Abstract

Pushing towards the almost universal adoption of Artificial Intelligence (AI) across the globe, the Philippines is not far behind. This tsunami has huge promise, but at the same time, under the present legal footing, it is likely to raise critical issues of ethics that have yet to be resolved. Against this background, the present paper reviews related literature on this emerging issue of AI bias, explainability, and algorithmic accountability. It comes down mainly to work done regarding bias in AI relative to the domain of recruitment and facial recognition technologies, in this case how it leads to discrimination. This asks to discuss the “black box problem” applied to nontransparent AI systems for which there is a need for the outcome to be explainable. It identifies the Data Privacy Act (DPA) of 2012 as the nearest framework that may be the firm foundation in the assurance of the right to understand AI decision-making. The other issue the article is concerned with is algorithmic accountability. Currently, guiding laws exist in the country, but these are narrow in scope and may not necessarily capture the many faces of AI behavior. In other words, the paper reviews the European Union’s General Data Protection Regulation (GDPR) as a model that can possibly find a solution for the biases. To summarize, this country needs a legal framework to overcome the challenges that have been brought about and reach an agreement on AI explainability enhancement, a clear definition of who is responsible and liable for what, and bias mitigation. The identified gaps in previous studies will form the basis for making recommendations on further research into AI bias within Philippine enterprises. All this underlines ever-necessary comparative research on the other rules concerning AI that has been put in place elsewhere. Still more importantly, it complements reasons for exporting such an idea to which the Philippines should develop an all-encompassing legal framework in demeanor to the rise of responsible and ethical research, development, and deployment of AI.

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