When it comes to the technology behind conversational UI, there might be some concerns that need addressing. This includes artificial intelligence (AI), machine learning (ML), large language models (LLM), generative AI, and more. Some of these concepts have been around and in use for a long time while others gained popularity more recently. At times they are also used interchangeably though they are not the same thing which can lead to confusion.
These technologies have gained immense popularity in recent years, offering the promise of more natural and personalized interactions with users, at scale. However, as with any emerging technology, there are challenges and limitations that need to be addressed. In this post, we will explore some of the main challenges and potential ways to address them.
Data privacy and security
Once Chat-GPT gained rapid traction with the general public and everyone was using it and posting about it, privacy and security concerns were raised pretty quickly. By companies like Walmart and Amazon as well as member states of the EU. These systems are designed to collect and analyze large amounts of user data. This poses a potential risk to user privacy and data security.
Companies must take steps to ensure that user data is collected and stored securely and that appropriate measures are in place to protect against data breaches and cyber-attacks. And there’ll certainly be outside pressure to ensure this. The EU already started working on regulation and if we learned anything from GDPR, they take privacy and security seriously.
At Unless, we address this with our AI privacy safeguard, a carefully designed infrastructure that shields personally identifiable information (PII) from leaking. First off it’s good to mention that the AI does not know who you are. We separate personal data in protected user-profiles and filter PII from ad-hoc user input. This way personal data never reaches the AI. Additionally, user profiles are located in a heavily secured data storage in the European Union only.
We are not only fully compliant with the GDPR and CCPA but also cookieless by default. This means advanced tracking can only be used after triggering our consent API however the default is to be cookieless.
Additionally, we take data policies seriously. What data we store, why, where, and for how long is all exposed transparently. You can find more information at our Data and Privacy Center.
Bias and fairness
Artificial intelligence is only as unbiased as the data it is trained on. If the training data is biased or incomplete, the AI may perpetuate and even amplify these biases. There have been various examples of this in regard to gender and race biases. To ensure fairness and prevent discrimination, it is crucial to carefully monitor and analyze the data used to train these systems.
To address the issue, companies can use diverse and representative data sets to train and employ techniques like debiasing and fairness metrics to identify and address bias in the training data. Additionally, they can implement user feedback mechanisms to identify and address biases in real time.
At Unless, you train your own AI to become an expert in your product, using the AI training zone in your account. (Ps. This is very simple to do within minutes and does not require any technical skills.) You can submit a variety of training sources such as your website, help center, FAQs, PDFs with marketing or sales material, and more. You control the material that your AI is trained on. It is also possible to test the AI and give feedback on the responses to train it better. What you put in determines what comes out.
User acceptance and adoption
Finally, the success of conversational UI and the technology behind it ultimately depends on user acceptance and adoption. Many users may still prefer traditional methods of communication, such as email or phone calls, and may be hesitant to embrace conversational UI. Companies must work to educate users about the benefits of AI solutions and provide a seamless and intuitive user experience to encourage adoption.
There are a couple of things that can be done to address this. Companies can implement these AI solutions with a user-centric approach, prioritizing ease of use and intuitiveness. They can also invest in user education and training to help users understand the benefits of AI technology and how to use it effectively. Additionally, companies can implement feedback mechanisms to solicit user feedback and use that feedback to continually improve the experience.
For us at Unless, the last couple of months have been interesting. This is because machine learning and artificial intelligence have been part of our product and offering for a couple of years already. And in the past, we would come across hesitance or concern more often. The tide seems to have shifted. Now existing customers and new contacts are approaching us, looking for ways to implement these technologies into their products.
There is certainly significant hype at the moment and the speed at which ChatGPT reached 100 million monthly active users is proof of that. These technologies have gained a lot of traction in a short amount of time. The important thing now will be to make it last. The way to do that is to not just have AI for the sake of it but consider the value you are adding and the impact it is having on users. Use it to streamline your processes and make the life of your users easier and more pleasant.
In conclusion, while conversational UI powered by technologies like AI, ML, and LLM holds great promise for natural and personalized interactions with users, it is essential to address potential concerns surrounding its implementation.
Data privacy and security must be prioritized, with strict measures in place to protect user information. Bias and fairness issues can be mitigated through diverse and representative training data and real-time user feedback. Companies should focus on providing a user-centric experience and continuously improving the technology based on user feedback.
At Unless, we understand the importance of addressing these concerns. Our AI privacy safeguard ensures the protection of user data, and we are fully compliant with regulations like GDPR and CCPA. By allowing users to train their own AI and providing transparent insights into data policies, we empower companies to leverage conversational UI effectively. The growing interest in implementing these technologies indicates the value they bring, but it is crucial to prioritize impact and user experience to ensure their long-term success.