New technologies have changed operational processes across industries, from supply chain and CRM applications to the rollout of simple Apps that expedite customer touchpoints with organizations. The financial industry is no exception. Technologies have expedited payment processes, trading and investment activities, commercial banking and lending operations, to name a few. Unfortunately, digitization can also introduce a dark side to business activities in the form of cybercrimes. A growing problem in fintech is money laundering. According to the US government, money laundering involves disguising financial assets so they can be used without detection of the illegal activity that produced them and transformed into funds with a legal source. The UN has estimated that up to 5% of global GDP is laundered annually. As a result, regulators have adopted a sound risk management approach, including policies and regulations to mitigate these activities. Difficulties arise, however, as financial organizations seek to maintain their operational efficiencies while adhering to these regulations.
Embodied AI changes the data problem in artificial intelligence. Text, images, and videos can be collected from the digital world at large scale, but robot interaction data must be produced through physical execution [1], [2]. A useful dataset may include camera streams, robot trajectories, force and tactile signals, teleoperation commands, task outcomes, and environmental context. These records are costly because they depend on hardware, operators, calibration, safety checks, and repeated trials. As a result, many embodied AI systems still rely on small demonstrations, isolated laboratory datasets, or task-specific data pipelines.
The democratic evolution of governments across Africa took prominence after a series of military dictatorships following the shift from colonial rule. This era of post-independence democratization efforts, often referred to as the "third wave of democratization" [25], began in the early 90s, with countries such as Benin, Guinea-Bissau, and Zambia transitioning to democratic rule. While organizations like the African Union, initially established as the Organization for African Unity in 1963, have been essential in promoting democratic principles, a growing shift towards authoritarian rule across the continent has resulted in democratic backsliding in countries such as Ethiopia, Côte D'Ivoire, Guinea, the Central African Republic, and Mali [2]. While the COVID-19 pandemic has accelerated this democratic backsliding [22], African countries face enduring issues such as corruption, economic instability, ethnic rivalry, religious conflict, and weak governance institutions that hinder democratic maturity [4, 7]. As political instability, conflict, and foreign intervention undermine the quality of governance, AI will introduce new complexities to these challenges. Issues with access to electrification, a lack of computing infrastructure, and a low concentration of skilled AI talent hinder AI development across the continent. Additionally, as African governments increasingly leverage AI in governance and democratic processes such as elections, AI tools could limit political awareness, intensify voter suppression, and spread disinformation.
Since inception, AI research has been inspired by the idea of building machines that think and behave like humans. Or put another way, machines that think and act rationally [14]. This paradigm has its historical foundations in the assumption that what makes humans different from other forms of life is our capacity for self-reflective, rational thought. This assumption has caused research into artificial intelligence and human intelligence more generally to undervalue the role of emotions in generalized problem-solving. The very definition of rationality has, for much of Western history, discounted emotions from its conception. Where emotions are investigated, they are often treated as irrational, immaterial, or illusory aspects of human experience. This has been an unfortunate mistake.
