Enrico Panai

Explanations are conceived to ensure the trustworthiness of AI systems. Yet, relying solemnly on algorithmic solutions, as provided by explainable artificial intelligence (XAI), might fall short to account for sociotechnical risks jeopardizing their factuality and informativeness. To mitigate these risks, we delve into the complex landscape of ethical risks surrounding XAI systems and their generated explanations. By employing a literature review combined with rigorous thematic analysis, we uncover a diverse array of technical risks tied to the robustness, fairness, and evaluation of XAI systems. Furthermore, we address a broader range of contextual risks jeopardizing their security, accountability, reception alongside other cognitive, social, and ethical concerns of explanations. We advance a multi-layered risk assessment framework, where each layer advances strategies for practical intervention, management, and documentation of XAI systems within organizations. Recognizing the theoretical nature of the framework advanced, we discuss it in a conceptual case study. For the XAI community, our multifaceted investigation represents a path to practically address XAI risks while enriching our understanding of the ethical ramifications of incorporating XAI in decision-making processes.

Explanations are conceived to ensure the trustworthiness of AI systems. Yet, relying solemnly on algorithmic solutions, as provided by explainable artificial intelligence (XAI), might fall short to account for sociotechnical risks jeopardizing their factuality and informativeness. To mitigate these risks, we delve into the complex landscape of ethical risks surrounding XAI systems and their generated explanations. By employing a literature review combined with rigorous thematic analysis, we uncover a diverse array of technical risks tied to the robustness, fairness, and evaluation of XAI systems. Furthermore, we address a broader range of contextual risks jeopardizing their security, accountability, reception alongside other cognitive, social, and ethical concerns of explanations. We advance a multi-layered risk assessment framework, where each layer advances strategies for practical intervention, management, and documentation of XAI systems within organizations. Recognizing the theoretical nature of the framework advanced, we discuss it in a conceptual case study. For the XAI community, our multifaceted investigation represents a path to practically address XAI risks while enriching our understanding of the ethical ramifications of incorporating XAI in decision-making processes.

Artificial intelligent technologies are utilized to provide online personalized recommendations, suggestions, or prompts that can influence people's decision-making processes. We call this AI-enhanced nudging (or AI-nudging for short). Contrary to the received wisdom we claim that AI-enhanced nudging is not necessarily morally problematic. To start assessing the risks and moral import of AI-nudging we believe that we should adopt a risk-factor analysis: we show that both the level of risk and possibly the moral value of adopting AI-nudging ultimately depend on the complexity of the “moral situation” in which various risk-factors play a causal role in determining levels of uncertainty.

In informationally mature societies, almost all organisations record, generate, process, use, share and disseminate data. In particular, the rise of AI and autonomous systems has corresponded to an improvement in computational power and in solving complex problems. However, the resulting possibilities have been coupled with an upsurge of ethical risks. To avoid the misuse, underuse, and harmful use of data and data-based systems like AI, we should use an ethical framework appropriate to the object of its reasoning. Unfortunately, in recent years, the space for data-related ethics has not been precisely defined in organisations. As a consequence, there has been an overlapping of responsibilities and a void of clear accountabilities. Ethical issues have, therefore, been dealt with using inadequate levels of abstraction (e.g. legal, technical). Yet, if building an ethical infrastructure requires the collaboration of each body, addressing ethical issues related to data requires leaving room for the appropriate level of abstraction. This paper first aims to show how the space of data ethics is already latent in organisations. It then highlights how to redefine roles (chief data ethics officer, data ethics committee, etc.) and codes (code of data ethics) to create and maintain an environment where ethical reasoning about data, information, and AI systems may flourish.

The alignment problem in the context of large language models must consider the plurality of human values in our world. Whilst there are many resonant and overlapping values amongst the world’s cultures, there are also many conflicting, yet equally valid, values. It is important to observe which cultural values a model exhibits, particularly when there is a value conflict between input prompts and generated outputs. We discuss how the co- creation of language and cultural value impacts large language models (LLMs). We explore the constitution of the training data for GPT-3 and compare that to the world’s language and internet access demographics, as well as to reported statistical profiles of dominant values in some Nation-states. We stress tested GPT-3 with a range of value-rich texts representing several languages and nations; including some with values orthogonal to dominant US public opinion as reported by the World Values Survey. We observed when values embedded in the input text were mutated in the generated outputs and noted when these conflicting values were more aligned with reported dominant US values. Our discussion of these results uses a moral value pluralism (MVP) lens to better understand these value mutations. Finally, we provide recommendations for how our work may contribute to other current work in the field.