Sources of bias
Algorithmic bias results in unfair outcomes due to skewed or limited input data, unfair algorithms, or exclusionary practices during AI development. As we know, bias in datasets can therefore result in biased output from the AI that was trained on those datasets. AVAILABILITY BIAS A tendency to place more weight on events or experiences that immediately come to mind or are readily available than those that are not. CONFIRMATION BIAS A tendency to place more weight on infor - mation that corroborates an existing belief than information that contradicts or casts doubt on that belief. GROUPTHINK A tendency to think or make decisions as a group that discourages creativity or individual responsibility.
OVERCONFIDENCE BIAS A tendency to overestimate one’s own ability to make accurate assessments of risk or other judgements or decisions. ANCHORING BIAS A tendency to use an initial piece of information as an anchor against which subsequent information is inadequately assessed. AUTOMATION BIAS A tendency to favour output generated from automated systems, even when human reasoning or contradictory information raises questions as to whether such output is reliable or fit for purpose. To tackle any cognitive – or indeed AI – bias, it is important to step back and question what you are seeing and the conclusions that you are reaching.
AI AND ETHICS | PART FOUR: SOURCES OF BIAS
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