Chun-Ping Yen

Jerry Fodor and Ernest Lepore (1999/2002) frame the debate over meaning holism in terms of a distinction between meaning atomism and meaning anatomism. The former holds that the meaning of an expression E is determined by some relation between E and some extra-linguistic entity. The latter holds that the meaning of E is at least partly determined by some of E’s “inward” relations (IRs) with other expressions in the very language. They (1992) argue that meaning anatomism inevitably collapses into meaning holism, which is the view that the meaning of E is determined by E’s IRs with every other expression in the very language because there is no principled distinction for the anatomist to divide the meaning-determining IRs from the non-meaning-determining ones. In response, the non-holistic anatomist urges that Fodor and Lepore’s no-principled-basis consideration is groundless because the lack of a generally accepted criterion for such a distinction does not undermine the viability of the distinction itself. While this point is well taken, I think that Fodor and Lepore are onto an important question here. That is, what does it make non-holistic anatomism distinctive from its holistic counterpart if without a principled basis for the distinction among IRs? I look into this question and give an alternative argument from Fodor and Lepore’s to suggest that non-holistic anatomism cannot bypass the no-principled-basis consideration. The non-holistic anatomist will need a principled distinction in kind between IRs to back her point.

Should you be targeted by police for a crime that AI predicts you will commit? In this paper, we analyse when, and to what extent, the person-based predictive policing (PP) — using AI technology to identify and handle individuals who are likely to breach the law — could be justifiably employed. We first examine PP’s epistemological limits, and then argue that these defects by no means refrain from its usage; they are worse in humans. Next, based on major AI ethics guidelines (IEEE, EU, and RIKEN, etc.), we refine three basic moral principles specific to person-based PP. We also derive further requirements from case studies, including debates in Chicago, New Orleans, San Francisco, Tokyo, and cities in China. Instead of rejecting PP programs, we analyse what necessary conditions should be met for using the tool to achieve social good. While acknowledging its risks, we conclude that the person-based PP could be beneficial in community policing, especially when merging into a larger governance framework of the social safety net.

This paper investigates the representation of authors with different linguistic backgrounds in academic publishing. We first review some common rebuttals of concerns about linguistic injustice. We then analyze 1039 authors of philosophy journals, primarily selected from the 2015 Leiter Report. While our data show that Anglophones dominate the output of philosophy papers, this unequal distribution cannot be solely attributed to language capacities. We also discover that ethics journals have more Anglophone authors than logic journals and that most authors are affiliated with English-speaking universities, suggesting other factors may also play significant roles. Moreover, some interesting results are revealed when we combine the factor of sex with place of affiliation and linguistic background. It indicates that while certain linguistic injustice is inevitable in academic publishing, it may be more complex than thought. We next introduce Broadbent’s :457–476, 2012, Philosophy of epidemiology, Palgrave Macmillan, New York, 2013, Stud Hist Philos Biol Biomed Sci 48:250–257, 2014) contrastive account of causation to give a causal explanation of our findings. Broadbent’s account not only well characterizes the multifaceted causality in academic publishing but also provides a methodological guideline for further investigation.

This paper examines racial discrimination and algorithmic bias in predictive policing algorithms (PPAs), an emerging technology designed to predict threats and suggest solutions in law enforcement. We first describe what discrimination is in a case study of Chicago’s PPA. We then explain their causes with Broadbent’s contrastive model of causation and causal diagrams. Based on the cognitive science literature, we also explain why fairness is not an objective truth discoverable in laboratories but has context-sensitive social meanings that need to be negotiated through democratic processes. With the above analysis, we next predict why some recommendations given in the bias reduction literature are not as effective as expected. Unlike the cliché highlighting equal participation for all stakeholders in predictive policing, we emphasize power structures to avoid hermeneutical lacunae. Finally, we aim to control PPA discrimination by proposing a governance solution—a framework of a social safety net.

Whereas using artificial intelligence (AI) to predict natural hazards is promising, applying a predictive policing algorithm (PPA) to predict human threats to others continues to be debated. Whereas PPAs were reported to be initially successful in Germany and Japan, the killing of Black Americans by police in the US has sparked a call to dismantle AI in law enforcement. However, although PPAs may statistically associate suspects with economically disadvantaged classes and ethnic minorities, the targeted groups they aim to protect are often vulnerable populations as well (e.g., victims of human trafficking, kidnapping, domestic violence, or drug abuse). Thus, determining how to enhance the benefits of PPA while reducing bias through better management is important. In this paper, we propose a policy schema to address this issue. First, after clarifying relevant concepts, we examine major criticisms of PPAs and argue that some of them should be addressed. If banning AI or making it taboo is an unrealistic solution, we must learn from our errors to improve AI. We next identify additional challenges of PPAs and offer recommendations from a policy viewpoint. We conclude that the employment of PPAs should be merged into broader governance of the social safety net and audited publicly by parliament and civic society so that the unjust social structure that breeds bias can be revised.