Solving Data Bias With Junction of Data and Intellectual Diversity
In this article, see how to solve bias with junction of data and intellectual diversity.
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Big Data, an accelerating trend, is penetrating in various industries. To automate the systems and eliminate the need for human power, the need is for diverse data that comprehensively covers all possible aspects of human behavior and actions. The world is getting digitized faster than the normal pace. There are several challenges in the way. For instance, the digital world demands robust processing and security. These attributes vary from niche to niche. The challenge is to maintain a balance between both of them and smoothly perform digital processing.
To automate the online systems, advanced technologies and algorithms are applied. For example, the automated chatbots integrated with the online systems tend to communicate with the customers without any assistance from the human query handler. In these chatbots, abundant data is used to train them against online queries. The data includes frequently asked questions and some queries other than normal ones. A diverse dictionary is embedded in the models to train as well as test them.
Artificial intelligence and machine learning models are hungry for data. Among AI approaches, a new breed called ‘lifelong learning machines’ is being designed to process data indefinitely and continually. A stream of data builds itched models that are longed and desired ones. But, the increasing importance and demand for data are introducing hurdles in the form of ‘data bias’. AI companies around the world are facing difficulties in addressing the issue of data bias actively.
Outrageous Mistakes by AI Models
‘The technology world needs improvements’. This stance came on the scene as a result of multiple failures in the AI models. For instance, Google photos at times fail to label the entity correctly. The applications labeled African Americans as ‘gorillas’. There are some more; the facial recognition system of Amazon marked the congress members criminals. This verbosity leads to ruinous circumstances when it enters the practical world. This is the reason that the founder of Microsoft Corporation, Bill Gates discouraged the use of AI-models and such technologies for surveillance purposes for example in war.
Data bias is not a part of AI-models, but the data contains bias elements. For example, the algorithms used for data processing, filtration, labeling, and analysis are inefficient to categorize a particular trait proportionately. All the issues correspond commonly to the data bias in which the models fail to frictionlessly classify the case in a relevant category. Besides, legal repercussions impose hefty penalties on AI companies as an outcome of in practice model failure.
Diverse Dataset: A Solution
The AI data passes through various stages. The issue of data bias can actively be addressed during the curation phase. The reason is that the data that is collected sometimes do not contain all the possibilities or assorted element. The data sources in this matter hold a key role. For example, the data collected from some source holds more data regarding how men look.
The attributes about men are clearly defined and present in data whereas it does not contain any detail regarding the women’s features. Now while classification, the data related to men is more and less in the women category. This dataset will be trained efficiently in recognizing men and will give negative results in the case of women identification. This leads to data bias. So the first thing is an even and diverse data collection.
An Ethnographic Perspective
At the stage of data collection, a diversified survey and population analysis should be done. This method in the specific terms is called ethnography. Just the way, in technical research methodology, ethnography corresponds to a diverse social analysis to propose a solution. It should be considered while collecting data for AI models that are gathered keeping in mind an abstract viewpoint and sources. For example, an AI-based facial recognition system fed up with men’s facial features more than the women’s will smoothly identify the characteristics of men as compared to women.
Today, the facial recognition systems are built taking into consideration a spacious data view. They cover massive perspectives with respect to faces belonging to different cultures and countries. This is the reason that major improvements in facial recognition technology make it adopted worldwide at an industrial level.
Another type of diversity. When it comes to being creative in problem-solving and being productive, an intellectual group is required. The group includes political aspects, academic discipline, and risk tolerance programs. Intellectual diversity enhances both the productivity and growth of a model. Moreover, it increases the likelihood of feature identification in the correct category and ultimately reduces data bias. When an intellectual team contributes to developing AI models, broader views are covered efficiently to which standard entities could not show any significant results.
Whatsoever, the problem of data bias is not yet solved entirely. There exist some loopholes in AI models that are exceptions to which they have never come face to face. Data scientists around the world are all active in coming up with new approaches that could help minimize the issues in AI models and commercialize them to generate better revenue.
The game of Big Data here comes in. Big data techniques and methodologies are contributing to the production of huge data sets for diverse data sources and types. The more data, the better will be the AI models. Hence machines and automated systems will be replacing human effort in the near future creating an alarming situation for the employee distinction in the organizations.
A Blend of Humans and Diverse Data Shapes Future
Individually diverse data and human intellectuals do not produce expected results from AI models. The need is to harness them collectively. Combining the diverse dataset for model training and testing and intellectual diversity can help achieve efficiency in models. The game is just to label the input feature accurately in the relevant category and give output accordingly. A blend of diverse data and human intellectuals collaboratively enhances the optimization in the AI models making them more robust and accurate with respect to result precision.
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