Fairness-enhancing interventions in stream classification

The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to "fix" a (fair) model over the data stream as changes in the data might incur changes in the underlying model therefore, affecting its fairness. In this work, we propose fairness-enhancing interventions that modify the input data so that the outcome of any stream classifier applied to that data will be fair. Experiments on real and synthetic data show that our approach achieves good predictive performance and low discrimination scores over the course of the stream.Comment: 15 pages, 7 figures. To appear in the proceedings of 30th International Conference on Database and Expert Systems Applications, Linz, Austria August 26 - 29, 201

Fair-CMNB: Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization

Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, etc. This calls for handling massive amounts of incoming information with minimal response delay while ensuring fair and high-quality decisions. Although deep learning has achieved success in various domains, its computational complexity may hinder real-time processing, making traditional algorithms more suitable. In this context, we propose a novel adaptation of Naïve Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance through multi-objective optimization (MOO). Class imbalance is an inherent problem in discrimination-aware learning paradigms. To deal with class imbalance, we propose a dynamic instance weighting module that gives more importance to new instances and less importance to obsolete instances based on their membership in a minority or majority class. We have conducted experiments on a range of streaming and static datasets and concluded that our proposed methodology outperforms existing state-of-the-art (SoTA) fairness-aware methods in terms of both discrimination score and balanced accuracy

Floodplain management in temperate regions : is multifunctionality enhancing biodiversity?

Background: Floodplains are among the most diverse, dynamic, productive and populated but also the most threatened ecosystems on Earth. Threats are mainly related to human activities that alter the landscape and disrupt fluvial processes to obtain benefits related to multiple ecosystem services (ESS). Floodplain management therefore requires close coordination among interest groups with competing claims and poses multi-dimensional challenges to policy-makers and project managers. The European Commission proposed in its recent Biodiversity Strategy to maintain and enhance European ecosystems and their services by establishing green infrastructure (GI). GI is assumed to provide multiple ecosystem functions and services including the conservation of biodiversity in the same spatial area. However, evidence for biodiversity benefits of multifunctional floodplain management is scattered and has not been synthesised. Methods/design: This protocol specifies the methods for conducting a systematic review to answer the following policy-relevant questions: a) what is the impact of floodplain management measures on biodiversity; b) how does the impact vary according to the level of multifunctionality of the measures; c) is there a difference in the biodiversity impact of floodplain management across taxa; d) what is the effect of the time since implementation on the impact of the most important measures; and e) are there any other factors that significantly modify the biodiversity impact of floodplain management measures? Within this systematic review we will assess multifunctionality in terms of ESS that are affected by an implemented intervention. Biodiversity indicators included in this systematic review will be related to the diversity, richness and abundance of species, other taxa or functional groups. We will consider if organisms are typical for and native to natural floodplain ecosystems. Specific inclusion criteria have been developed and the wide range of quality of primary literature will be evaluated with a tailor-made system for assessing susceptibility to bias and the reliability of the studies. The review is intended to bridge the science-policy interface and will provide a useful synthesis of knowledge for decision-makers at all governance levels

Semi-supervised learning and fairness-aware learning under class imbalance

With the advent of Web 2.0 and the rapid technological advances, there is a plethora of data in every field; however, more data does not necessarily imply more information, rather the quality of data (veracity aspect) plays a key role. Data quality is a major issue, since machine learning algorithms are solely based on historical data to derive novel hypotheses. Data may contain noise, outliers, missing values and/or class labels, and skewed data distributions. The latter case, the so-called class-imbalance problem, is quite old and still affects dramatically machine learning algorithms. Class-imbalance causes classification models to learn effectively one particular class (majority) while ignoring other classes (minority). In extend to this issue, machine learning models that are applied in domains of high societal impact have become biased towards groups of people or individuals who are not well represented within the data. Direct and indirect discriminatory behavior is prohibited by international laws; thus, there is an urgency of mitigating discriminatory outcomes from machine learning algorithms. In this thesis, we address the aforementioned issues and propose methods that tackle class imbalance, and mitigate discriminatory outcomes in machine learning algorithms. As part of this thesis, we make the following contributions: • Tackling class-imbalance in semi-supervised learning – The class-imbalance problem is very often encountered in classification. There is a variety of methods that tackle this problem; however, there is a lack of methods that deal with class-imbalance in the semi-supervised learning. We address this problem by employing data augmentation in semi-supervised learning process in order to equalize class distributions. We show that semi-supervised learning coupled with data augmentation methods can overcome class-imbalance propagation and significantly outperform the standard semi-supervised annotation process. • Mitigating unfairness in supervised models – Fairness in supervised learning has received a lot of attention over the last years. A growing body of pre-, in- and postprocessing approaches has been proposed to mitigate algorithmic bias; however, these methods consider error rate as the performance measure of the machine learning algorithm, which causes high error rates on the under-represented class. To deal with this problem, we propose approaches that operate in pre-, in- and post-processing layers while accounting for all classes. Our proposed methods outperform state-of-the-art methods in terms of performance while being able to mitigate unfair outcomes

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

The Cost of Fairness in AI: Evidence from E-Commerce

Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness” have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness

Character Strengths and Academic Achievements of Undergraduate College Students of Guwahati, Assam

Character strengths, as conceptualised by the Values-In-Action (VIA) strengths classification system, are core characteristics of individuals that allow people to be virtuous (Seligman 2002). They are moral, intrinsically valuable, and ubiquitous traits that can be developed and enhanced. Social psychologists and sociologists consider achievements in college or university level, because of recognition and proper utilisation of the character strengths possessed by the individual students. The current study was conducted amongst 240 undergraduate college students of arts stream (60 males and 60 females) and science stream (60 males and 60 females) falling within the age group of 18-21 years, with the aim of finding out if the character strengths of the male and female undergraduate students are associated with their college academic achievements. It was found that significant correlation existed between appreciation of beauty and excellence, fairness, forgiveness, honesty, humour, kindness, love of learning and humility with the academic achievement of the students