The weaponisation of deepfakes against politicians or nation states has become something we're simply going to have to live with,

The debate on whether or not humans have free will has been raging for centuries. Although there are good arguments based on our current understanding of the laws of nature for the view that it is not possible for humans to have free will, most people believe they do. This discrepancy begs for an explanation. If we accept that we do not have free will, we are faced with two problems: (1) while freedom is a very commonly used concept that everyone intuitively understands, what are we actually referring to when we say that an action or choice is "free" or not? And, (2) why is the belief in free will so common? Where does this belief come from, and what is its purpose, if any? In this paper, we examine these questions from the perspective of reinforcement learning (RL). RL is a framework originally developed for training artificial intelligence agents. However, it can also be used as a computational model of human decision making and learning, and by doing so, we propose that the first problem can be answered by observing that people's common sense understanding of freedom is closely related to the information entropy of an RL agent's normalized action values, while the second can be explained by the necessity for agents to model themselves as if they could have taken decisions other than those they actually took, when dealing with the temporal credit assignment problem. Put simply, we suggest that by applying the RL framework as a model for human learning it becomes evident that in order for us to learn efficiently and be intelligent we need to view ourselves as if we have free will.

International economics has a long history of improving our understanding of factors causing trade, and the consequences of free flow of goods and services across countries. The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and pandemics, raise the need for improved predictions to inform policy decisions. AI methods are allowing economists to solve such prediction problems in new ways. In this manuscript, we present novel methods that predict and associate food and agricultural commodities traded internationally. Association Rules (AR) analysis has been deployed successfully for economic scenarios at the consumer or store level, such as for market basket analysis. In our work however, we present analysis of imports and exports associations and their effects on commodity trade flows. Moreover, Ensemble Machine Learning methods are developed to provide improved agricultural trade predictions, outlier events' implications, and quantitative pointers to policy makers.

Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide library of algorithms for different problems. One important notion for the adoption of AI algorithms into operational decision process is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 - 2021, and aims to provide a structured alternative to the landscape. A new AI assurance definition is adopted and presented and assurance methods are contrasted and tabulated. Additionally, a ten-metric scoring system is developed and introduced to evaluate and compare existing methods. Lastly, in this manuscript, we provide foundational insights, discussions, future directions, a roadmap, and applicable recommendations for the development and deployment of AI assurance.

The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e. Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial), lead to shifts in planning and budgeting, but most importantly, reduces confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This manuscript presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.

Participatory Budgeting (PB) is a process in which voters decide how to allocate a common budget; most commonly it is done by ordinary people -- in particular, residents of some municipality -- to decide on a fraction of the municipal budget. From a social choice perspective, existing research on PB focuses almost exclusively on designing computationally-efficient aggregation methods that satisfy certain axiomatic properties deemed "desirable" by the research community. Our work complements this line of research through a user study (N = 215) involving several experiments aimed at identifying what potential voters (i.e., non-experts) deem fair or desirable in simple PB settings. Our results show that some modern PB aggregation techniques greatly differ from users' expectations, while other, more standard approaches, provide more aligned results. We also identify a few possible discrepancies between what non-experts consider \say{desirable} and how they perceive the notion of "fairness" in the PB context. Taken jointly, our results can be used to help the research community identify appropriate PB aggregation methods to use in practice.

In the fall of 2018, corporate finance advisory firm Hampleton published a report titled, "The healthtech sector is currently one of the most dynamic in technology M&A." As a summary of the report notes, "aging populations, increasing patient demands and the rise of lifestyle diseases, coupled with pressure on the costs for delivering care are forcing healthcare providers to innovate to improve the quality of their services and lower their prices." Those innovations are made possible by technologies that range from blockchain and artificial intelligence to big data analysis and advanced sensors. IoT connectivity plays a key role, too. And data is central, but not on its own.

The United Nations' High Commissioner for Human Rights Michelle Bachelet is lobbying for a temporary suspension on usage and sale of Artificial Intelligence systems, according to National Public Radio. BGSU's campus is no stranger to this branch of computer science, as it is seeing integration of new artificial intelligence products in the form of Starship Robots, and may very well be home to more advanced tech in the coming years. Bachelet's request comes in response to a United Nations report out of Geneva detailing unaddressed risks of AI. The field of AI was formally founded at Dartmouth College in 1956. It is commonly defined as the ability of machines to self-learn, without explicitly being programmed to do so.

The Virginia Information Technologies Agency (VITA) is working to offer artificial intelligence as a service to the state's 65 agencies. VITA is rolling out the Digital Customer Experience, an AI service that's essentially a basic chatbot that state agencies can implement into user-facing functions. For more complex AI and machine learning solutions, VITA will provide the country's first public-sector AI as a service. "What we wanted to do was design a service that avoids the common AI pitfalls," said Jon Ozovek, chief operating officer at VITA. "AI at this point in time is highly predicated on data, so you have 65 different agencies with 65 different levels of understanding of business processes, 65 different levels of datasets, 65 different levels of data security. Al as a service will look like this, he explained: VITA will conduct an intake process for state agencies that want to implement AI to get a solid understanding of the business process it would apply to, the datasets ...

North Carolinians may someday have pizzas or groceries delivered to their homes or businesses by driverless vehicles, under a bill signed into law by Gov. Roy Cooper this week. The bill allows fully autonomous vehicles designed to deliver cargo to operate on public streets and highways in the state. The new law refers to them as "neighborhood occupantless vehicles" and permits their use on roads with a speed limit of 45 mph or less. It's not clear where and when such vehicles will appear in North Carolina. But Rep. Jason Saine, one of the bill's primary sponsors, says he wanted to make sure driverless vehicles would be legal when companies want to begin introducing them to the state.