Students’ Corner
Bias in Analytics
Mr. Himadri Jana Student - MBA 07 IIM Amritsar
We want to believe that our decisions are founded on facts rather than a gut or a guess. However, this is not always the case, and our prejudices might have an impact on our thinking. Allowing for some predispositions can have a negative impact on results, even in the most data-driven companies. For everybody who works with data, eliminating the bias should be a key priority. Bias is a natural propensity that we all have, but it must be suppressed to the greatest extent feasible in order to make better decisions. Bias in data analytics can be avoided by asking the correct questions, which allow respondents to react without being influenced by outside factors, and by continuously improving algorithms. Mentioning below some biases which exist in data analytics: -
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Aggregation Bias When a 'one size fits all' model is used to a population that is more diverse than the model allows for, this occurs. As a result, a single model variable may have multiple meanings for different groups within a population. It's crucial to strike a balance between your model's simplicity and performance.
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Evaluation Bias When the testing or benchmark data does not reflect the target population for which the model will be employed, this occurs. This suggests that your model works well for certain people in that population and not so well for others.
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Deployment Bias This occurs when there is a misalignment between the aim that the model was created to solve and how it is ultimately used. This could be in relation to the aim itself or the target population.
Representation Bias This happens when the data used to train your model and it does not accurately reflect the population for which it is be employed. This could be due to incorrect sampling procedures in your training dataset or an out-of-date training dataset. For e.g. In both underwriting and claims analytics, there may be a lack of representationof people with impairments in
Measurement Bias This happens when you're choosing the features and labels to include in the model. Bias occurs when the measurement technique is not uniform among groups within your target population. If certain groups generate more data to be measured than others, the same thing happens. Furthermore, your classification strategy may cause issues.
Confirmation Bias When researchers utilise the responses of respondents to corroborate their hypothesis, this is known as confirmation bias. Researchers will sometimes accept such evidence if it supports their own research and reject it if it contradicts it. Confirmation bias has the disadvantage of supporting only one point of view and outright rejecting others, therefore reducing the company's focus. Researchers must be willing to study and reconsider respondents' responses, as well as get away with preconceived conceptions and beliefs, in order to reduce confirmation bias.
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Interpretation Bias Elizabeth Loftus of the University of California conducted a research in which she showed participants a DVD of car accidents. She then divided them into two groups and asked them to sit in different rooms. "What do you believe the speed of
