Connecting ethics and epistemology of AI

Abstract

The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and other normative considerations, such as intersectoral vulnerabilities, at critical stages of the whole process from design and implementation to use and assessment. To connect ethics and epistemology of AI, we perform a double shift of focus. First, we move from trusting the output of an AI system to trusting the process that leads to the outcome. Second, we move from expert assessment to more inclusive assessment strategies, aiming to facilitate expert and non-expert assessment. Together, these two moves yield a framework usable for experts and non-experts when they inquire into relevant epistemological and ethical aspects of AI systems. We dub our framework ‘epistemology-cum-ethics’ to signal the equal importance of both aspects. We develop it from the vantage point of the designers: how to create the conditions to internalize values into the whole process of design, implementation, use, and assessment of an AI system, in which values (epistemic and non-epistemic) are explicitly considered at each stage and inspectable by every salient actor involved at any moment.

A Philosophical Inquiry into AI-Inclusive Epistemology

“…However, Artificial Intelligence (AI) offers distinct capabilities that diverge fundamentally from human abilities and among many concerns other than ethics have been raised about it. [3], [4] AI systems are capable of processing and synthesizing vast amounts of data, uncovering patterns and relationships that frequently remain elusive or incomprehensible to human observers.…”

Scientific understanding through big data: From ignorance to insights to understanding

“… 17. Thanks to a reviewer for raising this issue. Discussions on the importance of transparency and communication in (collective) epistemology and ethics of AI can be found in Russo et al (2023: Sec. 4).…”