Cyber & IT Supervisory Forum - Additional Resources

Apply pre-processing data transformations to address factors related to demographic balance and data representativeness. Apply in-processing to balance model performance quality with bias considerations. Apply post-processing mathematical/computational techniques to model results in close collaboration with impact assessors, socio technical experts, and other AI actors with expertise in the context of use. Apply model selection approaches with transparent and deliberate consideration of bias management and other trustworthy characteristics. Collect and share information about differences in outcomes for the identified groups. Consider mediations to mitigate differences, especially those that can be traced to past patterns of unfair or biased human decision-making. Utilize human-centered design practices to generate deeper focus on societal impacts and counter human-cognitive biases within the AI lifecycle. Evaluate practices along the lifecycle to identify potential sources of human-cognitive bias such as availability, observational, and confirmation bias, and to make implicit decision-making processes more explicit and open to investigation. Work with human factors experts to evaluate biases in the presentation of system output to end users, operators and practitioners. Utilize processes to enhance contextual awareness, such as diverse internal staff and stakeholder engagement.

Organizations can document the following: Transparency & Documentation

To what extent are the established procedures effective in mitigating bias, inequity, and other concerns resulting from the system? If it relates to people, does it unfairly advantage or disadvantage a particular social group? In what ways? How was this mitigated?

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