Ongoing Research

Large language models (LLMs) have significantly advanced natural language processing, but their progress has yet to be equal across languages. While most LLMs are trained in high-resource languages like English, multilingual models generally underperform monolingual ones. Additionally, aspects of their multilingual foundation sometimes restrict the byproducts they produce, like computational demands and licensing regimes. Hence, we developed the TeenyTinyLlama pair: two compact models for Brazilian Portuguese text generation.

Worldwide AI Ethics is a systematic literature review done by AIRES researchers. Building on the work done by other meta-analysts, this study presents an analysis of 200 documents related to AI ethics and governance, presenting a collection of typologies used to classify our sample, all condensed into an interactive, freely accessible online tool.

Aira is a series of chatbots achieved via instruction-tuning and DPO. Thi sseries was developed to help researchers explore the challenges related to the Alignment problem. All models and datasets developed are part of Nicholas Kluge's doctoral dissertation, "Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment."

The Teeny-tiny Castle is a repository of educational tools for AI Ethics and Safety research. It is a python based course on how to create and use tools for addressing certain safety issues in AI (e.g., interpretability, sustainability, fairness, robustness). In it, you can also find an introductory course on Machine Learning.

We have built a web application to automate an experiment in moral philosophy. This experiment aims to find out how ethical guidelines influence software developers' decision making. We also are seeking to investigate different forms of how we can "teach ethics" in STEM fields.

This projects aims at creating a library of published machine learning models. This library would contain information about these models, their capabilities, and the possible risks (together with ethical concerns) regarding their development/utilization. The aim is to map the biggest problems and threats that contemporary AI poses to our society.

Ethical Problem-Solving (EPS) is a framework aimed at promoting the development of safe and ethical artificial intelligence. It is divided into an evaluation stage (performed via Algorithmic Impact Assessment tools) and a recommendation stage. Both these stages represent distinct steps in a human-centered EaaS (Ethics as a Service) framework.