Process mitigates bias
Biases exist because they speed up our thinking, but in many cases, that speed hurts us in the long term. As Kahneman characterized it, “The brain is a machine for jumping to conclusions.”
There is a lot of scope creep, wasted effort, and harm in that jump. A well constructed IA process addresses biases by making our .
Overconfidence bias occurs when we are surer of our abilities or plans than is objectively reasonable. We mitigate it forcing realism about details. Creating a comprehensive list of every attribute and element your experience needs early in the process makes it harder to assume it will be easy.
Expedience bias occurs when we make a decision based on what comes to mind fastest, rather than being deliberate and gathering objective information. We mitigate it by investigating the assumptions underlying complicated plans. Making things more complicated than they are can be a way to avoid the underlying complexity of simple ideas.
Experience bias occurs when we believe that everyone thinks the way we do and anyone who disagrees with us is wrong. We mitigate it by assembling realistically-sampled evidence to validate our assumption. Gathering alternative labels for a content type from many different people, including research transcripts and highlighting inconsistencies in definitions makes it harder to assume everyone will understand what we understand.
occurs when we favor or choose people whom we identify as similar to us. We mitigate it by separating the contribution of ideas from judging them. Getting ideas on the board without attribution and testing how well it will work makes it harder to track who provided what.
Attribution bias occurs when we perceive or judge the actions of others more harshly than we would judge ourselves. We mitigate it using standards and precedent. Applying the same set of heuristics to each feature helps everybody be realistic about the outcomes, if not the reasons for those outcomes.