The rapid advancement of Artificial Intelligence (AI) technologies has already transformed business operations across the globe. From customer service chat-bots to adaptive cybersecurity, the applications of AI are nearly limitless. When properly designed, AI can help minimize paperwork, reduce costs, and drive better business decisions by increasing the predictive accuracy of future outcomes and mitigating the cognitive biases inherent in human decision making. In the financial services industry, AI has the potential to expand access to affordable credit for consumers and small businesses and combat fraud and financial crimes, but many financial institutions remain reluctant to deploy AI to its maximum potential without clear guidance from US regulatory agencies. Like many new technologies, the current AI landscape lacks a depth of established legal and regulatory precedent to rely on.

Not a day passes without a fascinating snippet on the ethical challenges created by "black box" artificial intelligence systems. These use machine learning to figure out patterns within data and make decisions – often without a human giving them any moral basis for how to do it. Classics of the genre are the credit cards accused of awarding bigger loans to men than women, based simply on which gender got the best credit terms in the past. Or the recruitment AIs that discovered the most accurate tool for candidate selection was to find CVs containing the phrase "field hockey" or the first name "Jared". More seriously, former Google CEO Eric Schmidt recently combined with Henry Kissinger to publish The Age of AI: And Our Human Future, a book warning of the dangers of machine-learning AI systems so fast that they could react to hypersonic missiles by firing nuclear weapons before any human got into the decision-making process. In fact, autonomous AI-powered weapons systems are already on sale and may in fact have been used.

What comes to mind when we imagine a cutting-edge, tech-savvy workplace? But recent advances in technology, especially Artificial Intelligence, have attracted them too. AI-based tools have the ability to parse an unlimited amount of data, recognise patterns and apply them to new information. This allows legislators to have a dialogue with large constituents, analyse diverse opinions, participate remotely in plenary and committee meetings, and reduce paperwork through digitisation. Where is India in this picture?

The maturity of artificial intelligence (AI) was evident this year, as the conversations in the industry shifted focus from deployment and innovation to ethics and legislation of algorithms. Building better data foundations to make the most of AI The UK government's national artificial intelligence strategy relies on businesses putting in place the foundations for better use of data – EY research highlights the challenges ahead. Making machine learning operational As artificial intelligence matures, IT departments will need to take control of change management and governance of data models. Self-regulation of AI is not an option The House of Lords Communications and Digital Committee recently took evidence from two experts, who were asked to share their thoughts on regulating artificial intelligence. Europe's proposed AI regulation falls short on protecting rights The European Commission's proposal for artificial intelligence regulation focuses on creating a risk-based, market-led approach replete with self-assessments, transparency procedures and technical standards, but critics warn it falls short.

In November 2021 the All-Party Parliamentary Group ("APPG") on the Future of Work ("Future of Work") published its report titled "The New Frontier: Artificial Intelligence at Work" (the "Report"). The Report follows the National AI Strategy (the "Strategy") released by the government in September and sets out to identify and resolve challenges posed by artificial intelligence ("AI") in the workplace through the development of a new regulatory framework. Whilst the proposed framework addresses AI in the workforce, we consider some of the principles could be applied across all sectors. The recommendations made by the Future of Work inform the wider debate about AI governance and regulation as part of the Strategy. APPGs are informal cross-party groups that have no official status in Parliament but are run by and for Members of the Commons and Lords, bringing together parliamentarians, industry and civil society. There is an Artificial Intelligence APPG, but the author of the Report is the Future of Work, an APPG which aims to "foster understanding of the challenges and opportunities of technology and the future of work".

The New York City Council voted 38-4 on November 10, 2021 to pass a bill that would require hiring vendors to conduct annual bias audits of artificial intelligence (AI) use in the city's processes and tools. Companies using AI-generated resources will be responsible for disclosing to job applicants how the technology was used in the hiring process, and must allow candidates options for alternative approaches such as having a person process their application instead. For the first time, a city the size of New York will impose fines for undisclosed or biased AI use, charging up to $1,500 per violation on employers and vendors. Lapsing into law without outgoing Mayor DeBlasio's signature, the legislation is now set to take effect in 2023. It is a telling move in how government has started to crack down on AI use in hiring processes and foreshadows what other cities may do to combat AI-generated bias and discrimination.

Individual human rights, privacy and the free press are under siege in an increasing number of countries around the world. Liberty itself is being challenged by authoritarian governments whose power is enhanced by the unethical use of social media, facial recognition technology and the ability to intercept private communications. Even in democracies, disinformation and doctored videos are often used on social media to undermine confidence in political leaders. Conspiracy theories abound, amplified by unregulated technology. As we have seen in the United States, democracy is threatened when a high percentage of citizens lose confidence in governance and the electoral system.

By moving from massive antennas to antenna surfaces for software-defined wireless systems, the reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control the scattering and reflection profiles of signals, mitigating the propagation loss and multipath attenuation, and thereby improving the coverage and spectral efficiency. In this paper, covert communication is considered in the presence of the RIS. While there is an ongoing transmission boosted by the RIS, both the intended receiver and an eavesdropper individually try to detect this transmission using their own deep neural network (DNN) classifiers. The RIS interaction vector is designed by balancing two (potentially conflicting) objectives of focusing the transmitted signal to the receiver and keeping the transmitted signal away from the eavesdropper. To boost covert communications, adversarial perturbations are added to signals at the transmitter to fool the eavesdropper's classifier while keeping the effect on the receiver low. Results from different network topologies show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications.

Autonomous driving has achieved a significant milestone in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles on roads promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate more reliably without human interventions. However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, the AI systems of autonomous vehicles also need to explain how these decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds a comprehensive light on developing explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the present gaps with respect to explanations in the state-of-the-art autonomous vehicle industry. We then show the taxonomy of explanations and explanation receivers in this field. Thirdly, we propose a framework for an architecture of end-to-end autonomous driving systems and justify the role of XAI in both debugging and regulating such systems. Finally, as future research directions, we provide a field guide on XAI approaches for autonomous driving that can improve operational safety and transparency towards achieving public approval by regulators, manufacturers, and all engaged stakeholders.

As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI models and AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our survey highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other's work and produce generalizable scientific knowledge. We also hope this survey will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.