Artificial intelligence (AI) and big data can help recruiters better understand a person’s personality and behavioral style.
Perhaps the biggest beneficiary of big data is the field of artificial intelligence.
Combined, these two techniques can take psychometric analysis to the next level. Studying the impact of artificial intelligence and big data in psychometrics will be crucial for future improvements in the field.
The number of areas where psychometric assessment can have an impact is truly mind-boggling. From assessing job candidates during recruitment to running national campaigns, from marketing to law enforcement, psychometric assessments play an important role in understanding the pulse of large groups of people or the personality traits of an individual. If organizations, whether political parties or businesses, fully exploit the big data capabilities of psychometrics, they can gain an almost unassailable advantage on their respective battlefields.
As we all know, digitalization is penetrating into almost all aspects of people’s lives. Therefore, technologies such as artificial intelligence and big data will naturally have an impact in the field of psychometrics. The incredible data processing and analysis capabilities of artificial intelligence are well known in this day and age. Combining these properties with the comprehensive nature of big data is like providing rocket fuel for the growth and development of psychometrics. Wondering what (or how much) artificial intelligence and big data can achieve in psychometrics? Here are some answers:
Psychological testing has typically been done in the past Purpose of using logistic regression analysis. While these technologies have their advantages, they are simply not comparable to the achievements of artificial intelligence (supplemented by big data) in this field. For example, HR leaders can use machine learning to identify candidates’ strengths and weaknesses. To do this, HR leaders ask candidates a series of questions during interviews or remote interviews. When candidates answer questions, their demeanor, tone, and facial expressions can all be monitored through AI cameras. After the interview, recruiters use AI to assess the candidate’s perspective and judgment, empathy and emotional intelligence, as well as engagement, decision-making and supervisory abilities. These attributes are judged and evaluated to understand how the candidate engages in collaborative problem-solving and plays a decisive role in high-pressure situations.
In addition to decision-making and problem-solving abilities, candidates’ ability to complete their respective jobs within strict deadlines can also be assessed with the help of artificial intelligence and big data. In addition to interviewing and hiring exercises, other techniques can be used to assess a candidate's personality. For example, a recruiter can browse a candidate's social media pages to learn about their personality traits and opinions on general topics. Viewing someone's social media page should not be a way to negatively evaluate one's views. Instead, this is a good measure of how a candidate expresses their ideas verbally or visually. In short, the communication skills of the applicant can, to a certain extent, be determined in this way. Artificial intelligence and big data can help recruiters find this data on the web and then process it through pattern and anomaly recognition to find potential personality traits of candidates.
In addition to this, machine learning can further be used to integrate augmented reality tools into candidate recruitment. Augmented reality tools can create real-world-like simulations to assess candidates’ ability to handle actual operational crises. Artificial intelligence uses the vast repository of big data to evaluate candidates' performance on this test. Augmented reality adds a whole new dimension to candidate recruitment and selection that would not be possible without the power of artificial intelligence and the staggering scope of big data.
You may have heard how Cambridge Analytica helped former US President Donald Trump win the 2016 election. Mr. Trump’s campaign was one of the most data-driven political campaigns ever. Before exploring, however, it is important to understand the primary purpose of psychometric analysis.
Psychological testing is first used to obtain information about an individual (or a group of people), as well as their likes, dislikes, views and opinions on various topics. How the data collector processes this information depends on the type of end result desired. In this case, big data and artificial intelligence can help expand the scope of psychological assessments across the state or country. It has been proven that a person's personality can be researched to convince him or her to purchase certain products or services. What's more, this information can be used to persuade individuals to vote for a specific candidate or party in an election.
Let’s take a look at Cambridge Analytica’s role in influencing the 2016 U.S. presidential election.
There are indications that the technology company has been associated with Mr. Trump’s campaign for some time before the campaign. The group used psychometric artificial intelligence and big data to gain an electoral advantage. This approach is particularly groundbreaking because previous candidates have primarily leveraged demographic arguments and focused on other core voter issues. Cambridge Analytica brings advanced psychometrics into the mix to produce positive end results.
To succeed in the election, the organization uses behavioral science and voter monitoring, in addition to some common tools such as the OCEAN model, the concept of bombarding individuals with AI-driven systems and models, and advanced big data analyze.
The initial stage of this process required the organization to purchase large amounts of data on millions of individuals from social media pages of well-known organizations such as Facebook. In addition to such records, details such as pending maintenance bills, land and property registers, shopping data, purchase history of products and services, etc. are also collected and carefully analyzed. If the message is long and wide, that means it covers several people and several aspects of each person. In other words, big data. After gathering all this information, the British company aggregated and organized the data. In addition, the organization has deployed artificial intelligence tools to classify each person differently based on the Big Five personality traits.
Based on this information, Republican presidential candidates addressed voters in speeches that were more vulnerable and easier to manipulate. Even election speeches were carefully tuned and tailored to resonate with individuals across all segments of society. The company has generated over $5 million in revenue for its highly data-driven efforts. Yet the real heroes in Mr Trump’s landslide victory were artificial intelligence and big data.
As mentioned above, artificial intelligence and big data can be used to understand the characteristics, likes and preferences of potential customers in order to use specific, targeted marketing Ads flood their inboxes. For marketing purposes, organizations use big data, including customers’ social media pages, digital retailers’ purchase history, and even text messages in some cases.
Compared with artificial intelligence, big data is arguably more important in the above application areas. So, now that we have seen some of the application areas of artificial intelligence and big data in psychometrics, here are the challenges that organizations may face when using big data for personality analysis:
1. What big data brings The issue relates to the reliability of the information provided to the AI system for analysis. The reliability of big data will be seriously affected by existing data, technology and artificial intelligence algorithms. The chaos and complexity of big data can cause problems for AI systems when making predictions and high-level decisions.
2. Bias in artificial intelligence has always been a problem that technology needs to overcome. With the addition of big data, the fairness of AI output may remain an issue. In addition, it can also be said that the scope of influence of artificial intelligence and big data is limited to some extent by the closed greenhouse of the Internet. Therefore, in many cases, big data is insufficient to include information about economically disadvantaged individuals or households because these people do not have access to the Internet and cannot purchase computing devices.
3. After reliability and fairness, comes the challenge of user privacy. As seen, artificial intelligence and big data make extensive use of user data (sometimes without the user’s signed consent) to produce final results. Therefore, big data and artificial intelligence continue to face ethical dilemmas in this regard.
The myriad capabilities of artificial intelligence and big data are critical to the field of psychometrics. However, there are some challenges that need to be addressed for further improvements. But it is certain that these techniques can further deepen the scope of psychometrics in the future, given its near-continuous development. In the meantime, big data and artificial intelligence will continue to remain in the field of psychometric research to achieve the above purposes and more.
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