Material Issues in the Blossoming World of AI
Today, AI is becoming ever more present in supply chains, administration, customer service and daily activities of almost every sector. While sectors such as the financial, health and marketing sectors have been known to develop and use these new technologies, sectors such as retail are now investing billions into what is already a rapidly evolving technology. Although AI still seems a far-away and abstract concept to most people, developments and visibility of AI into a rapidly expanding commercial market have brought about legal and ethical questions…Learn More
A New Perspective on AI Impacts in the Healthcare Sector
The prevalence of Artificial Intelligence (AI) and related technologies can be seen in business and society, as well as healthcare. The complexity and abundance of data in the healthcare sector offers space for AI to increasingly be used in the field by healthcare providers and carers. With the increased use of AI comes the increased importance of analyzing and assessing the governance aspects and societal, ethical, legal and health…Learn More
As Artificial Intelligence (AI) becomes increasingly prevalent in the health sector, the need to assess and report the impact of AI on the sector and its stakeholders (i.e. patients, clinicians, non-professional caregivers, etc.) becomes ever more important. The first of its kind, YAGHMA’s categorization tool, AI Taxonomy, paves the way for healthcare professionals, policy makers and other healthcare stakeholders to make informed decisions on AI’s societal impacts…Learn More
Enabling Trustworthy AI Model Lifecycle
New solutions for assessing and monitoring the impacts of Artificial Intelligence (AI) systems should be adopted to ensure and facilitate positive impact in a complex and evolving legal and regulatory environment. The AI model lifecycle assessment is a technical evaluation that helps identify and address potential risks and unintended consequences of AI systems across businesses, to engender trust and build supportive systems around AI decision making. To enable a trustworthy AI model lifecycle, a series of qualitative and quantitative checks…Learn More
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