Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration, agents can use either the classical Continuous Prisoner's Dilemma or a new type of interaction called moral interaction, where agents donate or steal from other agents. We introduce moral interactions to (...) model ethical decision making. We also propose a novel agent type, called virtue agent, parametrised by the agent's level of ethics. Virtue agents' decisions are based on moral evaluations of past interactions. Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them to those of virtue agents. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

Introduction: Liposomal bupivacaine (LB) is a long-acting analgesic that, due to its liposomal formulation, purportedly extends its analgesic effect up to 72 hours. However, the clinical efficacy of LB appears mixed. This systematic review seeks to evaluate the effectiveness of liposomal bupivacaine in improving postoperative outcomes compared to ropivacaine (ROPI), another commonly used long-acting analgesic. -/- Materials and Methods: Prospective and randomized controlled trials (RCTs) evaluating the efficacy of LB compared to ROPI were selected for review. Primary outcomes included hospital (...) length of stay (LOS) and postoperative opioid consumption measured in oral morphine equivalents (OME). Secondary outcomes included analgesic cost. -/- Results: 14 studies met the review criteria. We found that LB and ROPI are equivalent in managing postoperative pain. 8 of the 14 trials reported equal efficacy between LB and ROPI as determined by OME post-procedure and 10 of the 14 trials reported similar LOS after surgery. These findings remained consistent across multiple surgical procedures and multiple drug administrative modalities. -/- Conclusion: Our systematic review found that LB was not superior to ROPI in reducing postoperative OME use and hospital LOS. The only consistent finding was the significantly increased cost of LB compared to that of ROPI. Therefore, the use of LB over ROPI cannot be justified. (shrink)