Artificial intelligence tools in hiring have so far remained unregulated by U.S. civil rights agencies, despite growing use and potential discrimination risks. One EEOC official wants that to change. "What is unfair is if there are enforcement actions or litigation, both from the government and from the private sector, against those who are using the technologies, and the federal agency responsible for administering the laws has said nothing," Keith Sonderling, a Republican commissioner on the U.S. Equal Employment Opportunity Commission, told Bloomberg Law in an exclusive interview. The use of artificial intelligence for recruitment, resume screening, automated video interviews, and other employment tasks has for years been on the radar of federal regulators and lawmakers, as workers began filing allegations of AI-related discrimination to the EEOC. Attorneys have warned that bias litigation could soon be on the horizon.

Transformers changed modern NLP in many ways. However, they can hardly exploit domain knowledge, and like other blackbox models, they lack interpretability. Unfortunately, structured knowledge injection, in the long run, risks to suffer from a knowledge acquisition bottleneck. We thus propose a memory enhancement of transformer models that makes use of unstructured domain knowledge expressed in plain natural language. An experimental evaluation conducted on two challenging NLP tasks demonstrates that our approach yields better performance and model interpretability than baseline transformer-based architectures.

In cognitive science, behaviour is often separated into two types. Reflexive control is habitual and immediate, whereas reflective is deliberative and time consuming. We examine the argument that Hierarchical Predictive Coding (HPC) can explain both types of behaviour as a continuum operating across a multi-layered network, removing the need for separate circuits in the brain. On this view, "fast" actions may be triggered using only the lower layers of the HPC schema, whereas more deliberative actions need higher layers. We demonstrate that HPC can distribute learning throughout its hierarchy, with higher layers called into use only as required.

The United States Securities and Exchange Commission (SEC) signed a deal with blockchain analytics firm AnChain.AI to help its efforts in monitoring the decentralized finance (DeFi) space. The company's service focuses on tracking illicit activity across crypto exchanges, DeFi protocols, and traditional financial institutions. The contract between the blockchain analysis firm and the SEC started in May and probably played a role in AnChain.AI securing a $10 million Series A round led by Susquehanna Group affiliate SIG Asia Investments LLP. Why It Matters: The regulator is seemingly leveraging the contractor to monitor the DeFi ecosystem more closely, as expected after recent remarks by SEC Chairman Gary Gensler. For instance, Fang explained that it is actually an amalgam of 30,000 separate smart contracts that manages over $1.8 billion worth of transactions in the 24 hours prior to the Forbes article being published.

With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular data generation. The model includes two phases of training. In the first phase, the model is trained to accurately generate synthetic data similar to the reference dataset. In the second phase we modify the value function to add fairness constraint, and continue training the network to generate data that is both accurate and fair. We test our results in both cases of unconstrained, and constrained fair data generation. In the unconstrained case, i.e. when the model is only trained in the first phase and is only meant to generate accurate data following the same joint probability distribution of the real data, the results show that the model beats state-of-the-art GANs proposed in the literature to produce synthetic tabular data. Also, in the constrained case in which the first phase of training is followed by the second phase, we train the network and test it on four datasets studied in the fairness literature and compare our results with another state-of-the-art pre-processing method, and present the promising results that it achieves. Comparing to other studies utilizing GANs for fair data generation, our model is comparably more stable by using only one critic, and also by avoiding major problems of original GAN model, such as mode-dropping and non-convergence, by implementing a Wasserstein GAN.

This paper presents key principles and solutions to the challenges faced in designing a domain-specific conversational agent for the legal domain. It includes issues of scope, platform, architecture and preparation of input data. It provides functionality in answering user queries and recording user information including contact details and case-related information. It utilises deep learning technology built upon Amazon Web Services (aws) lex in combination with aws Lambda. Due to lack of publicly available data, we identified two methods including crowdsourcing experiments and archived enquiries to develop a number of linguistic resources. This includes a training dataset, set of predetermined responses for the conversational agent, a set of regression test cases and a further conversation test set. We propose a hierarchical bot structure that facilitates multi-level delegation and report model accuracy on the regression test set. Additionally, we highlight features that are added to the bot to improve the conversation flow and overall user experience.

Participants sit at computers to play a video game at the 2019 DreamHack video gaming festival in Leipzig, Germany. GPUs were originally designed for the video gaming industry because they are particularly good at matrix arithmetic. As AI and machine learning become more and more widespread in the global economy, there is an increasing focus on the hardware that drives them. Currently, nearly all AI systems run on a chip known as a GPU that was designed for video gaming. Are current chip designs fit for purpose in an AI future, or is a new type of chip needed?

The world's food system is in disarray. One in ten people is undernourished. One in four is overweight. More than one-third of the world's population cannot afford a healthy diet. Food supplies are disrupted by heatwaves, floods, droughts and wars.

In this position paper, we present five key principles, namely interpretability, inherent capability to explain, independent data, interactive learning, and inquisitiveness, for the development of conversational AI that, unlike the currently popular black box approaches, is transparent and accountable. At present, there is a growing concern with the use of black box statistical language models: While displaying impressive average performance, such systems are also prone to occasional spectacular failures, for which there is no clear remedy. In an effort to initiate a discussion on possible alternatives, we outline and exemplify how our five principles enable the development of conversational AI systems that are transparent and thus safer for use. We also present some of the challenges inherent in the implementation of those principles.

In this paper we outline a proposal for improving the governance of artificial intelligence (AI) by investing in government capacity to systematically measure and monitor the capabilities and impacts of AI systems. If adopted, this would give governments greater information about the AI ecosystem, equipping them to more effectively direct AI development and deployment in the most societally and economically beneficial directions. It would also create infrastructure that could rapidly identify potential threats or harms that could occur as a consequence of changes in the AI ecosystem, such as the emergence of strategically transformative capabilities, or the deployment of harmful systems. We begin by outlining the problem which motivates this proposal: in brief, traditional governance approaches struggle to keep pace with the speed of progress in AI. We then present our proposal for addressing this problem: governments must invest in measurement and monitoring infrastructure. We discuss this proposal in detail, outlining what specific things governments could focus on measuring and monitoring, and the kinds of benefits this would generate for policymaking. Finally, we outline some potential pilot projects and some considerations for implementing this in practice.