Welcome to our October 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we learn about a Nigerian sign language dataset, hear from researchers working on different robotic control projects, and dig into the latest governmental AI policies. Steven Kolawole created a pioneering dataset for Nigerian sign language, in collaboration with a TV sign language broadcaster and two schools in Nigeria. He used this dataset of over 8000 images to create a model to convert sign language to text or speech. In this interview, Steven told us about the goals of this research, his methodology, and how the work has inspired research in other languages.

What was your answer the last time your spouse or partner asked how much you like them? Whatever your answer was, it probably did not include a unit of measurement. For most people, it is cognitively hard to tell how much they like somebody or something with objectivity. This has an invisible but crucial effect on how we humans make group decisions. Namely, due to the difficulty of quantifying strengths of preferences, as well as the potential that individuals might deliberately misrepresent strengths to manipulate outcomes, most voting systems (or group decision protocols) only elicit a ranking of alternatives from voters.

The IJCAI distinguished paper awards recognise some of the best papers presented at the conference each year. This year, three articles were named as distinguished papers. The winners were selected by the associate programme committee chairs, the programme and general chairs, and the president of EurAI. Abstract: The metric distortion framework posits that n voters and m candidates are jointly embedded in a metric space such that voters rank candidates that are closer to them higher. A voting rule's purpose is to pick a candidate with minimum total distance to the voters, given only the rankings, but not the actual distances.