Operationalizing AI Ethics, No Longer An Option But An Imperative

Operationalizing AI Ethics has become imperative due to the challenges posed by machine learning models aiming for real-life mirroring and prediction. Despite reputational, regulatory, and legal risks, many companies still lack the ability to identify, evaluate, and mitigate ethical risks associated with their AI/ML products. Reid Blackman suggests that implementing systems identifying ethical risks throughout an organization is crucial. His seven steps to operationalizing ethical AI include leveraging existing infrastructure, creating tailored risk frameworks, optimizing guidance for product managers, building organizational awareness, incentivizing employee involvement in risk identification, and monitoring impacts while engaging stakeholders. An approach focusing on integrating the most appropriate ML infrastructure tools and processes is recommended by Blackman to make AI socially and ethically responsible.

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