Book Reviews by Gabriela Arriagada Bruneau
Journal of Ethics and Emerging Technologies31, (2), 2021
This book takes us to explore the 'life of robots' and presents us with a refreshing narrative th... more This book takes us to explore the 'life of robots' and presents us with a refreshing narrative that demystifies their recurrent anthropomorphic understanding. General ideas of what robots are and what they can do often lack knowledge about the limitations, functionality, and complexity needed to turn a robot into a fully operational machine. In this book, the authors portray a grounded and accessible description of current research developing robots. They are insightful, yet still allow the reader to understand basic processes and requirements of robotics. They deliver on their goal to show robots as human artifacts instead of placeholders for anxiety, i.e., a threat. However, they underdeliver regarding the ethical insights of robots as moral agents in the final chapter. Withal, this is an essential introductory reading for anyone interested in learning about the functioning of robotics and AI, and their integration to society.
En los últimos 20 años el sistema de salud a nivel mundial ha cambiado radicalmente.
Drafts by Gabriela Arriagada Bruneau
In the following analysis I attempt to draw Peter Singer's ethical theory in order to identify an... more In the following analysis I attempt to draw Peter Singer's ethical theory in order to identify and define the role the concept of person plays inside this theory and be able to grasp the consequences this concept originates. To achieve this goal I will, first explain and clarify all the elements that constitute Singer's ethical theory, including values, principles and concepts. After this, I will show how the concept of person is a crucial element of this theory, in particular, referring to the practical side of this theory. Finally, I will present some of the consequences that arise from this system directly influenced by the concept of person, which I conclude produce an unexpected and counterproductive effect to Singer's theory.
Translations by Gabriela Arriagada Bruneau
Resumen: En este trabajo identifico cuatro temas que, considero, deberían ser más prominentes en ... more Resumen: En este trabajo identifico cuatro temas que, considero, deberían ser más prominentes en el enfoque de las capacidades para el desarrollo internacional y la reflexión ética respecto de los fines, medios y procesos del desarrollo: (1) desigualdad de poder, (2) agencia y empoderamiento, (3) democracia y desarrollo y (4) corrupción. Sostengo que el primero y el cuarto son desafíos de urgencia para la campaña del enfoque de las capacidades, y que el segundo y tercer temas son importantes maneras en las cuales los desafíos pueden y deberían ser en-frentados.
Traducción de Gabriela Arriagada Bruneau.
Papers by Gabriela Arriagada Bruneau
In this review, we present some ethical imperatives observed in this pandemic from a data ethics ... more In this review, we present some ethical imperatives observed in this pandemic from a data ethics perspective. Our exposition connects recurrent ethical problems in the discipline, such as, privacy, surveillance, transparency, accountability, and trust, to broader societal concerns about equality, discrimination, and justice. We acknowledge data ethic’s role as significant to develop technologi- cal, inclusive, and pluralist societies.
SSM Annual Scientific Meeting, 2021
During the COVID-19 pandemic we have seen various disastrous approaches regarding the use an impl... more During the COVID-19 pandemic we have seen various disastrous approaches regarding the use an implementation of measures and studies that performed on past and current health data. Accordingly, in this study, we criticize the lack of conceptual engineering to integrate ethical principles and values into the design and application of data-driven endeavours, with a particular examination at health data. We argue how we cannot strive for a robust ethical assessment without a critically causal framework
Firstly, we analyse the translational gap and conceptual conflation of the terms: ‘bias and fairness’ and ‘transparency and explainability’, highlighting the misleading definitions and uses given to these concepts at a technical and ethical level. The main distinctions presented clarify the moral expectations given to these concepts and criticise the insufficient development of a conceptual analysis that targets them. We suggest that a fundamental part of a solution to reduce this translational gap implies embracing and applying a causal framework. Thus, we show why using causal models and, most importantly, a causal narrative cannot only help to prevent unethical effects, but it can also influence the efficiency of prediction models and their outcomes. Efficiency, in this case, transforms into an ethically laden concept that demands a causal narrative to align with ethical principles. Finally, we go through examples of COVID-19 decision-making that could have benefitted from a causal approach, highlighting the negative consequences of the NHS electronic health records platform and an OpenSAFELY publication in Nature that substantially suffers from the Table 2 Fallacy
This analysis puts into discussion an interdisciplinary approach to increase critical ethical awareness about fairness. Providing robust and reliable frameworks to analyse and present data, especially in sensitive times like a world pandemic, requires trustworthy practices.
Integrating ethics into data-driven solutions cannot be limited by the bias-aware fairness formalisations or the naive applications of transparency and explainability. When it comes to the real-world application of models, their effects can harm individuals in society. Non-causal approaches tend to dissipate elements of agency and responsibility, which are fundamental to the development of what we can call ‘good science’.
Veritas, 2020
In this review, we present some ethical imperatives observed in this pandemic from a data ethics ... more In this review, we present some ethical imperatives observed in this pandemic from a data ethics perspective. Our exposition connects recurrent ethical problems in the discipline, such as, privacy, surveillance, transparency, accountability, and trust, to broader societal concerns about equality, discrimination, and justice. We acknowledge data ethic's role as significant to develop technological , inclusive, and pluralist societies.
In this paper, I will be primarily concerned with moral issues regarding future people and the en... more In this paper, I will be primarily concerned with moral issues regarding future people and the environment. When it comes to the future, we have deontological and epistemic limitations. The closer to the present, the higher the certainty and the knowledge we have about facts. Thus, when we intend to find moral clarity regarding a future scenario, we deal with an inverse relation between certainty and time (the further to the future, uncertainty gets higher). The main problem is that most ways of dealing with moral issues about future scenarios do not address this relation, and rather focus on things that seem to simplify and clarify the uncertainties of the future. In response to this, I propose a different approach, one that operates neutrally and timelessly dealing with the uncertainties of the future while providing moral groundings that can help to clarify the future's state of moral vagueness. Resumen En este artículo me enfoco primordialmente en problemas morales asociados a las personas futuras y al medio ambiente. Cuando se trata del futuro, tenemos limitaciones tanto deontológicas como epistemológicas. A mayor cercanía con el presente, mayor es la certeza y el conocimiento que tenemos. Por lo tanto, cuando intentamos encontrar claridad moral respecto de escenarios futuros, nos vemos forzados a lidiar con una relación inversa entre certeza y tiempo (a mayor distancia en el futuro, mayor falta de certeza). El gran problema con esto es que la mayoría de las perspectivas para afrontar algunos problemas morales del futuro no reconocen esta relación y, más bien, se enfocan en cosas que simplifiquen y clarifiquen las incertidumbres del futuro. En respuesta a esto, propongo una aproximación diferente, que opera desde la neutralidad y la atemporalidad, lidiando con las incerti-dumbres del futuro y, al mismo tiempo, proveyendo fundamentos morales que ayuden a clarificar el estado moral de vaguedad del futuro. Palabras clave: Personas futuras, ética ambiental, obligaciones morales, problema de la identidad personal, vaguedad moral. MSc in Philosophy, The University of Edinburgh. This article is based on my MSc dissertation funded by CONICYT as part of their programme BECAS CHILE, folio 73170156.
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Book Reviews by Gabriela Arriagada Bruneau
Drafts by Gabriela Arriagada Bruneau
Translations by Gabriela Arriagada Bruneau
Traducción de Gabriela Arriagada Bruneau.
Papers by Gabriela Arriagada Bruneau
Firstly, we analyse the translational gap and conceptual conflation of the terms: ‘bias and fairness’ and ‘transparency and explainability’, highlighting the misleading definitions and uses given to these concepts at a technical and ethical level. The main distinctions presented clarify the moral expectations given to these concepts and criticise the insufficient development of a conceptual analysis that targets them. We suggest that a fundamental part of a solution to reduce this translational gap implies embracing and applying a causal framework. Thus, we show why using causal models and, most importantly, a causal narrative cannot only help to prevent unethical effects, but it can also influence the efficiency of prediction models and their outcomes. Efficiency, in this case, transforms into an ethically laden concept that demands a causal narrative to align with ethical principles. Finally, we go through examples of COVID-19 decision-making that could have benefitted from a causal approach, highlighting the negative consequences of the NHS electronic health records platform and an OpenSAFELY publication in Nature that substantially suffers from the Table 2 Fallacy
This analysis puts into discussion an interdisciplinary approach to increase critical ethical awareness about fairness. Providing robust and reliable frameworks to analyse and present data, especially in sensitive times like a world pandemic, requires trustworthy practices.
Integrating ethics into data-driven solutions cannot be limited by the bias-aware fairness formalisations or the naive applications of transparency and explainability. When it comes to the real-world application of models, their effects can harm individuals in society. Non-causal approaches tend to dissipate elements of agency and responsibility, which are fundamental to the development of what we can call ‘good science’.
Traducción de Gabriela Arriagada Bruneau.
Firstly, we analyse the translational gap and conceptual conflation of the terms: ‘bias and fairness’ and ‘transparency and explainability’, highlighting the misleading definitions and uses given to these concepts at a technical and ethical level. The main distinctions presented clarify the moral expectations given to these concepts and criticise the insufficient development of a conceptual analysis that targets them. We suggest that a fundamental part of a solution to reduce this translational gap implies embracing and applying a causal framework. Thus, we show why using causal models and, most importantly, a causal narrative cannot only help to prevent unethical effects, but it can also influence the efficiency of prediction models and their outcomes. Efficiency, in this case, transforms into an ethically laden concept that demands a causal narrative to align with ethical principles. Finally, we go through examples of COVID-19 decision-making that could have benefitted from a causal approach, highlighting the negative consequences of the NHS electronic health records platform and an OpenSAFELY publication in Nature that substantially suffers from the Table 2 Fallacy
This analysis puts into discussion an interdisciplinary approach to increase critical ethical awareness about fairness. Providing robust and reliable frameworks to analyse and present data, especially in sensitive times like a world pandemic, requires trustworthy practices.
Integrating ethics into data-driven solutions cannot be limited by the bias-aware fairness formalisations or the naive applications of transparency and explainability. When it comes to the real-world application of models, their effects can harm individuals in society. Non-causal approaches tend to dissipate elements of agency and responsibility, which are fundamental to the development of what we can call ‘good science’.