Gabriela Arriagada Bruneau
One of the main discussions in the fields of data science and AI relates to how we can deal with bias and fairness, particularly due to big data’s capacity to reflect societal biases. Biased datasets pose a threat, given that when used to train algorithmic models, they replicate or escalate societal biases, thus making AI systems unfair and discriminatory against minority groups, particularly when used for decision-making processes. Most efforts to overcome unfairness have considered technical de-biasing fixes, merely hinting towards broader ethical implications but without further theorization on how human/ethical notions of bias and fairness influence their technical counterparts. My research aims to contribute to this debate by developing an ethical framework for data science, based on a conceptualisation of bias and fairness as distinct entities. Hence, I argue that the terminology for human bias, in its definition, should reflect the ethical neutrality of the phenomenon of bias, that is, bias is not unfair per se. Thus challenging the common association of technical and human bias with ethical unfairness in data contexts, and claiming that AI systems cannot be unfair or discriminatory in an ethical way.
Article: Arriagada Bruneau, Gabriela. (2018). Do we have moral obligations towards future people? Addressing the moral vagueness of future environmental scenarios. Veritas, (40), 49-65. https://dx.doi.org/10.4067/S0718-92732018000200049
Book Reviews: Susan Lufkin Krantz, Refuting Peter Singer’s Ethical Theory: The importance of Human Dignity, Praeger, 155 pp: Westport, in Aporia N° 7 (2014), p. 93-97.
Denis G. Arnold (ed.) 2009, Ethics and the Business of Biomedicine. Cambridge: Cambridge University Press, in Dilemata Nº 20 (2016), pp. 125-131.
Translation: David A. Crocker, Enfrentando la desigualdad y la corrupción: Agencia, empoderamiento y desarrollo democrático [Original Title: Confronting inequality and corruption: Agency, empowerment, and democratic development], Veritas Nº34 (2016), pp. 65-76.
Applied Ethics, Data ethics, AI ethics, Ethics and Technology.
- MSc in Philosophy - University of Edinburgh
- BA in Philosophy - Pontifical Catholic University of Chile