Responsibility and ethics of AI in the contact center

AI makes it easier to collect and process massive amounts of customer data, to help organizations predict consumer behavior and personalize individual experiences. However, alongside the use of this data and the interactions between AI systems, customers and contact center agents, there are concerns about the ethics of AI. With the beneficial rise of AI in the contact center is set to continue, UK organizations must take steps to develop AI strategies that are both practical and ethical.

The past few years have seen the customer push for digital and personalized experiences increase. Therefore, to provide cost-effective customer services that keep up with customer expectations and competition, artificial intelligence (AI) systems are a must. However, the deployment of AI in the contact center requires taking into account new practical and ethical issues in the sphere of customer service.

Ethics is a human thought process, which applies the concepts of ‘right’ and ‘wrong’ behavior to activities and actions. Therefore, as AI-enhanced systems like automating and intelligent routing systemsare only tools – the responsibility of AI Ethics rests with the people assigning their function and deployment. By asking the big questions to define “how”, “why” and “where” artificial intelligence will be deployed, it is possible to create a framework to minimize risk and maximize efficiency. If companies consider the applications and outputs of AI systems, such as “advice and guidance” or “decision making,” they can also control the associated risk.

Understanding AI Ethics

In order to visualize where AI ethics should be applied, it is helpful to consider what an AI system is. To do this, we must start by looking at automated tools. These tools generally work well for repetitive tasks and are essentially ‘decision trees‘, boiling down to the simple set of rules: if ‘x’ occurs, then the next step is ‘y’. Artificial intelligence comes into play when these decisions are made more accurate by applying models trained with machine learning (ML) methods. Also, the more these systems work with data, the more examples they can use to adjust these decisions.

In this way, performance and accuracy constantly increase through learning and adjustment. These decision trees can grow increasingly complex, processing masses of information, almost instantaneously, and yielding potentially endless “if-then” decisions that benefit agents and customers with speed and precision.

However, in some other applications, the ‘black box phenomenon‘ comes into play. In this situation, why an AI is making a certain decision becomes obscured by an incomprehensible level of complexity. The input and output are visible, but the inner workings of the AI ​​decision, “the black box”, are unknown. All systems have innate limitations, even the newer “explainable AI” which aims to eliminate black box situations. However, due to the sheer complexity of decision trees and the imperfect nature of the data fed into the imperfect systems in which AI tools are deployed, it is possible to inadvertently introduce bias. In some cases, this accidentally perpetuated social inequalities, such as race and gender. For example, science career ads are disproportionately targeted at a male audienceso social media algorithms could mean advertising jobs here breaks UK equality lawand facial recognition software tends to be less accurate at recognizing darker skin tones.

Within the contact center, however, when personalization is a driving factor, decisions are made based on a customer’s individual input, the impact of this is minimized. Of course, no system is ever perfect, which is why ongoing ML training and oversight of the AI ​​mission and success with the voice of customer programs, KPI analysis and tracking are essential. .

AI ethics to consider when deploying

A simple consideration, which can impact the trust customers develop in AI systems, is transparency of use. The icons, names, and voice of AI-driven bots should make it clear to the user, or customer, that they are interacting with an AI and not an agent.

The quality of all AI systems also depends on the quality of the data they operate on. As the saying goes: garbage inside, garbage outside. Errors and bad results from AI most often depend on the quality of the information fed into it. It is therefore crucial to keep accurate, up-to-date and non-siloed data. Without it, the results of these AI processes will be unsatisfactory, or in a minority of cases – for example, those involving vulnerable customers – even harmful.

AI systems are meant to help agents, whose empathy and ability to think laterally about complex and unique customer situations will always be needed. Final decisions or interactions with serious consequences and emotional circumstances should always be escalated, or directly road (using AI systems) to agents.

This brings us to the next point, the role of AI systems should be to increase and assist, do not replace agents. We feared that almost 30% of UK jobs were at risk to be replaced or made redundant in the early 2030s, due to automation. However, more balanced reviews now suggest that the actual result is unlikely to be as black and white with certain trades being more suited to automation that others. This must also be weighed against the ‘Big resignation‘ currently underway, with the implication that there are simply jobs that people don’t want, or that currently don’t offer enough job satisfaction. Therefore, where AI is deployed, to accomplish the tasks that people don’t want or to help them achieve greater job satisfaction, it can be seen as an ethical way to go.

AI ethics is an equation that CCaaS solutions can balance

Contact Center Solutions as a Service (CCaaS) providing contact centers with both the AI-powered tools to meet customer needs cost-effectively and efficiently, as well as the keys to supervision. Omnichannel robotsintelligent routing and agent assistance features all use AI to free up agents’ time and allow them to use their skills to the fullest. Analytic and speech analysis facilitate the accumulation of an increasing amount of data to fuel AI precision and data-driven policy decisions. In this way, AI can be seamlessly integrated and expertly monitored, to empower business leaders with the power of AI, with the assurance of an ethical model.

Odigo helps large organizations connect with people through world-class cloud contact center solutions. Its cutting-edge proprietary technologies enable a seamless and efficient omnichannel experience for its customers, and a satisfying and engaging experience for service agents. Odigo serves over 400,000 agents and business users worldwide. With a 35-year history of industry firsts, Odigo has over 250 customers worldwide.

For more information about Odigo, see their company profile