AI Ethics, Risk Mitigation & Governance Program

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As simple as it seems, ethics are difficult to be translated into computer code. The reason being is the complexity, subjectivity, and diversity of ethical standards.  AI models require rigid, predictable, and binary logic; while human ethics are context-dependent and often contradictory. Like an iceberg, some risks may be emergent in AI ethics, while other things, such as edge cases, may be more nuanced and less apparent.

It is said that human ethics are as critical as machine ethics, therefore when building ethical systems, organizations must apply a holistic and intersectional lens. Further, we must ensure that automated systems are not mirroring flawed human processes, where discrimination and system failures are likely to contribute to unfair outcomes.

Traditionally, fairness is modeled after the Pareto efficiency, where resources are allocated in such a way that it is impossible to make any one individual better off without making another person worse off. Viewing resources and opportunities from a limited perspective is likened to cutting pie in accord to a zero sum scenario. If the system is designed using this model, then accuracy, efficiency, and trust are likely to be compromised.

Human ethics are the first and most important step in achieving machine ethics, as well as in safeguarding the rights and privacy of stakeholders. The HLTWai™ AI Ethics, Risk Mitigation, and Governance course takes a different approach to understanding these business challenges and designing solutions. Adopt a framework that is more effective than other generic programs, and use our checklists to account for context and culture, while adjusting for speed and innovation.

Take your career to the next level, join the next HLTWai™ cohort for AI Ethics, Risk Mitigation, and Governance.