasible interpretive arguments (MDIA).

To enable data-driven scenario planning, we take the flows is, therefore, a requisite to better plan urban areas. In this first steps in leveraging the Geo-contextual Multitask Embedding context, an important task is to study hypothetical scenarios in Learner (GMEL) model, previously proposed in Liu et al. [16], as our which possible future changes are evaluated. For instance, how the base model for predicting commuting flows based on geographic increase in residential units or transportation modes in a neighborhood information (e.g., infrastructure, land use, transportation). Commuting will change the commuting flows to or from that region? In flows are defined as flows between a workers' residence this paper, we propose to leverage GMEL, a recently introduced location and a workplace location. While major cities have the resources graph neural network model, to evaluate changes in commuting to collect and process high-resolution land use data, other flows taking into account different land use and infrastructure scenarios.