Projects

We are proud to share the outstanding work of Harvard ReCompute undergraduate students, who have collaborated with EAAMO this past semester. Their efforts focused on reading and highlighting critical questions from papers presented at previous ACM EAAMO conferences, contributing valuable insights to the broader community. As a result we present to you Sociotechnical and System Considerations for End-to-end Fairness: An Annotated Reading List.

As machine learning and data science applications grow ever more prevalent, the search for quality data has also increased. The Global South plays a central role in the global data landscape – from being the target for “untapped” data mining, to contributing to the bulk of data labelling (ghost work) labour, to driving data sharing practices across the globe.

The goal of the Summer of Science Program is to support female students from indigenous communities in Mexico as they design and execute research projects aimed at improving their local communities.

The EAAMO Algorithm, Law, and Policy Working Groups members examine the impact of advanced AI technology within the European Union’s Digital Markets Act. The team argue for the inclusion of generative AI in the DMA’s scope. They suggest that some generative AI services have characteristics similar to those of major platform controllers and should be identified as ‘core platform services’ in the DMA.

This chapter provides insights into the challenges and opportunities of adopting and implementing AI technologies in Africa. The study explores the concept of responsible AI and its implications for technologies developed and used in Africa. It further analyzes the hurdles for effective adoption and implementation of AI, including digital literacy, the scarcity of local AI talent, and governmental barriers.

Faidra Monachou and Ana-Andreea Stoica wrote the Fairness and equity in resource allocation and decision-making: an annotated reading list for ACM SIGecom Exchanges.

Members of the MD4SG Discrimination and Equality in Algorithmic Decision-Making Working Group organized themselves and wrote a position paper on the current state of fair rankings. Read about their experiences while working together to produce a paper presented at ACM FAccT 2022.

At the beggining of the pandemics, our members gathered to conduct an experiment about what makes people donate towards COVID-19 vaccines.

Jessica Finocchiaro and Faidra Monachou wanted to share their experiences while working on the project Bridging Mechanism Design and Machine Learning towards Algorithmic Fairness that got published at ACM Conference on Fairness, Accountability, and Transparency 2021.