From Fair Decision Making to Social Equality

The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality between the qualifications of the groups. How can we incorporate influence dynamics in decision making? How well do dynamics-oblivious fairness policies fare in terms of reaching equality? In this paper, we propose a simple yet revealing model that encompasses (1) a selection process where an institution chooses from multiple groups according to their qualifications so as to maximize an institutional utility and (2) dynamics that govern the evolution of the groups' qualifications according to the imposed policies. We focus on demographic parity as the formalism of affirmative action. We then give conditions under which an unconstrained policy reaches equality on its own. In this case, surprisingly, imposing demographic parity may break equality. When it doesn't, one would expect the additional constraint to reduce utility, however, we show that utility may in fact increase. In more realistic scenarios, unconstrained policies do not lead to equality. In such cases, we show that although imposing demographic parity may remedy it, there is a danger that groups settle at a worse set of qualifications. As a silver lining, we also identify when the constraint not only leads to equality, but also improves all groups. This gives quantifiable insight into both sides of the mismatch hypothesis. These cases and trade-offs are instrumental in determining when and how imposing demographic parity can be beneficial in selection processes, both for the institution and for society on the long run.Comment: Short version appears in the proceedings of ACM FAT* 201

From Parity to Preference-based Notions of Fairness in Classification

The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. However, the existing notions of fairness, based on parity (equality) in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy. In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and propose preference-based notions of fairness -- given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the (dis)parity as compared to the other groups. Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness. Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness

Menciptakan Rezim Perdagangan Bebas yang Adil: Sebuah Pendekatan Teori Keadilan

This article purposes to knowthe free trade regime from the perspective of Rawls's justice and create a free trade regime of the approach Rawls's theory of justice. This article argues that free trade must be based on a theory of justice by John Rawls (justice as fairness). the principle of Rawls If equality and freedom not reached when there is still inequality in social and economic conditions, it is necessary preferentialthe disadvantaged. Results of analysis showed that free trade regime in the provisions of the WTO has not yet adopted a principles of justice by John Rawls. An attempt to create a fair free trade regime that provides a special and different treatment to the developing countries by providing a greater advantage. Furthermore the institutional reform in the decision-making procedures more fair

Role of Savings and Credit Cooperative Societies in Promoting Gender Equality in Agarfa District, Ethiopia

When the issues of cooperatives are raised participation based on gender equality is unquestionable to make the development of cooperatives fruit full. The study was aimed at assessing the role of savings and credit cooperatives in promoting gender equality and identifying the influencing constraints in the study area, Agarfa district, Ethiopia. A multi stage sampling technique was employed to select 120 (60 women and 60 men) sample respondents from four Savings and credit cooperative societies of the district and then interviewed using pre-tested semi structured interview schedule. Both primary and secondary data were collected and analysed to understand various roles of savings and credit. Qualitative data were used to supplement quantitative data. Data were analysed using descriptive and inferential statistics. The economic roles of the cooperatives in promoting gender equality were fair in some areas except in saving amount and credit term. In the social roles of the cooperatives, they were not promoting gender equality in the areas of cooperatives awareness creation, cooperatives training and cooperatives relations, except in community service. In the decision making practices of the cooperatives there was gender equality only in the general assembly meeting and gender inequality was there in others. The gender mainstreaming practices were seen in the cooperatives, but they were not properly implemented Cultural barriers, lack of awareness on cooperative ideologies and gender disparity in implementing the mainstreamed plans were the major constraints of promoting gender equality. The implications of the research show that cooperatives working intensively on awareness creation, training and education programs help to strengthen women's capacities and capabilities. Cooperatives should review their policies and plan periodically to ensure that they are gender sensitive and cooperatives need to focus on gender issues in their action plans. Keywords: cooperatives, gender equality, gender participation, savings and credi

Why Health-Related Inequalities Matter and Which Ones Do

I outline and defend two egalitarian theories, which yield distinctive and, I argue, complementary answers to why health-related inequalities matter: a brute luck egalitarian view, according to which inequalities due to unchosen, differential luck are bad because unfair, and a social egalitarian view, according to which inequalities are bad when and because they undermine people’s status as equal citizens. These views identify different objects of egalitarian concern: the brute luck egalitarian view directs attention to health-related well-being, while social egalitarianism focuses on health-related capabilities that are central to a person’s status as a citizen. I argue that both views are correct and should jointly guide priority-setting in health

Cosmopolitan Justice and Rightful Enforceability

The liberal debate on global justice has long been polarized between cosmopolitans, who champion global equality, and statists, who defend global sufficiency. Interestingly, little attention has been given to what these outlooks have in common: a focus on justice. Justice differs from other types of values in that it sets out rightfully enforceable entitlements. Once this is appreciated, however, cosmopolitanism and statism can be shown to offer inadequate accounts of global justice. Since the principles they advocate are reasonably contested, directly enforcing them on dissenting others would violate the liberal commitment to equal respect for persons. When the demands of justice are reasonably disagreed upon, as they are at the global level, conflicts over them need to be procedurally adjudicated. The chapter concludes that taking the enforceability of justice seriously leads us to advocate global outcome sufficiency, and global procedural equality, thereby steering a middle course between statism and cosmopolitanism

The invisible power of fairness. How machine learning shapes democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.Comment: 12 pages, 1 figure, preprint version, submitted to The 32nd Canadian Conference on Artificial Intelligence that will take place in Kingston, Ontario, May 28 to May 31, 201