'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.Comment: 14 pages, 3 figures, ACM Conference on Human Factors in Computing Systems (CHI'18), April 21--26, Montreal, Canad

Characterization of neonatal opioid withdrawal syndrome in Arizona from 2010-2017.

In this paper, we describe a population of mothers who are opioid dependent at the time of giving birth and neonates exposed to opioids in utero who experience withdrawal following birth. While there have been studies of national trends in this population, there remains a gap in studies of regional trends. Using data from the Arizona Department of Health Services Hospital Discharge Database, this study aimed to characterize the population of neonates with neonatal opioid withdrawal syndrome (NOWS) and mothers who were opioid dependent at the time of giving birth, in Arizona. We analyzed approximately 1.2 million electronic medical records from the Arizona Department of Health Services Hospital Discharge Database to identify patterns and disparities across socioeconomic, ethnic, racial, and/or geographic groupings. In addition, we identified comorbid conditions that are differentially associated with NOWS in neonates or opioid dependence in mothers. Our analysis was designed to assess whether indicators such as race/ethnicity, insurance payer, marital status, and comorbidities are related to the use of opioids while pregnant. Our findings suggest that women and neonates who are non-Hispanic White and economically disadvantaged, tend be part of our populations of interest more frequently than expected. Additionally, women who are opioid dependent at the time of giving birth are unmarried more often than expected, and we suggest that marital status could be a proxy for support. Finally, we identified comorbidities associated with neonates who have NOWS and mothers who are opioid dependent not previously reported