The Psychology Of Algorithms: The Intersection Of Chatbots And Humans When human behavioral imprints are used to evolve artificial chatbots, machine learning modalities power them to exhibit a nearly human-level interaction

In modern commerce, humans are not the only ones alluring buyers and providing after-sales support. Today, artificial intelligence-backed chatbots have gained impetus. Human psychological attributes are being used to program lifelike chatbots that align with our empathy spectrums. These futuristic chatbots can provide expert customer support and effectively market products. When human behavioral imprints are used to evolve artificial chatbots, machine learning modalities power them to exhibit a nearly human-level interaction.

Designing algorithms to understand human behavior

According to Drift's 2020 State of Conversational Marketing report, about 25 per cent of consumers used the chatbot functionality to communicate with brands in 2020 instead of other traditional tools such as emails, phone calls, and social media interactions. With the emergence of terms such as human chatbot relationships (HCRs), the exploration to replicate human behaviors on computer-operated chatbots has gained momentum. These relationships function on the psychological concept of self-disclosure, wherein revelation of personal information is the key and grounding factor for forging more profound relationships. All chatbots have algorithms designed from a previous set of information disclosed by the consumer, which gets built on to become more predictive and relevant with every passing interaction. As humans find it easier to express themselves in online chats (instead of face-to-face interactions), more information about them is gathered and imbibed into machine learning algorithms to develop an experience aligned with the human spectrum of empathy.

Equipping chatbots to understand human feelings

Herein, a terminology called anthropomorphism comes into play. This concept attributes emotions, intentions and human traits to non-human entities, including objects like chatbots. As humans anthropomorphize more, chatbots gradually become purposeful in meeting their interactive needs. A text's simple tonality and mood language can help chatbots understand the emotional state of the interacting buyer. The pre-fed algorithmic database is made to contain emotional vocabulary that makes the chatbot skilled at emotional intelligence. For instance, words like joy, excitement, and delight can convey an understanding that the human counterpart is happy.

In contrast, words such as disappointed, upset, and bad mean that the user is troubled. Some chatbots take it further by using camera and sensor data from users' mobile devices to narrow down their emotional states. Infrared images and dots are used to read facial features and construe emotions. Moreover, data from sensors such as accelerometers, heart rate monitors, temperature, and light sensors are also being processed to ascertain the buyer's nearest mood landscape.

The necessity of human intervention in AI conversations

While not everything can be automated, relying entirely upon AI mechanisms is also not advisable. Human interventions are required to verify the results that the AI algorithms predict about their interacting counterparts. Though AI has advanced over time, it hasn't reached a position wherein complete trust can be placed on its predictions. While some reports state that prediction through facial recognition AI methodologies is accurate up to 96 per cent, a chatbot has its limitations. Not all of them employ facial recognition features, as asking for camera access may be flagged as a privacy concern by many users. In such cases, for the most accurate predictive interaction to be termed as true, human operators are required to analyze the output of the AI. If something is not aligned, learnings from the same are manually taught, and the system is augmented to evolve further.

Importance of empathy while designing conversations

Empathy is the core human trait that binds us together. Without an empathetic trait, we would not be able to make longer and stronger relationships. The same applies to chatbots. To be more relevant to consumers in assisting with their needs, machine learning and AI algorithms need to be programmed to pick up empathy characteristics in language. For instance, a chatbot that can remember your favorite foods, loved places, and current state of mood can provide a more comforting experience to the user. Such algorithms are highly efficient in the e-commerce industry, wherein appropriate predictions and suggestions can catalyze the sales of relevant products. For instance, in a food delivery app, if the chatbot learns that you like your pizza with garlic bread in lunch, it will automatically slip in such suggestions the next time you're up to buy.

Conclusion: A futuristic outlook

Conversational AI is the need of modern sales, and more emphasis has been placed on chatbots powered by machine learning tools. The more predictive a chatbot gets to comprehend the emotional mood of the consumer, the better and more pertinent it becomes for the consumers. While chatbot disruption in modern sales is not only bringing down monetary expenditure, it has become a new marketing tool.