Politicians and engineers are increasingly realizing that values are important in the development of technological artefacts. What is often overlooked is that different conceptualizations of these abstract values lead to different design-requirements. For example, designing social media platforms for deliberative democracy sets us up for technical work on completely different types of architectures and mechanisms than designing for so-called liquid or direct forms of democracy. Thinking about Democracy is not enough, we need to design for the proper conceptualization of these (...) values. As we see it, we cannot responsibly engineer and innovate and shape technology in accordance with our moral values without engaging in systematic and continuous conceptual engineering: This is not only an academic, or theoretical issue, it is also not simply an issue for public policy or politics, or regulators, it has become a central problem for engineering and the world of technology. In this paper, we present a framework for doing the necessary conceptual work in the context of requirement engineering. We draw on the literature on conceptual engineering to lay out a methodology to (1) assess different conceptions and (2) to develop new conceptions. Moreover, we integrate this methodology with extant approaches in the philosophy of technology which aim at designing technological artefacts ethically. In the final section we apply this integrated framework to freedom in the context of social media networks. (shrink)

Algorithmic biases, or algorithmic unfairness, have been a topic of public and scientific scrutiny for the past years, as increasing evidence suggests the pervasive assimilation of human cognitive biases and stereotypes in such systems. This research is specifically concerned with analyzing the presence of discursive biases in the text generated by GPT-3, an NLPM which has been praised in recent years for resembling human language so closely that it is becoming difficult to differentiate between the human and the algorithm. The (...) pertinence of this research object is substantiated by the identification of race, gender and religious biases in the model’s completions in recent research, suggesting that the model is indeed heavily influenced by human cognitive biases. To this end, this research inquires: How does the Natural Language Processing Model GPT-3 replicate existing social biases?. This question is addressed through the scrutiny of GPT-3’s completions using Critical Discourse Analysis (CDA), a method which has been deemed as amply valuable for this research as it is aimed at uncovering power asymmetries in language. As such, the analysis is specifically centered around the analysis of gender and race biases in the model’s generated text. Research findings suggest that GPT-3’s language generation model significantly exacerbates existing social biases while replicating dangerous ideologies akin to white supremacy and hegemonic masculinity as factual knowledge. (shrink)