Securing Customer Trust in the Age of AI

In the age of Artificial Intelligence where the fears of deep fakes reign supreme, customer trust seriously comes under question. It is also true that the blatant misuse of Artificial intelligence is solely responsible for the trust issue. Though AI has several positive roles to play, especially in customer relationship management, it is still tabooed as a killer of the customer trust. At ConvergeHub, we are constantly leveraging the potential of AI to build and design CRM strategy that works.

It’s high-time to defeat the AI fear

Like every other technology, Artificial intelligence also came with its own set of good and bad. However, as a responsible organization, we have incorporated AI capabilities in our CRM product to accelerate daily time-intensive tasks in matters of seconds. For businesses looking to lower costs and increase productivity, AI can be very powerful too. An AI-powered CRM accelerates innovation by quickly delivering what customers want.

But, what causes the fear?

Ideally, the lack of transparency erodes trust. A lot of times, businesses often delay the adoption of AI or end up unsuccessfully implementing it, creating a competitive disadvantage. Along with that, there’s the risk of data breach with Artificial intelligence being on the scene. Here comes the promising role of AI that could be used to address data breach challenges.

Come to Precision Data to make the most of Artificial Intelligence

Data is essential for successful usage of AI. because AI systems feed on data to deliver value. Often that data includes personal information, information about identified or identifiable individuals. All this data is ideally used by the AI to deliver the result.

How to secure customer trust amidst all the uncertainties?

Distinguishing between trustworthiness and responsibility within AI is essential. A trustworthy AI system consistently performs as expected, fostering reliability. However, this reliability must mirror the trust humans invest in each other. Thus, while we may not extend trust to AI like humans, developers and stakeholders are not absolved of accountability for AI system failures.

Trustworthiness relates to technical performance, ensuring that AI systems yield dependable results. On the other hand, responsible AI transcends mere technical reliability to encompass the broader ethical and societal repercussions of AI systems. It entails deploying AI in ways aligned with moral principles, upholding fairness, and mitigating adverse consequences.

Trustworthiness centers on the system’s capacity to generate reliable and consistent outcomes, holding AI systems responsible for technical mishaps or inconsistencies in performance. In responsible AI, the onus shifts to developers and stakeholders to guarantee accountable and ethical development and deployment of AI.

How to Ensure Accountability?

On the developers’ side, it needs to be taken care of that they must refrain from creating AI solutions that might have technical glitches and come with extremely biased outcomes. It comes from an ethical obligation, recognition of societal impact, and principles of fairness and transparency. It is extremely important to practice accountability that nurtures trust and ensures that AI benefits society while mitigating potential risks.

Another area where AI has a critical impact is employment. For instance, the increasing use of AI-driven chatbots in customer service has reduced human involvement, enhancing efficiency. It is critical to factor in potential job displacement when designing AI systems and consider how employees can build new skill sets.

AI systems often have an inherent bias while devising training data. It not only influences decisions, but also erodes trust at some point of time. For instance, an AI-based recruitment tool was biased toward male candidates due to skewed training data and eventually scrapped. Biases and other issues must be identified and rectified as quickly as possible.

Transparency Is Key

Transparency is the lynchpin for upholding public trust in AI. Financial institutions must be transparent in explaining AI decisions, especially in areas such as automated trading and investment recommendations.

Accountability augments transparency by compelling developers to take ownership of AI failures. For instance, a glitch from an AI algorithm in a major financial firm can cause a market disruption, resulting in significant losses. The firm must investigate the issue and swiftly inform regulators and clients. They must take ownership and collaborate with regulators to enhance industry-wide safeguards, reinforcing a culture of accountability and transparency.

Legal frameworks are also maturing to regulate AI deployment. For instance, the EU’s General Data Protection Regulation (GDPR) mandates AI systems to safeguard user data privacy.

Developers’ accountability for AI’s societal impact, bias mitigation, transparency, and regulatory compliance is essential. It underscores their crucial role in steering the evolution of AI technology in ways that enrich society. Responsible development fosters trust among users and stakeholders – a prerequisite for the widespread acceptance and integration of AI across diverse domains.

Wrapping up

Establishing trust in AI is a collective journey and needs the concerted efforts of developers, policymakers, and society. Through collaborative efforts, AI’s potential can be harnessed while trust stands at the cornerstone of technological evolution.