AI is an opportunity to do better, to deprogram our society of bias, and directly encode the ethics and values we’d like to see reflected in AI-driven processes. The path there requires a nuanced understanding of algorithmic bias, how it is produced, and how it can be mitigated, and also a more comprehensive framework for accountability and governance of AI systems in general, encompassing all risks including bias.
Every use case is unique, and context is pivotal to extrapolating how an AI will interact with a process and impact different groups of people. It is never just math.
Download this white paper by DataRobot and DataCamp to learn more about:
- Algorithmic bias, how to define and identify bias in AI, what are the sources of bias, and how to mitigate it
- Accountability and governance frameworks, to develop a comprehensive understanding of the risks in an AI use case, and to put into action guardrails to monitor and reduce those risks
- The cultivation of data literacy, to make available to stakeholders of all technical levels a shared understanding of an AI and its data, for more informed decision-making
Trust is not an option, it is a requirement. Building AI systems without trust tenets invites disaster, for your organization, your own personal brand and for stakeholders impacted by the AI system.
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