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Welcome to the nexus of ethics, psychology, morality, technology, health care, and philosophy
Showing posts with label Distributive Justice. Show all posts
Showing posts with label Distributive Justice. Show all posts

Saturday, December 30, 2023

The ethics of doing human enhancement ethics

Rueda, J. (2023). 
Futures, 153, 103236.

Abstract

Human enhancement is one of the leading research topics in contemporary applied ethics. Interestingly, the widespread attention to the ethical aspects of future enhancement applications has generated misgivings. Are researchers who spend their time investigating the ethics of futuristic human enhancement scenarios acting in an ethically suboptimal manner? Are the methods they use to analyze future technological developments appropriate? Are institutions wasting resources by funding such research? In this article, I address the ethics of doing human enhancement ethics focusing on two main concerns. The Methodological Problem refers to the question of how we should methodologically address the moral aspects of future enhancement applications. The Normative Problem refers to what is the normative justification for investigating and funding the research on the ethical aspects of future human enhancement. This article aims to give a satisfactory response to both meta-questions in order to ethically justify the inquiry into the ethical aspects of emerging enhancement technologies.

Highlights

• Formulates second-order problems neglected in the literature on the ethics of future enhancement technologies.

• Discusses speculative ethics and anticipatory ethics methodologies for analyzing emerging enhancement innovations.

• Evaluates the main objections to engaging in research into the ethical aspects of future scenarios of human enhancement.

• Shows that methodological and normative meta-questions are key to advance the ethical debate on human enhancement.

Friday, July 22, 2022

Neoliberalism and the Ideological Construction of Equity Beliefs

Goudarzi, S., Badaan, V., & Knowles, E. D. (2022). 
Perspectives on Psychological Science. 
https://doi.org/10.1177/17456916211053311

Abstract

Researchers across disciplines, including psychology, have sought to understand how people evaluate the fairness of resource distributions. Equity, defined as proportionality of rewards to merit, has dominated the conceptualization of distributive justice in psychology; some scholars have cast it as the primary basis on which distributive decisions are made. The present article acts as a corrective to this disproportionate emphasis on equity. Drawing on findings from different subfields, we argue that people possess a range of beliefs about how valued resources should be allocated—beliefs that vary systematically across developmental stages, relationship types, and societies. By reinvigorating notions of distributive justice put forth by the field’s pioneers, we further argue that prescriptive beliefs concerning resource allocation are ideological formations embedded in socioeconomic and historical contexts. Fairness beliefs at the micro level are thus shaped by those beliefs’ macro-level instantiations. In a novel investigation of this process, we consider neoliberalism, the globally dominant socioeconomic model of the past 40 years. Using data from more than 160 countries, we uncover evidence that neoliberal economic structures shape equity-based distributive beliefs at the individual level. We conclude by advocating an integrative approach to the study of distributive justice that bridges micro- and macro-level analyses.

From the Conclusions section

The extant literature in psychology conceptualizes neoliberalism as a belief system that can vary dispositionally and situationally (Beattie et al., 2019; Bettache & Chiu, 2019). Bay-Cheng and colleagues (2015) developed a Neoliberal Beliefs Inventory that taps into four subfacets of neoliberal thinking: System Inequality, conceptualized as views about the existence and the extent of inequality in society; Competition, which measures the extent to which one views competition as natural and beneficial; Personal Wherewithal, defined as attributing outcomes and success to personal dispositions such as hard work and merit; and Government Interference, which gauges the extent to which state intervention is seen to constrain personal freedom and endanger the meritocratic ideal. In another attempt, Grzanka and colleagues (2020) created the single-facet Anti-Neoliberal Attitudes Scale using items from existing inventories. Moreover, the endorsement of neoliberal policies has been shown to predict other orientations and belief systems that legitimize group and system inequalities (Azevedo et al., 2019). Becker (2021) examined the situational effect of neoliberal beliefs and found that exposure to neoliberal messages that prioritize freedom over justice and equality, individual success over public spirit, and distributions according to ability over need induced antielite sentiment and that this was mediated by feelings of threat, unfairness, and hopelessness.

Although the research described above is informative, from a cultural-psychological perspective, the notion of ideology also includes laws, policies, institutions, and practices embodying prescriptive and descriptive ideas about fair socioeconomic arrangements. Therefore, a sociocultural model of neoliberal ideology entails empirically investigating the dynamics of neoliberal belief systems (at an individual level) with neoliberal laws, institutions, and cultural practices and products, as in the present analysis. To our knowledge, the empirical analysis presented in this article is the first illustration within psychology and related fields of how neoliberal macro structures influence distributive preferences and beliefs.

Sunday, January 2, 2022

Towards a Theory of Justice for Artificial Intelligence

Iason Gabriel
Forthcoming in Daedelus vol. 151, 
no. 2, Spring 2022

Abstract 

This paper explores the relationship between artificial intelligence and principles of distributive justice. Drawing upon the political philosophy of John Rawls, it holds that the basic structure of society should be understood as a composite of socio-technical systems, and that the operation of these systems is increasingly shaped and influenced by AI. As a consequence, egalitarian norms of justice apply to the technology when it is deployed in these contexts. These norms entail that the relevant AI systems must meet a certain standard of public justification, support citizens rights, and promote substantively fair outcomes -- something that requires specific attention be paid to the impact they have on the worst-off members of society.

Here is the conclusion:

Second, the demand for public justification in the context of AI deployment may well extend beyond the basic structure. As Langdon Winner argues, when the impact of a technology is sufficiently great, this fact is, by itself, sufficient to generate a free-standing requirement that citizens be consulted and given an opportunity to influence decisions.  Absent such a right, citizens would cede too much control over the future to private actors – something that sits in tension with the idea that they are free and equal. Against this claim, it might be objected that it extends the domain of political justification too far – in a way that risks crowding out room for private experimentation, exploration, and the development of projects by citizens and organizations. However, the objection rests upon the mistaken view that autonomy is promoted by restricting the scope of justificatory practices to as narrow a subject matter as possible. In reality this is not the case: what matters for individual liberty is that practices that have the potential to interfere with this freedom are appropriately regulated so that infractions do not come about. Understood in this way, the demand for public justification stands in opposition not to personal freedom but to forms of unjust imposition.

The call for justice in the context of AI is well-founded. Looked at through the lens of distributive justice, key principles that govern the fair organization of our social, political and economic institutions, also apply to AI systems that are embedded in these practices. One major consequence of this is that liberal and egalitarian norms of justice apply to AI tools and services across a range of contexts. When they are integrated into society’s basic structure, these technologies should support citizens’ basic liberties, promote fair equality of opportunity, and provide the greatest benefit to those who are worst-off. Moreover, deployments of AI outside of the basic structure must still be compatible with the institutions and values that justice requires. There will always be valid reasons, therefore, to consider the relationship of technology to justice when it comes to the deployment of AI systems.

Monday, December 1, 2014

Legal Theory Lexicon: Justice

By Lawrence Solum
Legal Theory Blog
Originally published November 9, 2014

Introduction

The connection between law and justice is a deep one. We have "Halls of Justice," "Justices of the Supreme Court," and "the administration of justice." We know that "justice" is one of the central concepts of legal theory, but the concept of justice is also vague and ambiguous. This post provides an introductory roadmap to the the idea of justice.  Subsequent entries in the Legal Theory Lexicon will cover more particular aspects of this topic such as "distributive justice." As always, this post is aimed at law students (especially first-year law students) with an interest in legal theory.

The entire blog post is here.