Benchmarking, ethical alignment, and evaluation framework for conversational AI: Advancing responsible development of ChatGPT

By establishing guidelines for responsible AI design, organizations can ensure that AI models are built in a safe, trustworthy, and ethical manner. By adhering to the principles of responsible AI, organizations can develop and deploy AI systems that prioritize accountability, transparency, fairness, privacy, security, reliability, and sustainability. These principles guide the ethical and trustworthy development of AI technology and shape the future of responsible AI. Designing responsible AI involves setting clear goals and principles, as well as following best practices for governance, risk management, and training. It also requires creating AI systems that are explainable and interpretable, as well as eliminating biases and ensuring privacy and security. Responsible AI implementation can vary among organizations, but it often involves the development of AI frameworks and the establishment of dedicated teams to oversee responsible AI practices.

Therefore, a tool-based approach to ethical AI systems still raises many questions about the relation of ethics and AI designs, cf. Over time, this should help establish an ethical practice and condemn unethical practices, taking into account specific context, domain ethics, and intended purpose. Such approaches may be combined with audits, labels, declarations, and regulation.

1 Missing ethical issues

To fill the gap, ethical frameworks have emerged as part of a collaboration between ethicists and researchers to govern the construction and distribution of AI models within society. This past August, the Open Voice Network (OVON), under the auspices of The Linux Foundation, successfully launched its Ethical Principles for Conversational AI course on edX! Guided by the principles of their TrustMark Initiative and carefully developed by OVON’s Ethical Use Task Force, this course has empowered organizations across the globe to create more trustworthy and ethics-based AI interactions. From the United States and Belgium to India and Japan, the course has been completed by users from 27 countries – and OVON looks to continue spreading awareness even further. We shouldn’t underestimate the impact of being able to use natural language as an interface. With it, we don’t need to learn specific commands or understand how to navigate an application’s complex interface.

• Born from real-world application and an urgent demand for practical guidelines, principlism has become a mainstream ethical approach in medical and biomedical practice.
• But the nine steps in this model should capture most of the relevant ethical decision points during the design of a new AI application.
• Companies must establish clear processes and controls to ensure the quality, reliability, and traceability of their AI systems.
• When one loses trust in an entity, feelings of loyalty are also lost — and brands rarely gain them back.
• And medical professionals expect that the biggest, most immediate impact will be in analysis of data, imaging, and diagnosis.
• Get expert guidance on building intelligent apps with Azure Container Apps and AI services.

Facebook infamously granted Cambridge Analytica, a political firm, access to the personal data of more than 50 million users. Based on the results presented in Tables 7 and 8, it is now possible to study the relation between ethical issues and proposed approaches. For the issues addressed by more than 10 papers, Table 9 gives the number of approaches greater than one addressing the issue (e.g. 7 papers that address privacy suggest approaching the issue with an algorithm or a computational method). The classification of references can be performed according to approaches and by the ethics issue addressed by the approach (Tables 7 and 8).

Transparency

The release of ChatGPT in 2022 marked a true inflection point for artificial intelligence. The abilities of OpenAI’s chatbot — from writing legal briefs to debugging code — opened a new constellation of possibilities for what AI can do and how it can be applied across almost all industries. ChatGPT and similar tools are built on foundation models, AI models that can be adapted to a wide range of downstream tasks. Foundation models are typically large-scale generative models, comprised of billions of parameters, that are trained on unlabeled data using self-supervision. This allows foundation models to quickly apply what they’ve learned in one context to another, making them highly adaptable and able to perform a wide variety of different tasks.

20+ years industry experience centre technology across retail, financial services, telecoms and more. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. The goals depend on the chatbot and could be getting jobs done, getting people past where they are stuck, getting the user the information they need, completing a transaction, or anything else. The chatbot was not set up to understand or respond appropriately to the user’s request for help in this way.

It involves making AI systems more understandable and explainable to end-users and stakeholders. Greater transparency allows individuals to have insights into how AI systems make decisions, understand the factors that influence those decisions, and have the ability to question and challenge them. The goal is to have a widely adopted governance framework of AI best practices that promote human-centered, interpretable, and explainable AI systems. Currently, the implementation of responsible AI varies from company to company and relies on the discretion of data scientists and software developers. While it can be a challenge to develop given the free-form communication and unstructured data, you can follow an iterative process to build compelling, high quality, highly effective chatbots and voice assistants. Chatbots provide a great opportunity to gather feedback, given the conversational nature.

This means avoiding responses that promote hate speech, offensive language, or stereotypes. By maintaining a high standard of fairness, companies can create AI systems that provide a positive user experience for all users. By prioritizing privacy and implementing robust data protection measures, companies can demonstrate their commitment to responsible AI usage and build trust with users. Strict data security measures are essential throughout the entire lifecycle of conversational AI systems.

Data protection measures in Conversational AI

The approaches are spanning the whole range of concreteness from coded software to broad conceptual considerations. The latter are often offered in response to overall ethical concerns at societal level or involving several ethics issues at once. The more concrete and well-specified approaches such as code and algorithms mostly address only few ethical issues and usually only one at a time. This confirms and refines previous analyses in showing that many proposed technical approaches focus on only a few ethical issues, e.g. on explicability and fairness of AI systems.

With chatbots, users not only say what they want, but what they think about the response as well. This is an opportunity to gather additional insights that can be used to improve the experience. Consider proactively asking the user if they are satisfied with the response or interaction — i.e. “was that helpful, yes or no?

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