Biases in Big Data and Solutions

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Artificial intelligence (AI) and data analytics play an essential role in our lives. Be it in times of humanitarian crises, healthcare, or financial and banking services – data always finds how decisions are made. But sometimes, information collected may get biased, thus impacting enterprise productivity.

This year is gonna be the year of big data because everyone around is just talking about the hero ingredient “Big Data.” From extensively trendy articles in major newspapers to big data conferences, the science world and businesses are discovering how large datasets can give insights on previously intractable challenges.

Let’s take a hasty recap for those who always feel lost when data language gets tossed at them…

Big Data – a world full of data. I mean lots and lots and lots…of data. Qualitative and quantitative indicators are used to identify relationships, patterns, and trends.

When I first encountered the idea of biases in big data, it blew my mind and made me amazed at how I hadn’t heard this before. Take a look.

Biases in big data, accidental or intentional, may be advantageous to inaccurate judgments and inferior business results. These days, businesses are aware that big data is a crucial part of their decision-making process.

As we all know, the human judgment might be flawed sometimes due to the availability of massive data. Thus, it gets tricky for data analysts and scientists to analyze massive data. Several other factors may have a negative or positive impact on data generated. Therefore, data teams need to sort big data. It is only possible when data scientists and analysts are aware of the existential biases and the solutions to them.

“Social media data is one small slice of all the data that is out there,” said Kate Crawford, Principal Researcher at Microsoft Research New York.

To understand more about biases, let’s focus on an outstanding example given by Alexandra Olteanu, a Post-Doctoral Researcher at Microsoft. As per the New York state’s new law, insurance companies can use social media to decide the level for your premiums. But due to the lack of data, they end up using incomplete information.

For instance, when you buy vegetables from local farmers or supermarket, they don’t track you online. So no one knows about it. Vice versa, when you purchase products from a bakery, they might post images regarding your purchase. Based on this data, the insurance company might think you only eat cookies all the time. Thus, it shows that incomplete information can affect you.

Detailing of biases in big data and solutions

For all those who are always excited to work with big data and help enterprises to solve the biggest problems, inefficiencies, or questions should be familiarized with the term by now. Take a look at how data bias arises due to the fundamental characteristics of the systems that generate the data.

Six most common types of biases in big data and solutions:

1. Societal bias: mostly occurs in content produced by humans, whether it be news articles, or social media content.

2. Confirmation bias: awareness is everything and has a significant impact during the analysis of big data. Thus, it is termed confirmation bias that can skew data. It does not occur due to the lack of information. But it is the phenomenon where data is aligned with views, beliefs, and opinions of data analysts or scientists. These data biases are majorly seen amongst organizational leaders who always prefer information and evidence tuned to their perceptions. Thus, confirmation bias may lead to bad business outcomes; therefore, one should always look out for disconfirming evidence.

3. Underfitting and overfitting: a common misunderstanding among data scientists is that complicated data trends are expected to get specific inferences. However, minor fluctuations and unnecessary noise is detected when a considerable number of parameters are evaluated and added to the data model. Due to this, the significant trend might get ignored, thus leading to flawed predictive analysis.

It is totally opposite in the case of underfitting, where data analysts try to fit nonlinear data into a linear data model. Thus, these two methods may lead to biases and end up skewing outcomes.

4. Availability heuristic: it is also termed as availability bias that often occurs in big data. We should be more careful with this because its manifestation is subtle. It referred to how data scientists/analysts make inferences based on available recent information. They think that instant data is appropriate data. This is true in the case of news, as it has a massive discrepancy between what is covered and what actually happened. Thus, it may have a terrifying impact on big data and solutions. Just relying on recent data, available heuristics may lead to a narrow approach to data analytics.

5. Non-normality: the bias for non-normality is calculated through something called the bell curve or the t-test. The peak point on the bell curve denotes a series of data representing events of the highest probability. Sometimes it happens that data scientists try to fit data near the bell curve. Further, this leads to imprecise results that can harm an enterprise’s output.

6. Simpson’s paradox: you might be thinking about the American animated series “Simpsons,” but here we don’t mean the cartoon, Simpson. Here we are talking about a data bias called Simpson’s paradox. Just taking a look at data may seem perfectly fine, but an attentive data analyst/scientist should know how to read between the lines during a rise in data traffic. This is more important in the marketing and healthcare sector, as the audience in these two sectors is very sensitive.

Britannica defined Simpson’s paradox as: “the effect that occurs when the marginal association between two categorical variables is qualitatively different from the partial association between the same two variables after controlling for one or more other variables.

“But above all, social-science approaches help us to ask productive questions about data to prevent us from falling victim to our own cognitive biases that often suggest answers we expect or lead us to results we wish to find,” commented Kate Crawford, Principal Researcher at Microsoft Research New York.

Assumption

It is not only the responsibility of data analysts/scientists; it is a shared responsibility of everyone, including analysts and marketers directly involved in getting results based on correct data. In this world full of data, one should be keen to understand and rely on accurate facts to boost the productivity of an enterprise. Earlier, someone said that “the fact is not a fact until it’s proven.” Also, we have heard several times, “there are three categories of lies: statistics, lies, and outright lies.”

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