Mitigating Label Bias via Decoupled Confident Learning

Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels in training data are correct. This is problematic because bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. In particular, human-generated labels are prone to encoding societal biases. While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem. We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias. After illustrating its performance on a synthetic dataset, we apply DeCoLe in the context of hate speech detection, where label bias has been recognized as an important challenge, and show that it successfully identifies biased labels and outperforms competing approaches.Comment: AI & HCI Workshop at the 40th International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA. 202

Algorithmic Fairness in Business Analytics: Directions for Research and Practice

The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or short-sighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA

More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias

An increased awareness concerning risks of algorithmic bias has driven a surge of efforts around bias mitigation strategies. A vast majority of the proposed approaches fall under one of two categories: (1) imposing algorithmic fairness constraints on predictive models, and (2) collecting additional training samples. Most recently and at the intersection of these two categories, methods that propose active learning under fairness constraints have been developed. However, proposed bias mitigation strategies typically overlook the bias presented in the observed labels. In this work, we study fairness considerations of active data collection strategies in the presence of label bias. We first present an overview of different types of label bias in the context of supervised learning systems. We then empirically show that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem. Our results illustrate the unintended consequences of deploying a model that attempts to mitigate a single type of bias while neglecting others, emphasizing the importance of explicitly differentiating between the types of bias that fairness-aware algorithms aim to address, and highlighting the risks of neglecting label bias during data collection

Arrendamiento Financiero : Calcular el costo anual del arrendamiento financiero de la panaderia Delicias Esther para adquisición de un horno de por medio del Banco La Fise en periodo 2014-2015

El presente trabajo de seminario de graduación, denominado “Calcular el costo anual del arrendamiento financiero de la panadería “Delicias Esther” para adquisición de un horno por medio del banco la FISE en el periodo 2014-2016 con el objetivo de demostrar que es viable para la toma de decisión en cuanto a comprar o arrendar. Para empezar con este estudio nos referimos a las generalidades de arrendamiento financiero, este comprende lo que son conceptos básicos, características y tipos de arrendamiento, mencionando las partes que intervienen en un contrato de arrendamiento financiero y su clasificación. Valoramos la importancia de los arrendamientos financieros de igual manera se presentan las ventajas y desventajas que tiene este tipo de financiamiento para los arrendatarios. Damos a conocer las formas de arrendamientos financieros, las obligaciones y derechos que tienen que cumplir los arrendatarios, también se centran en aspectos contables y la contabilización en términos teóricos. Seguidamente valoramos los métodos financieros para analizar la inversión y a través de un ejemplo se hacen la comparación de alternativas que son: financiamiento con opción de compra o la opción de arrendar. Concluimos con los métodos de evaluación financiera para determinar las alternativas más viables para la panadería utilizando el método de la tasa interna de retorno (TIR). Finalmente se explica los cálculos de los costos reales del arrendamiento para la adquisición de un horno digital por medio del banco lafise bancentro determinando a que plazo es más atractivo para invertir, concluyendo con la decisión de invertir con la alternativa de mayor rendimient

Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection

Algorithmic bias often arises as a result of differential subgroup validity, in which predictive relationships vary across groups. For example, in toxic language detection, comments targeting different demographic groups can vary markedly across groups. In such settings, trained models can be dominated by the relationships that best fit the majority group, leading to disparate performance. We propose framing toxicity detection as multi-task learning (MTL), allowing a model to specialize on the relationships that are relevant to each demographic group while also leveraging shared properties across groups. With toxicity detection, each task corresponds to identifying toxicity against a particular demographic group. However, traditional MTL requires labels for all tasks to be present for every data point. To address this, we propose Conditional MTL (CondMTL), wherein only training examples relevant to the given demographic group are considered by the loss function. This lets us learn group specific representations in each branch which are not cross contaminated by irrelevant labels. Results on synthetic and real data show that using CondMTL improves predictive recall over various baselines in general and for the minority demographic group in particular, while having similar overall accuracy