3 steps to better mitigate bias in your data analysis
Gartner had already predicted that by 2022, 85% of AI projects would produce skewed results due to bias. With the new year fast approaching and cases of technological bias becoming more prevalent and dangerous, it is essential that organizations think critically about how to identify and eliminate bias in the information generated. by AI and analytical systems.
Bias ideas can happen in a number of ways; sometimes it’s humans who are only looking at a subset of the data, maybe because they feel more comfortable with it. We have a natural tendency to seek answers that we understand, which can mean inadvertently looking at an incomplete or incorrect dataset. On top of that, there is simply too much data for only humans to analyze. According to an IDC report, the amount of data created over the next three years will be greater than the data created over the past 30 years, largely due to the vast digital world of work created during the pandemic.
Here are three steps every IT, business, and data science professional can take to mitigate bias in their organization’s data sets.
Step 1: Make sure all available data is included in analytical decision making
It has long been said that companies do not use all the data at their disposal. A joint study 2020 of the 1,500 global CEOs of IDC and Seagate Technology found that about two-thirds (68%) of the data available to businesses is unused.
The large amount and speed of the data created is not the only issue here. Platforms, like data warehouses, were created to help, but they still require the expertise of someone who knows how to clean, prepare, and transform the data for analysis (a lacking skill with the growing demand for data scientists). With a lack of technical expertise and resources, organizations are limited in the types and amount of data they can physically access and examine, which ends up skewing any data analysis they perform.
To overcome this challenge, technology leaders need to think about how to modernize their analytics stacks to include solutions that can automate data preparation, transformation, and analysis across the enterprise. By using all the data available in the analyzes, companies can be confident that their information is derived from the richest and most inclusive data sets.
See more : How AI-powered analytics can bridge the information gap
Step 2: Open data access to more people
While data and IT teams have long been considered the experts in this field, they should no longer be solely responsible for interpreting data to inform business decisions. Limiting access and analysis of data to a small subset of individuals within an organization can lead to bias; it is essential that a wide and diverse range of viewpoints be involved in the research and analysis of the data.
To increase data literacy, organizations need to identify tools that make it easy for non-technical users to explore data. Features like natural language search can make it easy for business users to ask questions about data stored by their business, and then receive automated visualized information that’s easier to understand. While data scientists should always be called upon for the most complex data problems, they can be confident that the rest of their team has the tools they need to interpret and analyze the data that will provide the most relevant information. and impartial.
See more : An approach to mitigate AI biases in transforming marketing operations
Step 3: Strengthen the Feedback Loop Between Humans and Machines
The automation and democratization of data are not the only key features that technology leaders should consider in new platforms. To truly mitigate bias, it is essential that humans and machines work together and are able to effectively draw the correct conclusions from the data.
Many platforms today lack explainability and transparency for humans to understand why a decision was made (and whether that decision was influenced by a biased data set). To overcome this, companies must focus on building a process and using tools that strengthen the feedback loop between humans and machines. In this loop, employees can inspect machine-generated information and easily validate it or provide guidance to move towards meaningful conclusions by including, excluding, or improving the way input data is interpreted. By ensuring humans participate in an iterative process of collecting and analyzing data, these more advanced platforms will eliminate any instances where the machine becomes malicious or makes decisions without validation.
Bias data analysis presents a range of dangers to business and society, and it is up to those responsible for interpreting the data to address them. By collecting and analyzing all available data; open up these data to more diverse perspectives; and by investing in transparent and explainable tools, companies can ensure they are leveraging the most accurate, representative, and therefore most valuable information to inform their business decisions.
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