Applying Ethical AI Frameworks in practice: Evaluating conversational AI chatbot solutions

While it’s unlikely for these rules to become legislated, there will likely be a voluntary guideline that brands can add to their corporate social responsibility practices. They can also seek input from a large and diverse set of stakeholders and seriously engage with high-level ethical principles. Early on, it was popularly assumed that the future of AI would involve the automation of simple repetitive tasks requiring low-level decision-making. But AI has rapidly grown in sophistication, owing to more powerful computers and the compilation of huge data sets.

Companies must establish clear processes and controls to ensure the quality, reliability, and traceability of their AI systems. This involves defining guidelines and protocols for the training, testing, and deployment phases of conversational AI projects. AI systems learn from real examples of human language, which can result in biased responses based on gender, religion, ethnicity, or political affiliation. One important aspect of transparency is the disclosure of whether the user is interacting with a bot or a human, especially in contexts where sensitive topics are discussed. This disclosure helps users understand the nature of the conversation and manage their expectations accordingly. Companies can achieve transparency by using disclosure messages that clearly indicate the involvement of AI and by openly communicating the AI system’s limitations.

Building Trust and Loyalty through Ethical Conversational AI

Table 7 presents the classification of all references using the categories defined above without the excluded cases and the outliers [21, 40]. Generally, the classification of approaches is straightforward, for example for most of the algorithmic approaches. However, a few cases are more difficult to decide, especially where there are mixed approaches. In these cases, it was some papers fall into several categories, e.g. they may propose proposals for algorithms and for measurement (metrics) or to focus on the clearest category. In addition, several works are very general or are otherwise difficult to translate into designs for AI systems, most notably [10, 17, 52, 70, 71, 74, 90, 94, 96,97,98, 104].

We should seek to use technology to empower people in ways that they feel have value, not only in ways we judge have value. On the other side of that, having new ways to connect with our technology can be positive. This can be especially true for those who are lonely or isolated and would benefit from someone to talk to. It can provide help to people who can’t see a human therapist due to waiting lists or lack of financial resources or who simply feel reluctant to. In addition, some of the problems are very hard to specify with the necessary algorithmic or mathematical precision.

Handle common messages

One branch, machine learning, notable for its ability to sort and analyze massive amounts of data and to learn over time, has transformed countless fields, including education. But its game-changing promise to do things like improve efficiency, bring down costs, and accelerate research and development has been tempered of late with worries that these complex, opaque systems may do more societal harm than economic good. Some approaches do not directly address ethical AI systems design but concern measures to be taken after an AI system is developed such as audits, labels, or licenses. Hagendorff analyzed 21 documents with an average of nine ethical principles [119].

• Bootstrapping from the 106 references provided by [127], I propose definitions and a systematic structure for the various approaches.
• This means avoiding responses that promote hate speech, offensive language, or stereotypes.
• One of the key aspects of responsible AI design is the establishment of shared code repositories, which promote collaboration and transparency among developers.
• An important focus of step 3, data creation, will be ethical ways of data collection, data acquisition, and data integrity; this extends to step 4, where data quality and accuracy need to be investigated together with potential biases.
• Firms already consider their own potential liability from misuse before a product launch, but it’s not realistic to expect companies to anticipate and prevent every possible unintended consequence of their product, he said.

Although they could be relevant for several steps, they are also difficult to operationalize. This is also the reason for not providing a complete categorisation of all approaches to steps here. One of the fundamental ethical considerations in conversational AI is having clear and well-defined goals. Organizations should have a deep understanding of why they are using chatbots and virtual assistants and how they align with the needs and expectations of their users.

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

• As we have more and more conversations with technology, we need to never forget that it isn’t a trusted friend.
• Explainable AI enables humans to better understand the reasons why AI makes certain decisions.
• Approved model architectures provide a structured framework for AI development, ensuring that models are designed with ethical considerations in mind.
• By focusing on enhancing the technology’s capabilities, organizations can deliver more accurate and personalized conversational experiences.