Building a Framework of Ethics and Trust in Conversational AI

Organizations implementing responsible AI can establish their own governance frameworks and best practices to guide the development and deployment process. These frameworks typically include principles such as accountability, transparency, fairness, privacy, security, and reliability. By adhering to these principles, organizations can ensure the ethical and responsible use of AI technologies. Ethical AI frameworks are designed to encourage the accountability, responsibility and transparency of AI applications. To be truly transparent, it should be clear to the user of the AI application that the designers followed responsible AI principles.

One of the challenges in achieving accountability lies in the rise of generative AI. As AI systems increasingly generate content, debates surrounding ownership and intellectual property rights for AI-generated content have emerged. Companies must navigate these complexities and establish frameworks to determine the ownership and responsible usage of AI-generated content. Building trust through transparency in conversational AI helps foster long-term user loyalty and encourages users to confidently engage with the technology. As mentioned previously, chatbots can be a great way to prompt the user for feedback during the conversation. If the chatbot does this, examine the user responses to understand if the users are satisfied or not.

What are the reasons an AI enterprise needs Ethical Conversational Design?

But why do we care so much to know whether our call-center agent is a person or a computer? You don’t know the person in the call center — you will probably never meet them or even speak to them again. Why it matters, and when it matters, is something that businesses need to figure out before they get rid of all their humans. However, when our voices are used to analyse more than just what we asked for, the privacy question becomes enormous. Remember that this is a technology that will encourage you to connect in a new way, to feel like you have a relationship with it. It may be therapeutic to tell your troubles to something that appears to care for you, something that listens, that says the right things — but is it wise?

One of the products IBM offers to its customers is IBM watsonx.governance, which improves oversight and compliance with ethical AI standards. An organization can visualize and track AI models in production, validate and test models to mitigate regulatory risks, and increase visibility of the AI lifecycle. As instances of unfair outcomes have come to light, new guidelines have emerged, primarily from the research and data science communities, to address concerns around the ethics of AI. Leading companies in the field of AI have also taken a vested interest in shaping these guidelines, as they themselves have started to experience some of the consequences for failing to uphold ethical standards within their products. Lack of diligence in this area can result in reputational, regulatory and legal exposure, resulting in costly penalties. As with all technological advances, innovation tends to outpace government regulation in new, emerging fields.

1 Ethical AI frameworks

Under infrastructure I summarize resources such as the provision of online data sets to help support ethical machine learning and online communities of experts or resources for debate etc. If a human agent is needed to complete a customer service session, the brand voice should remain consistent. “Emotion and empathy come down to what makes us unique as humans — creative thinking,” said Carter. Trust and loyalty go hand in hand, particularly so when it comes to a brand and its customers. When one loses trust in an entity, feelings of loyalty are also lost — and brands rarely gain them back. A report from Capgemini entitled AI and the Ethical Conundrum revealed that 54% of customers have daily AI-based interactions with brands, and more importantly, 49% of those customers found their interactions with AI to be trustworthy.

Developers and organizations have moral responsibilities when it comes to conversational AI. They must prioritize the well-being and rights of individuals and communities who interact with their AI systems. Ethical considerations extend beyond legal compliance and encompass the ethical dimensions of AI development and deployment. It is essential for companies to acknowledge and address bias in conversational AI to ensure fairness, inclusivity, and equal treatment for all users.

The classification of approaches is not in all cases easy and should be carefully interpreted. Several approaches are so generally described that it is not fully clear how to best categorize them. Others claim to address a range of issues while they may only be clearly formulated for some. A guiding principle in my assessment here was the question of practicality and implementability. Stay updated with the latest news, expert advice and in-depth analysis on customer-first marketing, commerce and digital experience design.

• So many proposals, frameworks, and ideas have been brought forward that scholars had to systematically analyze them, particularly those relating to “ethical frameworks”, e.g. [117, 121].
• The main aim of this paper is to review suggestions and approaches in the literature based on the work by Morley and colleagues [127] and to provide a systematic analysis of these ideas from an implementation perspective.
• They are concrete and usually address a very specific potential ethical shortcoming, e.g. regarding bias or explicability.
• As customers increasingly interact with AI technologies, they are finding these interactions to be trustworthy and reliable, leading to heightened customer trust.

The more positive the overall sentiment, the more likely the users were satisfied and vice versa. There is one caveat however, in that for some customer service interactions, users may already have a negative sentiment to start, hence the outreach, and it may be more important to look at the change in sentiment over the interaction. Enterprises are building chatbots not just to reduce costs and improve operational efficiencies, but to provide a better overall experience. While examining the top messages and Intents can help identify themes and popular use cases, looking at the bottom ones can help identify issues as well. As mentioned earlier, one agency building voice skills dove deeper into the Intents that were hardly being triggered, and realized they were ones that were more complex with multiple entities.

By embracing Responsible AI, organizations can build trust with their users and stakeholders, protect brand reputation, and establish a competitive advantage in an increasingly AI-driven world. The inbound user messages can be clustered based on Semantic Similarity to identify both Intents and potential training phrases. A great way to identify initial use cases is by analyzing historical data for common use cases. If you have an existing live-agent channel, the data can be processed to identify common requests. One way to do this is via Semantic Similarity clustering of the user requests, using tools like TensorFlow’s Universal Sentence Encoder or BERT, or a web service like PiRobot.

We are moving from having to understand computers to computers having to understand us. Our encounters with technology can be simpler, more accessible, more delightful — more human. As mentioned before, the tools currently support some steps of the AI development process better than others. There is a clear lack of systematic, operationalizable approaches for AI ethics monitoring, only little on deployment, and very little on data creation in the chosen data set. A relatively large group of 23% of the approaches address ethical issues at a general level.

Grappling with ethical uncertainties

Many papers directly address coding of ethical AI systems either at the level of improved algorithms or at the software level with libraries or design patterns. Several approaches consist in supporting an ethical AI system design process ranging from less formal guidelines to process models and stricter standards. Many articles can be best described as overviews that summarize various ethical concerns and provide examples of problematic ethical issues arising from AI systems.

Read more about What Are the Ethical Practices of Conversational AI? here.