Volume 1, No. 1

Nathanael V. Navarro (Author)

WHY INFORMED CONSENT FORMS ALONE CANNOT PROTECT US FROM FALSE INFORMATION

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The advancement of communication technology has allowed us to have quick access to relevant information as much as we are also frequently bombarded with false information. Many theorists argue that this was the result of the optimization of greed and profit, overtaking how we create trust between and among the agents of social media. In particular, this study will show that the frequency of encountering disinformation and misinformation has so much to do with the algorithmic operations behind social media, as these are the main instruments that social media companies use to highly personalize content through their stimulating recommendations of options that users are to take for themselves. Crucial to this issue is the fact that algorithms used by social media are merely serving as cold conduits of data based on "highly engaging content" to optimize the watch time of users, which, in turn, becomes a profiling tool to place targeted advertisements. If we treat this whole scheme as internet research for the operation and development of social media platforms, I argue that this should be subject to ethical considerations. Coming from the purview of research ethics, I will explain why informed consent forms with various terms and conditions of digital platforms are not enough to effectively guard the trust that we value in human connection. This paper will elucidate, using Onora O'Neill and Neil Mason's theories, why trust in human interaction is so complex, which will entail rethinking how we appreciate the value of informed consent. Rather than siding with the misconception that informed consent forms must include all information that social media users, as research participants, might encounter in the use of social media to merely create a cover for legal liabilities and a ready-made response for any negative feedback and critical remarks from various participants and stakeholders of the research, I argue that we must pay attention to the inherent complexity of human communication. Trust can only be secured in any type of human communication when we acknowledge that it is open to ambiguity, inferentially rich, and always presumes shared background conditions of epistemic, practical, and ethical commitments. As a propositional act, consenting is always limited in its knowledge but is never abusive. Despite the mistrustful energies and the uncertainties that the operations of algorithms entail, this essay attempts to give hope for us to autonomously redirect our lives towards trust-building in social media. 

Rev. Fr. Cyriacus Emedolu, Remi Okeke (Author)

THE PHILOSOPHICAL FOUNDATION OF FAIRNESS IN MACHINE LEARNING: AN AFRICAN PERSPECTIVE

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The increasing integration of machine learning (ML) systems into critical decision-making processes necessitates a distinct exploration of fairness, especially within diverse sociocultural contexts. This paper examines the philosophical foundation of fairness in Machine Learning through an African lens, emphasizing the unique ethical, historical, and cultural dimensions that shape its interpretation and application across the continent. Unlike the Euro-Western paradigms that often prioritize individual rights or utilitarian principles, African philosophy, particularly the concept of Ubuntu, emphasizes communal well-being, interconnectedness and restorative justice. These principles provide a distinctive framework for addressing systemic biases and inequities embedded in Machine Learning systems. The paper also explores the broader philosophical underpinnings of fairness in African ethics, including the prioritization of holistic approaches and moral economy. These perspectives advocate for Machine Learning systems that consider the collective good, minimize harm and promote equity in access and outcomes. Furthermore, the African emphasis on pluralism and diversity underscores the need for adaptable fairness frameworks that respect local contexts while contributing to global Machine Learning ethics discourse. An African perspective on fairness therefore offers valuable insights for the global AI community.