Machine learning methods based on AdaBoost have been widely applied to various classification problems across many mission-critical applications including healthcare, law and finance. However, there is a growing concern about the unfairness and discrimination of data-driven classification models, which is inevitable for classical algorithms including AdaBoost. In order to achieve fair classification, a novel fair AdaBoost (FAB) approach is proposed that is an interpretable fairness-improving variant of AdaBoost. We mainly investigate binary classification problems and focus on the fairness of three different indicators (i.e., accuracy, false positive rate and false negative rate). By utilizing a fairness-aware reweighting technique for base classifiers, the proposed FAB approach can achieve fair classification while maintaining the advantage of AdaBoost with negligible sacrifice of predictive performance. In addition, a hyperparameter is introduced in FAB to show preferences for the fairness-accuracy trade-off. An upper bound for the target loss function that quantifies error rate and unfairness is theoretically derived for FAB, which provides a strict theoretical support for the fairness-improving methods designed for AdaBoost. The effectiveness of the proposed method is demonstrated on three real-world datasets (i.e., Adult, COMPAS and HSLS) with respect to the three fairness indicators. The results are accordant with theoretic analyses, and show that (i) FAB significantly improves classification fairness at a small cost of accuracy compared with AdaBoost; and (ii) FAB outperforms state-of-the-art fair classification methods including equalized odds method, exponentiated gradient method, and disparate mistreatment method in terms of the fairness-accuracy trade-off.

Posted by Catherina Xu and Tulsee Doshi, Product Managers, Google Research While industry and academia continue to explore the benefits ...

Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can spark major concerns when they exhibit vast performance discrepancies between people from various demographic and social backgrounds. Systematic diagnosis of fairness, harms, and biases of computer vision systems is an important step towards building socially responsible systems. To initiate an effort towards standardized fairness audits, we propose three fairness indicators, which aim at quantifying harms and biases of visual systems. Our indicators use existing publicly available datasets collected for fairness evaluations, and focus on three main types of harms and bias identified in the literature, namely harmful label associations, disparity in learned representations of social and demographic traits, and biased performance on geographically diverse images from across the world.We define precise experimental protocols applicable to a wide range of computer vision models. These indicators are part of an ever-evolving suite of fairness probes and are not intended to be a substitute for a thorough analysis of the broader impact of the new computer vision technologies. Yet, we believe it is a necessary first step towards (1) facilitating the widespread adoption and mandate of the fairness assessments in computer vision research, and (2) tracking progress towards building socially responsible models. To study the practical effectiveness and broad applicability of our proposed indicators to any visual system, we apply them to off-the-shelf models built using widely adopted model training paradigms which vary in their ability to whether they can predict labels on a given image or only produce the embeddings. We also systematically study the effect of data domain and model size.

"We want to use AI to augment the abilities of people, to enable us to accomplish more and to allow us to spend more time on our creative endeavors." Calling Google just a search giant would be an understatement with how quickly it grew from a mere search engine to a driving force behind innovations in several key IT sectors. Over the past couple of years, Google has planted its roots into almost everything digital, be it consumer electronics such as smartphones, tablets, laptops, its underlying software such as Android and Chrome OS or the smart software backed by Google's AI. Google has been actively innovating in the smart software industry. Backed by its expertise in search and analytical data acquired over the years have helped Google create various tools like TensorFlow, ML Kit, Cloud AI, and many more for enthusiasts and beginners alike who are trying to understand the capabilities of AI.

The developers of AI systems have entered a phase where tweaking algorithms and pumping up accuracy will do no good. Questions such as fairness and privacy are more important now than ever. But, an organisation cannot afford or expect a machine learning engineer to develop tools from scratch that can cater to the different demands at different stages of building a pipeline. Google is now offering a one-stop solution to all these challenges through its TensorFlow community. The team at TensorFlow have built tools to assist and overcome the errors that surface in data collection, processing, loading and deployment.

Ethics is one of the disciplines that must accompany the evolution of artificial intelligence(AI) systems. Building AI agents that provide ethical outcomes is one of the foundational challenges of the next decade of machine learning systems. Among the different aspects of ethical systems, fairness is one that deserves particular attention. The idea of a fair machine learning model is one whose outcomes don't favor any particular group based on a specific bias. Conceptually, the idea of fair machine learning systems seems incredibly intuitive but how can we materialize it technically.

Human societies are extremely complex. The cultural, racial and geographical differences around the globe and the lack of curated data make'fairness' in technology a huge challenge. Now, in an attempt to track the long term societal impacts of artificial intelligence, Google researchers recently released a machine learning fairness gym. They have done this by using Google's OpenAI Gym. OpenAI's Gym is a toolkit for developing and comparing reinforcement learning algorithms and is compatible with any numerical computation library, such as TensorFlow or Theano.

Fairness Indicators is designed to support teams in evaluating and improving models for fairness concerns in partnership with the broader Tensorflow toolkit. The tool is currently actively used internally by many of our products, and is now available in BETA to try for your own use cases. Fairness Indicators enables easy computation of commonly-identified fairness metrics for binary and multiclass classifiers. At Google, it is important for us to have tools that can work on billion-user systems. Fairness Indicators will allow you to evaluate across any size of use case.