OSD, Data Analytics Cell,
Views are personal
There are differing views of Artificial Intelligence (AI), with Elon Musk asserting that “AI is the biggest risk we face as a civilisation” on one end, and Bill Gates stating that such threats are not quite true, on the other extreme of the spectrum. The narrative on AI and Big Data has been combined with massive job losses. The fields of Big Data Analytics and Artificial Intelligence have been around for the last couple of decades, developing across different sectors. This has led to citizen empowerment, resulting in improved career prospects rather than job losses. Based on a few examples, this article would demonstrate this aspect of citizen empowerment based on AI and Big Data Analytics. Consider a call centre agent for an online retailer or your mobile service provider who logs customer calls and complaints. While the call centre agent logs the issue for which the customer had called to complain, the agent offers some interesting offers relevant to the customer—the customer accepts the offer and disconnects the call on a happy note. Since the call ended on a happy note, the agent gets a good rating and receives due recognition for the same. This personalised offer that popped up on the agent’s computer terminal is based on an Artificial Intelligence algorithm running at the backend. While the agent was typing in the complaint, the intelligent system at the backend would understand that the customer is unhappy due to some issue. The algorithm goes through huge volumes of data on past customer records and groups the customers across various characteristics into logically identifiable ways. Past data also provides insight on other customers’ reaction to similar issue. The predictive nature of the Artificial Intelligence algorithm finds the best offer that customers of similar profile, facing similar issues that the customer is most likely to accept. It would be pretty impossible for the agent to multi-task while receiving the call and also find the most appropriate offer for the customer. Big Data-based Artificial Intelligence here plays the role of empowering the call centre agent towards becoming a smart call centre agent.
Let’s consider another example, of healthcare. India has less than one (0.725) physician per thousand persons and the number is even worse for the number of specialists per thousand persons. Given the rate at which new physician or doctors are entering the system, this figure is unlikely to change in the near future. Hence, a large number of rural healthcare centres face a shortage of doctors and function with only nurses on the ground. Here, Big Data-based Artificial Intelligence can help in bridging the skills gap and empowering the nurses. While attending to the patient, the nurse can enter the key patient symptoms and measurements into a hand-held device. Based on the information about the patient symptoms and measurements, the device recommends the likely diagnosis and prescribes suitable medication—this may be further augmented with detailed explanation for prescribing these medications. In this case, the nurse can administer a prescription that an experienced doctor would have prescribed. The nurse would also have the option not to accept the recommendation if it doesn’t seem most suitable for the current patient condition. Again, these recommendations are provided based on Big Data Artificial Intelligence algorithms running at the backend—which analyse large amounts of patient records anonymously with similar symptoms and administered medications. Now one might propose taking these applications a step further and making the whole process automated—by which the call centre agent and the nurse would become jobless, instead of becoming empowered. This is unlikely to happen as, in both the cases, there is an aspect of human empathy that plays an important part of service delivery. The human aspect in these operations is important for the empathy aspect, along with ensuring that AI-based recommendation is appropriate in a given situation. Adoption of any new technology, including the use of Big Data-based AI, to empower the call centre agent or the nurse involves change management. This change management is an important aspect of any new technology adoption program and can be the thin defining line between the success and failure of this change initiative. In both these examples, the call centre agent or the nurse need to equip themselves towards the use of the digital platform which is intended at empowering them—this might involve a bit of new skill development. Lack of appropriate skilling along with new technology adoption often leads to the failure of such programmes and the new technology. This, in turn, is made responsible for the job loss due to this skilling gap. For example, the nurse might be well-trained in handling the patient and taking observations, but might not be well-versed in using a hand-held device, its various options, etc. In such a case, the nurse who is an expert in her medical domain needs to be augmented with appropriate digital skills, which would empower her in her new functions with the help of AI. Since the people aspect and empathy cannot be completely automated by machines, jobs would remain relevant in areas related to human interaction. People need to be skilled in order to appropriately adopt new technologies. This can be in terms of interacting with a digital interface, understanding the recommendations and scores from the backend AI engine or developing a judgement about when to follow the AI recommendations and when not. In conclusion, skilling and skill augmentation holds the key regarding public perception of Big Data-based AI as a vehicle for job loss or citizen empowerment.