Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people's moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person's assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people's unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people's fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.Comment: To appear in the Proceedings of the Web Conference (WWW 2018). Code available at https://fate-computing.mpi-sws.org/procedural_fairness

Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe

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

CrossWalk: Fairness-enhanced Node Representation Learning

The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. Much recent work has focused on developing algorithmic tools to assess and mitigate such unfairness. However, there is little work on enhancing fairness in graph algorithms. Here, we develop a simple, effective and general method, CrossWalk, that enhances fairness of various graph algorithms, including influence maximization, link prediction and node classification, applied to node embeddings. CrossWalk is applicable to any random walk based node representation learning algorithm, such as DeepWalk and Node2Vec. The key idea is to bias random walks to cross group boundaries, by upweighting edges which (1) are closer to the groups' peripheries or (2) connect different groups in the network. CrossWalk pulls nodes that are near groups' peripheries towards their neighbors from other groups in the embedding space, while preserving the necessary structural properties of the graph. Extensive experiments show the effectiveness of our algorithm to enhance fairness in various graph algorithms, including influence maximization, link prediction and node classification in synthetic and real networks, with only a very small decrease in performance.Comment: Association for the Advancement of Artificial Intelligence (AAAI) 202

Modeling Coordinated vs. P2P Mining: An Analysis of Inefficiency and Inequality in Proof-of-Work Blockchains

We study efficiency in a proof-of-work blockchain with non-zero latencies, focusing in particular on the (inequality in) individual miners' efficiencies. Prior work attributed differences in miners' efficiencies mostly to attacks, but we pursue a different question: Can inequality in miners' efficiencies be explained by delays, even when all miners are honest? Traditionally, such efficiency-related questions were tackled only at the level of the overall system, and in a peer-to-peer (P2P) setting where miners directly connect to one another. Despite it being common today for miners to pool compute capacities in a mining pool managed by a centralized coordinator, efficiency in such a coordinated setting has barely been studied. In this paper, we propose a simple model of a proof-of-work blockchain with latencies for both the P2P and the coordinated settings. We derive a closed-form expression for the efficiency in the coordinated setting with an arbitrary number of miners and arbitrary latencies, both for the overall system and for each individual miner. We leverage this result to show that inequalities arise from variability in the delays, but that if all miners are equidistant from the coordinator, they have equal efficiency irrespective of their compute capacities. We then prove that, under a natural consistency condition, the overall system efficiency in the P2P setting is higher than that in the coordinated setting. Finally, we perform a simulation-based study to demonstrate that even in the P2P setting delays between miners introduce inequalities, and that there is a more complex interplay between delays and compute capacities

A REVIEW ON PALMYRA PALM (BORASSUS FLABELLIFER)

The medicinal plants have very important role in the health of human beings as well as animals. India is the largest producer of medicinal plants. One such plant, Borassus flabellifer L, belongs to family Arecaceae, commonly known as Palmyra palm is a native of tropical Africa but cultivated throughout India. Traditionally the different parts of the plant such as root, leaves, fruit, and seeds are used for various human disorders. Leaves are used for thatching, mats, baskets, fans. Flowers of B. flabellifer were investigated for analgesic and antipyretic effects, anti-inflammatory activity, haematological, biochemical parameters, and immunosuppressant property. The different parts of the plant are being used for medicinal properties like antihelminthic and diuretic. The fruit pulp of B. flabellifer has been used in traditional dishes and the sap, has been used as a sweetener for diabetic patients. Phytochemical studies of the plant revealed the presence of spirostane-type steroid saponins; steroidal glycoside also contains a bitter compound called flabelliferrins. Although investigations have been carried out a lot more can still be explored, exploited and utilized. The present review highlights the phytochemical and pharmacological studies including folklore medicinal uses of this plant

Assessment of climate change and vulnerability in Indian state of Telangana for better agricultural planning

Climate variability and change pose ever-growing challenges in the semiarid tropics, where majority of the population depend on climate-dependent activities such as agriculture. This has rendered these countries more vulnerable to climate change–induced variability. In spite of the uncertainties about anticipated magnitude of climate change on regional scale, an assessment of the possible changes in key climatic elements to identify most vulnerable locations becomes important for formulating adaptation strategies. This study compiles the existing knowledge about observed climate and projections of future change in Telangana state of India. The agriculture in this semiarid state has to adapt to changes in mean climate variables to increased variability with greater risk of extreme weather events, such as prolonged dry spells. Based on climatic vulnerability assessment, we found that the number of vulnerable mandals (currently 28%) will be increased to 45% during early century and to 59% by mid-century. As per the climate exposure index scores, Jogulamba-Gadwal district was found to be most sensitive. Overall, vulnerability index scores indicated that Adilabad, Nagarkurnool, Nalgonda, Peddapalli, Suryapet, Wanaparthy, and Yadadri are extremely vulnerable districts in the state. The ranking of vulnerable mandals in each district envisages the need for a holistic approach for each mandal or a group of mandals to reduce their sensitivity though implementation of site-specific adaptation strategies to minimize climate-related shocks not only in agriculture but also in other sectors

Modeling the potential impacts of climate change and adaptation strategies on groundnut production in India

Groundnut is one of the significant sources of oil, food, and fodder in India. It is grown in marginal arid and semiarid agro-ecosystems with wide yield fluctuations due to spatial variability of rainfall and soil. Climate change, which is predicted to increase the intra- and inter-annual rainfall variability will further constrain the groundnut economy in India besides the global and domestic economic, social and policy changes. Through this study we aim to examine the biophysical and social economic impacts of climate change on groundnut production and prices to provide a comprehensive analysis of how agriculture and the food system will be affected. Using projected climate data for India, we estimated the biophysical impacts of climate change on groundnut during mid-century using representative concentration pathway (RCP 8.5) scenario. We examined the impacts of changes in population and income besides environmental factors on groundnut productivity. This is to highlight the importance of holistic assessment of biophysical and socioeconomic factors to better understand climate change impacts. Modelled projections show that by 2050, climate change under an optimistic scenario will result in−2.3 to 43.2% change in groundnut yields across various regions in India when climate alone was factored in. But the change in groundnut yields ranged from −0.9% to 16.2% when economic (population and income) and market variables (elasticities, trade, etc.) were also considered. Similarly, under pessimistic climate change scenario, the percent change in groundnut yields would be −33.7 to 3.4 with only the climate factored in and −11.2 to 4.3 with the additional economic and market variables included. This indicates the sensitivity of climat

Family stress and satisfaction in college students: does parental divorce matter?

[Resumen] Esta investigación explora si el divorcio de los padres se relaciona con el estrés y la satisfacción familiares de sus hijos universitarios, así como con la calidad y la frecuencia de las relaciones padres-hijos. Participaron 147 alumnos de primer curso de las titulaciones de Educación Primaria, Educación Social, Filología, Sociología y Logopedia de la Universidade da Coruña. Los resultados indicaron que los estudiantes universitarios pertenecientes a familias en las que los padres están divorciados tienen un estrés significativamente mayor, una satisfacción familiar menor y una calidad y frecuencia de las relaciones familiares menor que los estudiantes universitarios pertenecientes a familias de padres no divorciados. Estos resultados podrían estar relacionados con los efectos a largo plazo del divorcio y ponen de manifiesto diferencias en las condiciones familiares de los alumnos cuyos padres están divorciados comparados con aquellos cuyos padres continúan casados