Globally, there has been a significant increase in the number of people using prescription drugs for reasons other than why they were prescribed, sometimes combining them with other substances such as alcohol to sleep better or stimulants to perform better. A team of computer scientists and emergency physicians from Emory, Oregon, and Pennsylvania universities in the United States used AI to analyze drug misuse and the emotions users felt during times of use. The study, "Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use," was published in the journal Health Data Science. In 2021, more than 108,000 people in the U.S. died from drug overdoses, a number that is up 20% from 2020, many of these deaths were caused by the ingestion of prescription drugs, often mixed with other substances. In France, more than 10,000 people die each year as a result of medication misuse.

Determining whether an AI system is maintaining fairness in its predictions requires an understanding of models' short- and long-term effects, which might be informed by disparities in error metrics on a number of static data sets. In some cases, it's necessary to consider the context in which the AI system operates in addition to the aforementioned error metrics, which is why Google researchers developed ML-fairness-gym, a set of components for evaluating algorithmic fairness in simulated social environments. ML-fairness-gym -- which was published in open source on Github this week –is designed to be used to research the long-term effects of automated systems by simulating decision-making using OpenAI's Gym framework. AI-controlled agents interact with digital environments in a loop, and at each step, an agent chooses an action that affects the environment's state. The environment then reveals an observation that the agent uses to inform its next actions, so that the environment models the system and dynamics of a problem and the observations serve as data.

Scientists at the Stanford University have released the first report of their One Hundred Year Study on Artificial Intelligence (AI100), in which they try to predict the potential impacts of artificial intelligence on human life in the long run. Known as "Artificial Intelligence and Life in 2030," the report comes just two years since the researchers began their work. It focuses on the history of AI technology and how it is being used in various fields today, such as in the development of robots for medical purposes and self-driving vehicles for transportation. Experts believe the Stanford study is important not only for researchers but for policymakers as well who may need to create new laws that would better cater to such technological advancements. AI100 is the brainchild of Eric Horvitz, managing director of Microsoft Research's Redmond laboratory.