Smartphone apps might offer a low-threshold approach to the detection of mental health conditions, such as depression. Based on the gathering of ‘passive data,’ some apps generate a user’s ‘digital phenotype,’ compare it to those of users with clinically confirmed depression and issue a warning if a depressive episode is likely. These apps can, thus, serve as epistemic tools for affected users. From an ethical perspective, it is crucial to consider epistemic injustice to promote socially responsible innovations within digital mental (...) healthcare. In cases of epistemic injustice, people are wronged specifically as epistemic agents, i.e., agents of the production and distribution of knowledge. We suggest that epistemic agency relies on different resource- and uptake-related preconditions which can be impacted by the functionality of passive self-tracking apps. We consider how this can lead to different forms of epistemic injustice (testimonial, hermeneutical, and contributory injustice) and analyze the influence of the apps’ use on epistemic practices on an individual level, in mental healthcare settings, and on the structural level. (shrink)

Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites of application, this paper (...) posits that reconceptualizing replicability can help bridge the gap. Through a shift from model performance replicability to claim replicability, Machine Learning scientists can be held accountable for producing non-replicable claims that are prone to eliciting harm due to misuse and misinterpretation. In this paper, I make the following contributions. First, I define and distinguish two forms of replicability for ML research that can aid constructive conversations around replicability. Second, I formulate an argument for claim-replicability's advantage over model performance replicability in justifying assigning accountability to Machine Learning scientists for producing non-replicable claims and show how it enacts a sense of responsibility that is actionable. In addition, I characterize the implementation of claim replicability as more of a social project than a technical one by discussing its competing epistemological principles, practical implications on Circulating Reference, Interpretative Labor, and research communication. (shrink)

Recent scholarship on technology-induced ‘conceptual disruption’ has spotlighted the notion of a conceptual gap. Conceptual gaps have also been discussed in scholarship on epistemic injustice, yet up until now these bodies of work have remained disconnected. This article shows that ‘gaps’ of interest to both bodies of literature are closely related, and argues that a joint examination of conceptual disruption and epistemic injustice is fruitful for both fields. I argue that hermeneutical marginalization—a skewed division of hermeneutical resources, which serves to (...) diminish the experiences of marginalized folk—does not only transpire because of conceptual gaps, but also because of two other kinds of conceptual disruption: conceptual overlaps and conceptual misalignments. Hence, there are multiple kinds of conceptual disruption that can be usefully studied through the normative lens of epistemic injustice. Technology can play different roles vis-a-vis epistemic injustices, both as a causal trigger of conceptual disruption, but also as a mediator of hermeneutical resources. Its role is normatively significant, in particular because socially disruptive technologies can have different epistemic implications for different groups: they may amplify the epistemic resources of some groups, while diminishing those of others. (shrink)

In my paper entitled ‘Testimonial injustice in medical machine learning’,1 I argued that machine learning (ML)-based Prediction Drug Monitoring Programmes (PDMPs) could infringe on patients’ epistemic and moral standing inflicting a testimonial injustice.2 I am very grateful for all the comments the paper received, some of which expand on it while others take a more critical view. This response addresses two objections raised to my consideration of ML-induced testimonial injustice in order to clarify the position taken in the paper. The (...) first maintains that my critical stance toward ML-based PDMPs idealises standard medical practice. Moreover, it claims that the ML-induced testimonial injustice I discuss is not substantially different from situations in which it emerges in human–human interactions. The second claims that my analysis does not establish a link to issues of automation bias, even if these are to be considered the core of testimonial injustice in ML. In the following, I address each objection in turn. Gillett3 argues that my critical stance towards using risk prediction tools such as PDMPs implies the idealisation of standard (ie, non-ML-mediated) modes of clinical practice. Considering certain uses of ML in a different setting, that is, psychiatry, the author goes as far as claiming that ‘traditional models of clinical practice in psychiatry are far from a utopia, free from epistemic injustice, which Pozzi’s argument risks proposing’. Since this statement does not represent what I intend to suggest, I am glad to have the possibility to clarify …. (shrink)

Artificial intelligence (AI) and machine learning (ML) systems increasingly purport to deliver knowledge about people and the world. Unfortunately, they also seem to frequently present results that repeat or magnify biased treatment of racial and other vulnerable minorities. This paper proposes that at least some of the problems with AI’s treatment of minorities can be captured by the concept of epistemic injustice. To substantiate this claim, I argue that (1) pretrial detention and physiognomic AI systems commit testimonial injustice because their (...) target variables reflect inaccurate and unjust proxies for what they claim to measure; (2) classification systems, such as facial recognition, commit hermeneutic injustice because their classification taxonomies, almost no matter how they are derived, reflect and perpetuate racial and other stereotypes; and (3) epistemic injustice better explains what is going wrong in these types of situations than does the more common focus on procedural (un)fairness. (shrink)

Emerging technologies can have profound conceptual implications. Their emergence frequently calls for the articulation of new concepts, or for modifications and novel applications of concepts that are already entrenched in communication and thought. In this paper, we introduce the notion of “conceptual appropriation” to capture the dynamics between concepts and emerging technologies. By conceptual appropriation, we mean the novel application of a value-laden concept to lay a contestable claim on an underdetermined phenomenon. We illustrate the dynamics of conceptual appropriation by (...) analyzing the concept NATURE and its uptake in three discourses of emerging technology: cellular agriculture, solar geo-engineering, and biomimicry. We argue that NATURE and its cognate NATURALNESS are strongly valanced concepts upon which different stakeholders lay a claim. In doing so, stakeholders advance distinct conceptions of nature, typically to suit their own interests. Our case-studies illustrate how in discourses on emerging technology, the application of value-concepts is entangled with ideological stakes and power dynamics. (shrink)