Public policies that supply public goods, especially those involve collaboration by limiting individual liberty, always give rise to controversies over governance legitimacy. Multi-Agent Reinforcement Learning (MARL) methods are appropriate for supporting the legitimacy of the public policies that supply public goods at the cost of individual interests. Among these policies, the inter-regional collaborative pandemic control is a prominent example, which has become much more important for an increasingly inter-connected world facing a global pandemic like COVID-19. Different patterns of collaborative strategies have been observed among different systems of regions, yet it lacks an analytical process to reason for the legitimacy of those strategies. In this paper, we use the inter-regional collaboration for pandemic control as an example to demonstrate the necessity of MARL in reasoning, and thereby legitimizing policies enforcing such inter-regional collaboration. Experimental results in an exemplary environment show that our MARL approach is able to demonstrate the effectiveness and necessity of restrictions on individual liberty for collaborative supply of public goods. Different optimal policies are learned by our MARL agents under different collaboration levels, which change in an interpretable pattern of collaboration that helps to balance the losses suffered by regions of different types, and consequently promotes the overall welfare. Meanwhile, policies learned with higher collaboration levels yield higher global rewards, which illustrates the benefit of, and thus provides a novel justification for the legitimacy of, promoting inter-regional collaboration. Therefore, our method shows the capability of MARL in computationally modeling and supporting the theory of calculus of consent, developed by Nobel Prize winner J. M. Buchanan.

There has been growing interest in the development and deployment of autonomous vehicles on modern road networks over the last few years, encouraged by the empirical successes of powerful artificial intelligence approaches (AI), especially in the applications of deep and reinforcement learning. However, there have been several road accidents with ``autonomous'' cars that prevent this technology from being publicly acceptable at a wider level. As AI is the main driving force behind the intelligent navigation systems of such vehicles, both the stakeholders and transportation jurisdictions require their AI-driven software architecture to be safe, explainable, and regulatory compliant. We present a framework that integrates autonomous control, explainable AI architecture, and regulatory compliance to address this issue and further provide several conceptual models from this perspective, to help guide future research directions.

A bill passed by the New York City council early this month aims to ban companies from using artificial intelligent-powered hiring tools that discriminate based on an applicant's gender or race. If signed into law, the legislation will require providers the technology to systems evaluated each year by an audit service and provide the results to companies using those systems. Employers using systems that do not meet requirements could be fined up to $1,500 per violation, but the law states it will be left up to the vendors to conduct the audits and show employers that their tools meet the city's requirements. If the bill is pushed to law, it would go into affect January 2023 and make New York City the first place in the US to rein in AI hiring tools. A bill passed by the New York City council early this month aims to ban companies from using artificial intelligent-powered hiring tools that discriminate based on an applicant's gender or race However, Alexandra Givens, president of the Center for Democracy & Technology, notes that this legislation does not protect against disabilities or age.

Work believes that one key is to train officers on how to work with AI "to get the best features of both humans and machines." Robert Work is a national security professional who served as U.S. deputy secretary of defense for both the Obama and Trump administrations. Success on the battlefield will increasingly come down to the ability to make faster algorithmically aided decisions. "The battle networks of the future will feature human-machine collaboration, and these things will operate at extremely high speed," Work says. "These are going to make battle networks that do not have AI obsolete."

Job candidates rarely know when hidden artificial intelligence tools are rejecting their resumes or analyzing their video interviews. But New York City residents could soon get more say over the computers making behind-the-scenes decisions about their careers. A bill passed by the city council in early November would ban employers from using automated hiring tools unless a yearly bias audit can show they won't discriminate based on an applicant's race or gender. It would also force makers of those AI tools to disclose more about their opaque workings and give candidates the option of choosing an alternative process -- such as a human -- to review their application. Proponents liken it to another pioneering New York City rule that became a national standard-bearer earlier this century -- one that required chain restaurants to slap a calorie count on their menu items.

In the recent past, artificial intelligence has come to the limelight by showing its massive abilities to tackle mundane as well as complex tasks. From complex facial recognition to identifying fraudulent transactions, AI possesses the power to solve challenging problems faced by individuals and organisations on an everyday basis. Most economic sectors, including transportation, retail, advertising, and energy, are being disrupted by data digitization on a large scale, as well as the developing technologies that utilise it. Computerized systems are being employed in government services to increase accuracy and drive objectivity, and AI is influencing democracy and governance. However, as with every other good technology, AI also has a fair share of challenges that is hindering its usage at a large scale.

Joining Illinois and Maryland, on November 10, 2021, the New York City Council approved a measure, Int. The Bill, which is awaiting Mayor DeBlasio's signature, is to take effect on January 1, 2023. Should the Mayor not sign the Bill within thirty days of the Council's approval (i.e., by December 10), absent veto, it will become law. The Bill defines "automated employment decision tool" as "any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence," which scores, classifies, or otherwise makes a recommendation, that is used to substantially assist or replace the decision-making process from that of an individual. The Bill exempts automated tools that do not materially impact individuals, such as a junk email filter, firewall, calculator, spreadsheet, database, data set, or other compilation of data.

A new report by the All-Party Parliamentary Group (APPG) on the Future of Work has called on the government to bring forward robust proposals for artificial intelligence (AI) regulation. The APPG inquiry found that AI is transforming work and working lives across the country in ways that have plainly outpaced, or avoid, the existing regimes for regulation. Their recommendations are aimed at ensuring that the AI ecosystem is genuinely human-centred, principles-driven and accountable to shape a future of better work. They are centred around a proposal for an Accountability for Algorithms Act ('the AAA'). The AAA offers an overarching, principles-driven framework for governing and regulating AI in response to the fast-changing developments in workplace technology.

A few years ago, at the Princeton Club in Manhattan, I chanced on a memorable chat with the Harvard psychologist Steven Pinker. His spouse, the philosopher Rebecca Goldstein, with whom he was tagging along, had been invited onto a panel to discuss the conflict between religion and science and Einstein's so-called "God letter," which was being auctioned at Christie's. Pinker had recently published Enlightenment Now: The Case for Reason, Science, Humanism, and Progress. I was eager to pepper him with questions, mainly on religion, rationality, and evolutionary psychology. I remember I wanted Pinker's take on something Harvey Whitehouse, one of the founders of the cognitive science of religion, told me in an interview--that my own little enlightenment, of becoming an atheist in college, was probably mostly a product of merely changing my social milieu. I wasn't so much moved by rational arguments against the ethics and existence of God but by being distanced from my old life and meeting new, non-religious friends. I recall Pinker almost pouncing on that argument, defending reason's power to change our minds. He noted that people especially high in "intellectance," a personality trait now more commonly called "openness to experience," tend to be more curious, intelligent, and willing to entertain new ideas. I still think that Pinker's way of seeing things made more sense of my experience in those heady days. I really was, for the first time, trying my best to think things through, and it was exhilarating. We talked until the event staff shelved the wine, and parted ways at a chilly midtown intersection.

EARLY LAST year, researchers at the Massachusetts Institute of Technology (MIT) used a machine-learning algorithm to look for new antibiotics. After training the system on molecules with antimicrobial properties, they let it loose on huge databases of compounds and found one that worked. Because it operated in a different way, even bacteria that had developed a resistance to traditional antibiotics could not evade the new drug. Your browser does not support the audio element. Behind the success was a deeper truth: the algorithm was able to spot aspects of reality that humans had not contemplated, might not be able to detect and may never comprehend.