Thus, total blinding is not possible, and there is the possibility that the surgeon's knowledge of which treatment is being given might influence the outcome. Sometimes the researchers can partially get around this by using only surgeons who genuinely believe that the technique they are using is the better of the two. But this then introduces a confounding of technique and surgeon: it might be, for example, that the surgeons preferring one technique are more skilled or more experienced or more careful than the surgeons preferring the other, or have different training that affects the outcome regardless of the surgical method.
Overfitting en underfitting Underfitting means when a model gives an oversimplistic picture of reality. Overfitting is the opposite: i. Overfitting risks causing a certain assumption to be treated as the truth whereas in practice it is actually not the case.
Always ask the data analyst what he or she has done to validate the model. If the analyst looks at you with a rather glazed expression, there is a good chance that the outcomes of the analysis have not been validated and therefore might not apply to the whole database of customers. Always ask the data analyst whether they have done a training or test sample. If the answer is no, it is highly likely that the outcomes of the analysis will not be applicable for all customers.
Confounding variabelen If the research results show that when more ice creams are sold more people drown, ask whether they have checked for what are known as confounding variables. In this case, the confounding variable will be the temperature.
If the weather is hotter, people will eat more ice cream and more people will go swimming. This is likely to result in more drownings than on a cold day. A confounding variable is therefore a variable that is outside the scope of the existing analytical model but that does influence both the explanatory variable in this case, ice cream sales and the dependent variable the number of drownings.
Failing to allow for confounding variables can result in assuming there is a cause-effect relationship between two variables when there is in fact another variable behind the phenomenon. Bear in mind that a correlation is not the same thing as cause-effect. If a relationship between attributes is identified, this can be very helpful when you want to select the right customers for a particular campaign.
It is crucial to unequivocally confirm that the conclusion from the outcomes of research and analysis is not influenced by bias. This is not solely the responsibility of the analyst in question. It is the shared responsibility of everyone directly involved including the marketeer and the analyst to reach a valid verdict on the basis of the correct data.
In a world of marketing where data and analysis are playing an increasingly large part, you need to be able to rely on the correct facts. There are many biases that can negatively impact the data, including:. Ethical matters regarding the collection of data are increasingly being raised by the public, especially as it concerns consumer privacy. The impact of biased data on applications such as artificial intelligence is not always theoretical, or even subtle.
Tay was a chatbot released by Microsoft in that used AI technology to create and post to Twitter. Soon after going live, Tay began tweeting concerning content , much of it discriminatory in nature. After deactivating Tay, the Microsoft team released a statement about the incident. Tay used those threads as a means of data mining to influence its output. Although this incident was at least partially caused by intentional sabotage from users, it illustrates how discrimination can take form in the data that is increasingly being put to work in our day-to-day lives.
Because data-driven technology is now so omnipresent, biased data can have a wide range of consequences, including complex social repercussions. If we are constantly feeding prejudices back into our cultural consciousness through the vehicle of data-driven technology, these prejudices may be subconsciously reinforced, creating a loop we can only break with concerted effort.
The advantage that humans have over machine learning is that humans, at least in groups, have the capacity for cultural evolution, providing some level of checks and balances against prejudice. How human bias influences data Algorithms built to mimic the process of learning and conclusion-making do so by processing data gathered from human users.
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