Recent Publications
Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance
In the last decade, a great number of organizations have produced documents intended to standardize,
in the normative sense, and promote guidance to our recent and rapid AI development. However, the
full content and divergence of ideas presented in these documents have not yet been analyzed,
except for a few meta-analyses and critical reviews of the field. In this work, we seek to
expand on the work done by past researchers and create a tool for better data visualization of the
contents and nature of these documents.
We also provide our critical analysis of the results acquired by the application of our tool into a sample size
of 200 documents.
Risks of Using Facial Recognition Technologies in Public Security Applications
Counterfactual Analysis by Algorithmic Complexity: A metric between possible worlds
Counterfactuals have become an important area of interdisciplinary interest, especially in logic, philosophy of language, epistemology,
metaphysics, psychology, decision theory, and even artificial intelligence. In this study, we propose a new form of analysis for
counterfactuals: analysis by algorithmic complexity, inspired by Lewis-Stalnaker's Possible Worlds Semantics.
Engaging in a dialogue with literature this study will seek to bring new insights and tools to the debate, so that the object of interest,
counterfactuals, may be understood in an intuitively plausible way, and a philosophically justifiable manner, aligned with the way we
usually think about counterfactual propositions and our imaginative reasoning.
On the efficiency of ethics as a governing tool for artificial intelligence
Meta-analyses of the AI Ethics research field point to convergence on certain ethical principles that supposedly govern
the AI industry. However, little is known aboutthe effectiveness of this form of "Ethics."" In this paper, we would like
to conducta critical analysis of the current state of AI Ethics and suggest that this form ofgovernance based on principled
ethical guidelines is not sufficient to norm theAI industry and its developers. We believe that drastic changes are necessary,
both in the training processes of professionals in the fields related to the development of software and intelligent systems
and in the increased regulation ofthese professionals and their industry.
To this end, we suggest that law shouldbenefit from recent contributions from bioethics, to make the contributions of AI ethics
to governance explicit in legal terms.
Progress in the Federal Senate 2022 PL 21/20 - PL 5051/19 - PL 872/21
Good AI for the Present of Humanity Democratizing AI Governance
Singularity and Coordination Problems: Pandemic Lessons from 2020
Metanormativity: Solving questions of moral and empirical uncertainty
How can someone reconcile the desire to eat meat, and a tendency toward vegetarian ideals? How should we reconcile
contradictory moral values? How can we aggregate different moral theories? How individual preferences can be
fairly aggregated to represent a will, norm, or social decision? Conflict resolution and preference aggregation
are tasks that intrigue philosophers, economists, sociologists, decision theorists, and many other scholars,
being a rich interdisciplinary area for research. When trying to solve questions about moral uncertainty a
meta understanding of the concept of normativity can help us to develop strategies to deal with norms themselves.
2 nd-order normativity, or norms about norms, is a hierarchical way to think about how to combine many
different normative structures and preferences into a single coherent decision. That is what metanormativity
is all about, a way to answer: what should we do when we don't know what to do? In this study, we will review
a decision-making strategy dealing with moral uncertainty, Maximization of Expected Choice-Worthiness.
Given the similarity to this metanormative strategy to expected utility theory, we will also show that it is possible
to integrate both models to address decision-making problems in situations of empirical and moral uncertainty.