The increasing prevalence of image-altering filters on social media and video conferencing technologies has raised concerns about the ethical and psychological implications of using Artificial Intelligence (AI) to manipulate our perception of others. In this study, we specifically investigate the potential impact of blur filters, a type of appearance-altering technology, on individuals' behavior towards others. Our findings consistently demonstrate a significant increase in selfish behavior directed towards individuals whose appearance is blurred, suggesting that blur filters can facilitate moral disengagement through depersonalization. These results emphasize the need for broader ethical discussions surrounding AI technologies that modify our perception of others, including issues of transparency, consent, and the awareness of being subject to appearance manipulation by others. We also emphasize the importance of anticipatory experiments in informing the development of responsible guidelines and policies prior to the widespread adoption of such technologies.

The Mozambique National Institute for Disaster Management and Risk Reduction (INGD) and World Food Programme (WFP) built the case for drones' capacity to give all responders an accurate picture of cyclone damage and flooding extent. Two back-to-back cyclones battered Mozambique in 2019, destroying more than 800,000 hectares of farmland during harvest season. The devastation to crops and livelihoods left nearly two million people facing acute food insecurity. The United Nations (UN) World Food Programme (WFP) responded quickly, with two helicopters to ferry supplies and rescue stranded people. Given flooded roads, the air support was crucial but not nearly enough to distribute food and find stranded people across such a wide area of impact.

An international team of researchers have built a set of machine learning models they say can help predict global food shortages in the near future, helping governments and international agencies understand where they can best help. Scientists from the World Food Programme, University of London Mathematics Department and Central European University Department of Network and Data Science, made use of a "unique global dataset" to build machine learning models that can explain up to 81 percent of the variation in insufficient food consumption. The study claims the machine learning models draw from indirect data sources in areas such as food prices, macro-economic indicators (including GDP), weather, conflict, prevalence of undernourishment, population density, and previous food insecurity trends. The aim is to create near-term forecasts, or "nowcasts." "We show that the proposed models can nowcast the food security situation in near real-time and propose a method to identify which variables are driving the changes observed in predicted trends -- which is key to make predictions serviceable to decision-makers," the research paper published in Nature Food this week said. The outputs of the ML models have been used to create a world map including near-term food insecurity forecasts called HungerMap.

Roughly half of the 821 million people considered hungry by the United Nations are those who dedicate their lives to producing food for others: farmers. This is largely attributed to the vulnerability of farmers to agricultural risks, such as extreme weather, conflict, and market shocks. Smallholder farmers, who produce some 60-70% of the world's food, are particularly vulnerable to risks and food insecurity. Emerging technologies such as Artificial Intelligence (AI), however, have been particularly promising in tackling challenges such as lack of expertise, climate change, resource optimization and consumer trust. AI assistance can, for instance, enable smallholder farmers in Africa to more effectively address scourges such as viruses and the fall armyworm that have plagued the region over the last 40 years despite extensive investment, said David Hughes, Co-Founder of PlantVillage and Assistant Professor at Penn State University at a session on AI for Agriculture at last week's AI for Good Global Summit.