How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness

What is the best way to define algorithmic fairness? While many definitions of fairness have been proposed in the computer science literature, there is no clear agreement over a particular definition. In this work, we investigate ordinary people's perceptions of three of these fairness definitions. Across two online experiments, we test which definitions people perceive to be the fairest in the context of loan decisions, and whether fairness perceptions change with the addition of sensitive information (i.e., race of the loan applicants). Overall, one definition (calibrated fairness) tends to be more preferred than the others, and the results also provide support for the principle of affirmative action.Comment: To appear at AI Ethics and Society (AIES) 201

Preventing Discriminatory Decision-making in Evolving Data Streams

Bias in machine learning has rightly received significant attention over the last decade. However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting. Despite the wide prevalence of online systems in the real world, work on identifying and correcting bias in the online setting is severely lacking. The unique challenges of the online environment make addressing bias more difficult than in the offline setting. First, Streaming Machine Learning (SML) algorithms must deal with the constantly evolving real-time data stream. Second, they need to adapt to changing data distributions (concept drift) to make accurate predictions on new incoming data. Adding fairness constraints to this already complicated task is not straightforward. In this work, we focus on the challenges of achieving fairness in biased data streams while accounting for the presence of concept drift, accessing one sample at a time. We present Fair Sampling over Stream ($FS^2$), a novel fair rebalancing approach capable of being integrated with SML classification algorithms. Furthermore, we devise the first unified performance-fairness metric, Fairness Bonded Utility (FBU), to evaluate and compare the trade-off between performance and fairness of different bias mitigation methods efficiently. FBU simplifies the comparison of fairness-performance trade-offs of multiple techniques through one unified and intuitive evaluation, allowing model designers to easily choose a technique. Overall, extensive evaluations show our measures surpass those of other fair online techniques previously reported in the literature

Study of psychiatric morbidity among residents of government old age homes in Delhi

Context: The increased demand on long-term old age care homes in urban India is a result of demographic transition together with the disintegration of joint family system and changing social values which make them increasingly vulnerable to mental health problems. Aims: This study attempted to find out an array of mental health problems and associated morbidity among inhabitant of government old age homes. Settings and Design: This was a cross-sectional study which included government run old age homes (OAHs) in Delhi. Subjects and Methods: The sample comprised a total of 148 elderly in four OAHs with a mean age of 72.81 years. The World Health Organization Quality of Life-BREF Scale (QOL), Mini-Mental State Examination, Geriatric Depression Scale, Hamilton Anxiety Rating Scale, Brief Psychiatric Rating Scale, and Kesseler-10 Scale were administered. Statistical Analysis: Data were analyzed through SPSS version 20.0 version. Frequency distribution and cross-tabulation used to create summary tables and compare items. Results: Female constituted two-third of study population whereas one-third of subjects were illiterate and two-third without income. The study demonstrated psychiatric morbidity profile among OAH inhabitants and exhibited mild-moderate anxiety symptoms in almost 95% followed by mild-severe depression reported by 85%, mild-moderate psychotic illnesses, psychological distress, cognitive impairments, and poor QOL. Low income and education, low social connections and loss of spouse were key risk factors. Conclusions and Recommendation: Psychiatric morbidity profile and QOL among OAH residents is influenced by various psychological, social, and economic factors. This emphasized the need for better management of the government-run OAHs to ensure better overall mental health of the residents