Nicholas Kluge Corrêa

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, the proposed method allows for a new interpretation of the debate between David Lewis and Robert Stalnaker regarding the Limit and Singularity assumptions. Besides other results, we offer a new way to answer the problems raised by Goodman and Quine regarding vagueness, context-dependence, and the non- monotonicity of counterfactuals. Engaging in a dialogue with literature, this study will seek to bring new insights and tools to this debate. We hope our method of analysis can make counterfactuals more understandable in an intuitively plausible way, and a philosophically justifiable manner, aligned with the way we usually think about counterfactual propositions and our imaginative reasoning.

One of the strands of the Transhumanist movement, Singulitarianism, studies the possibility that high-level artificial intelligence may be created in the future, debating ways to ensure that the interaction between human society and advanced artificial intelligence can occur safely and beneficially. But how can we guarantee this safe interaction? Are there any indications that a Singularity may be on the horizon? In trying to answer these questions, We'll make a small introduction to the area of security research in artificial intelligence. We'll review some of the current paradigms in the development of autonomous intelligent systems and evidence that we can use to prospect the coming of a possible technological Singularity. Finally, we will present a reflection using the COVID-19 pandemic, something that showed that our biggest problem in managing existential risks is our lack of coordination skills as a global society.

For more than a century now, the automation of the means of work has created great apprehension among us. After all, will we all be replaced by machines in the future? Will all forms of labor be automatable? Such questions raise several criticisms in the literature concerned with machine ethics. However, in this study, I will approach this problem from another angle. After all, we can criticize the automation of the means of work in several ways. I invite the reader to entertain the following hypothesis: What if the automation of the means of labor is something beneficial? What if human emancipation does come through our technological development? If the answer is yes, why do we still work so much? I conclude that if automation processes are applied to key points in our social structure, we can emancipate the individual from a reality where we work for no reason.

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. 2nd-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. This strategy, proposed by William MacAskill, allows for the aggregation and inter-theoretical comparison of different normative structures, cardinal theories, and ordinal theories. In this study, we will exemplify the metanormative methods proposed by MacAskill, using has an example, a series of vegetarian dilemmas. 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. We believe that this kind of ethical-mathematical formalism can be useful to help develop strategies to better aggregate moral preferences and solve conflicts.

Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI.

What does Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a sub-genre of science fiction that explores the post-human relationships between human experience and technology. One similarity between AI Ethics and Cyberpunk literature is that both seek a dialogue in which the reader may inquire about the future and the ethical and social problems that our technological advance may bring upon society. In recent years, an increasing number of ethical matters involving AI have been pointed and debated, and several ethical principles and guides have been suggested as governance policies for the tech industry. However, would this be the role of AI Ethics? To serve as a soft and ambiguous version of the law? I would like to promote in this article a more Cyberpunk way of doing AI Ethics, whit a more anarchic way of governance. In this study, I will seek to expose some of the deficits of the underlying power structures of our society, and suggest that AI governance be subject to public opinion, so that ‘good AI’ can become ‘good AI for all’.

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance is not made prudently and critically-reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we really want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI.