From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making.

Prediction algorithms are regularly used to support and automate high-stakes policy decisions about the allocation of scarce public resources. However, data-driven decision-making raises problems of algorithmic fairness and justice. So far, fairness and justice are frequently conflated, with the consequence that distributive justice concerns are not addressed explicitly. In this paper, we approach this issue by distinguishing (a) fairness as a property of the algorithm used for the prediction task from (b) justice as a property of the allocation principle used for the decision task in data-driven decision-making. The distinction highlights the different logic underlying concerns about fairness and justice and permits a more systematic investigation of the interrelations between the two concepts. We propose a new notion of algorithmic fairness called error fairness which requires prediction errors to not differ systematically across individuals. Drawing on sociological and philosophical discourse on local justice, we present a principled way to include distributive justice concerns into data-driven decision-making. We propose that allocation principles are just if they adhere to well-justified distributive justice principles. Moving beyond the one-sided focus on algorithmic fairness, we thereby make a first step toward the explicit implementation of distributive justice into data-driven decision-making.

Non-response in surveys of very old people.

Very old people are known to participate less often in social surveys than younger age-groups. However, survey participation among very old people in institutional settings is understudied. Additionally, the focus of the literature is on response rates, which neglects the complexity of the process of survey participation. The present study uses standard definitions of the American Association for Public Opinion Research to give a detailed description of survey participation among very old people, including those in institutional settings. Data come from a German survey on quality of life and subjective well-being of persons aged 80-84, 85-89, and 90+ (N = 1800). The present study (a) estimates contact, cooperation, response, and refusal rates and (b) identifies associations of age, sex, and type of residence with each of these rates. Weighted outcome rates for the survey were: contact = 66.0%, cooperation = 39.6%, response = 26.1%, and refusal = 26.9%. Age, sex, and type of residence were not associated with the contact, cooperation, and response rate. Lower refusal rates were found for people aged 90+, men, and institutionalized people. Additional analyses showed higher rates of non-interviews due to health-related reasons for institutionalized people and those aged 90+. Overall, results indicate that institutionalized and non-institutionalized people showed similar levels of survey participation. Willingness to participate is a key factor for women and people in private households, while the ability to participate is more important for institutionalized people.

Correction to: Non-response in surveys of very old people.

[This corrects the article DOI: 10.1007/s10433-018-0488-x.].