Introduction
In the present-day world, the technological advances entailed a significant transformation in the mode of life, and it is possible to say that the new stage of social development is provoked by the growth of informational flow. From the psychological, moral, political, and economic perspectives, the scope of technology’s influence is highly extensive, and it does not leave any spiritual and material aspects of individual life unaffected. Technological advancement and free access to different types of information worldwide are deeply interrelated with current cultural changes and the evolution of global society.
The Hidden Biases in Big Data
Summary and critique
According to Crawford’s (2013) perspective, people tend to rely on big data indicators and consider that massive data “always reflect objective truth” but, in the author’s opinion, large data sets are associated with informational biases because big-data equations are incapable of capturing cultural and regional peculiarities of data sources and, as a result, it leads to data misinterpretation (par. 1). Based on this, it is suggested to employ qualitative analysis methods to achieve a better understanding of social differences and individual perceptions, decrease data biasing, and increase the efficiency of big data interpretation.
In the article, Crawford (2013) demonstrates the reasons why the results of massive data sets’ analysis administered according to traditional methodology may lack credibility. Nowadays, big data sets include a great variety of data types – audio, video, text messages, and other kinds of information – and the processing of such versatile data volumes becomes complicated when using only the quantitative analytical methods aimed to estimate and process numerical indicators.
Although it is considered that objective truth can be evaluated and scientifically explained through the administration of quantitative analysis that supports generalization of received information and detection of cause-effect relationships between the phenomena, the implementation of exploratory qualitative tools, such as interviews, and focusing on comprehension and explanation of versatile data may lead to the increase in validity of big-data interpretation by adding some subjectivity to the objective numerical information and making big-data interpretation more flexible.
Clarification
In her article, Crawford (2013) outlines that quantitative methods are considered to be traditional big-data analysis tools but, at the same time, she regards quantitative instruments as ineffective in capturing the inequities in digital devices distribution, differences in technology penetration, and the purposes of its use. She notes that “while massive datasets may feel very abstract, they are intricately linked to a physical place and human culture” (Crawford, 2013, par. 4).
Each geographic region and culture is associated with distinct demographic characteristics, social qualities, and individual interests. However, even within a particular community, the demographic differences among citizens may be sharp, and these drastic distinctions between diverse population groups may create significant gaps in the collected digital data sets.
Crawford (2013) draws a real-life example from Boston, where the citizens use the StreetBump app to detect potholes and report them (par. 4). However, the differences between lower and higher-income social members, as well as the low smartphones’ use frequency among the elderly population, make the input to app-related data sets from distinct demographic groups unequal. In this way, big data analysis that excludes consideration of multiple demographic and individual variables entails informational biases which make the further interpretation of data invalid and unreliable as it cannot be generalized to the whole population.
The suggested inclusion of qualitative analysis methods provides a solution to this problem. Free forms of data collection, including personal and focus group interviews, can help researchers to deepen the understanding of data’s origins and achieve clearer results in their interpretation. In this way, by considering potential demographic and cultural biases and addressing them through the application of qualitative instruments, it is possible to achieve greater objectivity and credibility in the interpretation of massive data sets.
Position Portion
Does technology improve relationships?
Nowadays, online social networks serve as one of the most popular means of communication. Facebook seems appealing due to the simplicity of its use and the provided opportunity to connect people from around the globe. Thus, many people associate it with the improvement and enrichment of social relations. However, despite these benefits, social online platforms may implicitly provoke negative psychological impacts on individuals, increase stress and anxiety.
According to Garber (2012), Facebook networks merge multiple social groups – friends, family members, colleagues – that impose diverse behavioral expectations on individuals (par. 5). The confluence of distinct social groups from different offline environments in a single online platform entails the possibility of harming someone’s feelings or own social identity by posting inappropriate content. As a result, the stress level increases. In the online context, it becomes more difficult to select appropriate communication tools to meet the expectations of different people. In this way, the online environment may provide a fertile ground for the development of interpersonal conflicts.
Is it only an algorithm?
Implementation of automatic bots induces many disputes in mass media. The researchers see great risks of blocking users’ access to large sets of diverse information provoked by the administration of automatic algorithms. However, automation can be used for a variety of purposes: promote ideas, generate new tendencies, and prevent the dissemination of particular types of information. It is possible to say that bots are harmless in their nature, but it is the intent behind their application which makes them good or bad.
Bots’ instant response to a variety of tasks endows them with a great potential of use. And according to Woolley and Hwang (2015), the negativity associated with automation of the programs is primarily related to the purposes of their implementation and the level of design sophistication.
Are we becoming information?
In order to increase brand awareness and product demand, advertising companies strive to implement more sophisticated and radical methods to find out potential customers’ preferences and needs. Nowadays, when the competition in the market is fierce, advertisers track online users’ activity through electronic devices to identify their interests and then personalize advertisements according to the obtained information about their identity.
According to Turow (2012), the advertising industry is now at the stage of the development of a new era when the companies will have the opportunity to develop personalized advertising campaigns for individuals by tracking their movements online.
Turow (2012) relates the rise of the new digital advertising era with significant privacy concerns and the risk of social discrimination. Such a personalized approach to advertising shifts the focus from individuals’ selection of informational content towards the company’s decision of what content a user should see. However, it is still almost impossible to identify a person’s true identity by monitoring the websites he/she visits, and, thus, advertisers’ personalization of promotional content lacks a solid rationalization.
Has technology changed cultural taste?
Modern media strongly influence pop culture development and the dissemination of new tendencies. Nowadays, the Internet provides a highly interactive space where anyone can express own creativity. Therefore, the online environment is characterized by intense competition for the attention of viewers and readers. To maintain users’ attention, independent bloggers or artists and traditional creative industries strive to add value to the broadcasted information.
According to Kutchinsky (2014), new technology-influenced culture is associated with a high level of connectivity. It means that web-mediated cultural evolution is based on the dialogue between culture consumers and producers. By offering opportunities for the evaluation of cultural content, modern communication media are deeply interrelated with the current cultural changes and formation of new esthetic tastes and tendencies.
References
Turow, J. (2012). How companies are ‘defining your worth’ online. NPR Books. Web.
Crawford, K. (2013). The hidden biases in big data. Harvard Business Review. Web.
Woolley, S., & Hwang, T. (2015). Bring on the bots. Civicist. Web.
Kutchinsky, S. (2014). Has technology changed cultural taste?The Guardian. Web.
Garber, M. (2012). Are your Facebook friends stressing you out? (Yes). The Atlantic. Web.