_RESUMO:_ O presente artigo tem como objetivo indagar e talvez responder – com auxílio dos comentadores – dois pontos relevantes na lógica de Hegel. O primeiro, diz respeito à concepção de fundamentação de um sistema filosófico sem um começo: a ideia de um pressuposto incondicionado. O segundo, concerne à condiçõe das modalidades de necessidade e contingência numa lógica que se propõe absoluta. Como método absoluto, a ciência da lógica expõe o devir como condição do puro pensamento e Aufhebung [suprassunção] aparece (...) como um conceito central, pois é o motor que impulsiona a dialética. Isso pode ser observado tanto na Ciência da Lógica (2016) – com as determinações do pensamento – como na Fenomenologia do Espiríto (2018) – com as figuras da consciência –. Observa-se que há uma relação entre os conceitos de Aufhebung, necessidade e início absoluto. Por tanto, a partir dessa relação, objetiva-se expor a condição das modalidades de necessidade e contingência num sistema absolouto. (shrink)

This chapter explores the concepts of ethnic identity, ethnogenesis, ethnonationalism, nationalism and multiculturalism, linking them to a critical analysis of the selective revival of the past. In particular, it looks at the meanings of the concept of ethnonationalism, and at its historical origins, in terms of how it relates to the history of nation building and national culture building, which ethnonationalism represents. Drawing on comparative ethnographic analysis, the chapter examines two opposite processes; the ways in which new ethnic groups are (...) created through a selective reading of the past, and the way in which larger polities are imposed on very different ethnic groups. (shrink)

Developing and implementing artificial intelligence (AI) systems in an ethical manner faces several challenges specific to the kind of technology at hand, including ensuring that decision-making systems making use of machine learning are just, fair, and intelligible, and are aligned with our human values. Given that values vary across cultures, an additional ethical challenge is to ensure that these AI systems are not developed according to some unquestioned but questionable assumption of universal norms but are in fact compatible with the (...) societies in which they operate. This is particularly pertinent for AI research and implementation across Africa, a ground where AI systems are and will be used but also a place with a history of imposition of outside values. In this paper, we thus critically examine one proposal for ensuring that decision-making systems are just, fair, and intelligible—that we adopt a principle of explicability to generate specific recommendations—to assess whether the principle should be adopted in an African research context. We argue that a principle of explicability not only can contribute to responsible and thoughtful development of AI that is sensitive to African interests and values, but can also advance tackling some of the computational challenges in machine learning research. In this way, the motivation for ensuring that a machine learning-based system is just, fair, and intelligible is not only to meet ethical requirements, but also to make effective progress in the field itself. (shrink)

Artificial Intelligence (AI) is increasingly being used in disaster risk management applications to predict the effect of upcoming disasters, plan for mitigation strategies, and determine who needs how much aid after a disaster strikes. The media is filled with unintended ethical concerns of AI algorithms, such as image recognition algorithms not recognizing persons of color or racist algorithmic predictions of whether offenders will recidivate. We know such unintended ethical consequences must play a role in DRM as well, yet there is (...) surprisingly little research on exactly what the unintended consequences are and what we can do to mitigate them. The aim of this perspective is to call researchers working on fairness, accountability, and transparency to work with DRM and local experts—so we can ensure that disaster mitigation and relief is accountable, considers local values, and is not unintentionally biased. (shrink)

Developing and implementing artificial intelligence (AI) systems in an ethical manner faces several challenges specific to the kind of technology at hand, including ensuring that decision-making systems making use of machine learning are just, fair, and intelligible, and are aligned with our human values. Given that values vary across cultures, an additional ethical challenge is to ensure that these AI systems are not developed according to some unquestioned but questionable assumption of universal norms but are in fact compatible with the (...) societies in which they operate. This is particularly pertinent for AI research and implementation across Africa, a ground where AI systems are and will be used but also a place with a history of imposition of outside values. In this chapter, we thus critically examine one proposal for ensuring that decision-making systems are just, fair, and intelligible—that we adopt a principle of explicability to generate specific recommendations—to assess whether the principle should be adopted in an African research context. We argue that a principle of explicability not only can contribute to responsible and thoughtful development of AI that is sensitive to African interests and values, but can also advance tackling some of the computational challenges in machine learning research. In this way, the motivation for ensuring that a machine learning-based system is just, fair, and intelligible is not only to meet ethical requirements, but also to make effective progress in the field itself. (shrink)