In this essay, I argue that the institution of journalism plays a vital role in informing citizens of a deliberative democratic society, and that to effectively fulfill this role, journalists must report the news objectively. I first examine the historical evolution of objectivity as it pertains to journalism. Then, I elaborate on some of the philosophical concepts that provide the foundation for objectivity. Next, I introduce John Rawls’ idea of public reason, which provides an improved understanding of the role of (...) journalism within a democratic society. I claim from this that journalism must re-envision its role as guardian of the public political forum. Finally, I bring these various discussions together by drawing in the requirements that Stephen Ward lays out in his theory of pragmatic objectivity, and argue that these are necessary to help journalism legitimize its authority to safeguard this forum. In doing so, journalism can ensure both that citizens are objectively informed and that the public forum offers them a sphere in which they can effectively participate in the governance of their democracy. (shrink)

(2013). Freedom of Expression v. Social Responsibility: Holocaust Denial in Canada. Journal of Mass Media Ethics: Vol. 28, No. 1, pp. 42-56. doi: 10.1080/08900523.2012.746119.

AI-driven journalism refers to various methods and tools for gathering, verifying, producing, and distributing news information. Their potential is to extend human capabilities and create new forms of augmented journalism. Although scholars agreed on the necessity to embed journalistic values in these systems to make AI systems accountable, less attention was paid to data quality, while the results’ accuracy and efficiency depend on high-quality data in any machine learning task. Assessing data quality in the context of AI-driven journalism requires a (...) broader and interdisciplinary approach, relying on the challenges of data quality in machine learning and the ethical challenges of using machine learning in journalism. To better identify these, we propose a data quality assessment framework to support the collection and pre-processing stages in machine learning. It relies on three of the core principles of ethical journalism—accuracy, fairness, and transparency—and participates in the shift from model-centric to data-centric AI, by focusing on data quality to reduce reliance on large datasets with errors, making data labelling consistent, and better integrating journalistic knowledge. (shrink)