Download or read book Towards Equity in Algorithmic Decision Making written by William Cai and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, many high-stakes societal decision-making systems have begun incorporating data and algorithms. This trend raises the question of how decision makers can do so in a way which creates equitable systems which ameliorate inequities. This dissertation considers two broad paths forward towards this goal. First, we review a series of interventions at various stages of the model-building and deployment process. Specifically, we consider how a model-builder might selectively acquire additional information, adaptively sample training data, and add personalization. We show that these interventions allow for model-builders to efficiently allocate resources to create decision-making systems which are inclusive of individuals from vulnerable groups. Second, we review two pieces of work where modern, online data sources give insights which can inform improvements for existing systems. In particular, we first consider how telematics data, containing records on the true prevalence of speeding, sheds light on inequities in traffic enforcement. Then, we see how online game records provide valuable insight into how users make decisions within social networks. Findings from both studies can be incorporated in future design of or interventions in decision-making systems within both spaces. Overall, this dissertation demonstrates two concrete paths for moving towards equitable decision making: intervening to efficiently improve outcomes for underserved groups, and leveraging insights from modern data sources to improve societal decision making systems.

Download or read book Algorithmic Equity written by Osonde A. Osoba and published by . This book was released on 2019 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report is an examination of pathologies in social institutions' use of algorithmic decisionmaking processes. The primary focus is understanding how to evaluate the equitable use of algorithms across a range of specific applications.

Download or read book AI Ethics and Discrimination in Business written by Marco Marabelli and published by Springer Nature. This book was released on 2024-05-03 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a historical approach to explore data, algorithms, their use in practice through applications of AI in various settings, and all of the surrounding ethical and DEI implications. Summarizing our current knowledge and highlighting gaps, it offers original examples from empirical research in various settings, such as healthcare, social media, and the GIG economy. The author investigates how systems relying on a binary structure (machines) work in systems that are instead analogic (societies). Further, he examines how underrepresented populations, who have been historically penalized by technologies, can play an active role in the design of automated systems, with a specific focus on the US legal and social system. One issue is that main tasks of machines concern classification, which, while efficient for speeding up decision-making processes, are inherently biased. Ultimately, this work advocates for ethical design and responsible implementation and deployment of technology in organizations and society through through government-sponsored social justice, in contrast with free market policies. This interdisciplinary text contributes to the timely and relevant debate on algorithmic fairness, biases, and potential discriminations. It will appeal to researchers in business ethics and information systems while building on theories from anthropology, psychology, sociology, management, marketing, and economics. Marco Marabelli is a Professor of Computer Information Systems at Bentley University, USA. His research focuses on the ethical and DEI implications of the use of emerging technologies in organizations and society and on the historical and legal aspects concerning social injustice associated with the use of artificial intelligence.

Download or read book Standards for the Control of Algorithmic Bias written by Natalie Heisler and published by CRC Press. This book was released on 2023-07-04 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Governments around the world use machine learning in automated decision-making systems for a broad range of functions. However, algorithmic bias in machine learning can result in automated decisions that produce disparate impact and may compromise Charter guarantees of substantive equality. This book seeks to answer the question: what standards should be applied to machine learning to mitigate disparate impact in government use of automated decision-making? The regulatory landscape for automated decision-making, in Canada and across the world, is far from settled. Legislative and policy models are emerging, and the role of standards is evolving to support regulatory objectives. While acknowledging the contributions of leading standards development organizations, the authors argue that the rationale for standards must come from the law and that implementing such standards would help to reduce future complaints by, and would proactively enable human rights protections for, those subject to automated decision-making. The book presents a proposed standards framework for automated decision-making and provides recommendations for its implementation in the context of the government of Canada’s Directive on Automated Decision-Making. As such, this book can assist public agencies around the world in developing and deploying automated decision-making systems equitably as well as being of interest to businesses that utilize automated decision-making processes.

Download or read book A Human s Guide to Machine Intelligence written by Kartik Hosanagar and published by Penguin. This book was released on 2020-03-10 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence.

Download or read book Using Algorithms to Tame Discrimination written by Deven R. Desai and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies that try to address inequality in employment face a paradox. Failing to address disparities regarding protected classes in a company's workforce can result in legal sanctions; but proactive actions to address and avoid such disparities can also face legal scrutiny and sanctions too. After the summer of 2020, companies such as Microsoft announced large programs to address inequity in employment. They soon received letters from the Labor Department's Office of Federal Contract Compliance Programs (OFCCP) because of the OFCCP's concern that the plans will end up discriminating based on race. At the same time, the OFCCP announced a settlement with Microsoft on September 19, 2020, for $3 million back pay and interest to address hiring disparities “against Asian applicants” for several positions from December 2015 to November 2018. These examples are not isolated and are likely to persist. Any company seeking to identify talent will likely use data and algorithms to screen and hire employees. That practice will again raise the tension of how to increase diversity without running into problems of embedded inequity and making decisions that are prohibited because they are based on protected class status. We offer a potential path forward to solve this paradox by exploring current advances in Computer Science and Operations Research. By carefully acknowledging uncertainties in candidates' data (using the framework of partially ordered sets), a hiring entity can improve equal opportunity practices. The solution is to embed error-mitigation due to uncertainties or biases in an algorithmic decision-making process without crossing into illegal discriminatory practices (e.g., without enforcing quotas). In short, this work explains a way to design fair screening methods that account for biases and uncertainties in data and abide by anti-discrimination law.

Download or read book The Ethical Algorithm written by Michael Kearns and published by Oxford University Press. This book was released on 2019-10-04 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps. Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

Download or read book An Intelligence in Our Image written by Osonde A. Osoba and published by Rand Corporation. This book was released on 2017-04-05 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning algorithms and artificial intelligence influence many aspects of life today. This report identifies some of their shortcomings and associated policy risks and examines some approaches for combating these problems.

Download or read book Algorithms of Oppression written by Safiya Umoja Noble and published by NYU Press. This book was released on 2018-02-20 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author

Download or read book Algorithmic Decision Theory written by Francesca Rossi and published by Springer. This book was released on 2009-10-13 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the papers presented at ADT 2009, the first International Conference on Algorithmic Decision Theory. The conference was held in San Servolo, a small island of the Venice lagoon, during October 20-23, 2009. The program of the conference included oral presentations, posters, invited talks, and tutorials. The conference received 65 submissions of which 39 papers were accepted (9 papers were posters). The topics of these papers range from computational social choice preference modeling, from uncertainty to preference learning, from multi-criteria decision making to game theory.

Download or read book Big Data and Social Science written by Ian Foster and published by CRC Press. This book was released on 2016-08-10 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

Download or read book Algorithmic Regulation written by Karen Yeung and published by . This book was released on 2019-09-05 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the power and sophistication of of "big data" and predictive analytics has continued to expand, so too has policy and public concern about the use of algorithms in contemporary life. This is hardly surprising given our increasing reliance on algorithms in daily life, touching policy sectorsfrom healthcare, transport, finance, consumer retail, manufacturing education, and employment through to public service provision and the operation of the criminal justice system. This has prompted concerns about the need and importance of holding algorithmic power to account, yet it is far fromclear that existing legal and other oversight mechanisms are up to the task.This collection of essays, edited by two leading regulatory governance scholars, offers a critical exploration of "algorithmic regulation", understood both as a means for co-ordinating and regulating social action and decision-making, as well as the need for institutional mechanisms through whichthe power of algorithms and algorithmic systems might themselves be regulated. It offers a unique perspective that is likely to become a significant reference point for the ever-growing debates about the power of algorithms in daily life in the worlds of research, policy and practice. The range ofcontributors are drawn from a broad range of disciplinary perspectives including law, public administration, applied philosophy, data science and artificial intelligence. Taken together, they highlight the rise of algorithmic power, the potential benefits and risks associated with this power, theway in which Sheila Jasanoff's long-standing claim that "technology is politics" has been thrown into sharp relief by the speed and scale at which algorithmic systems are proliferating, and the urgent need for wider public debate and engagement of their underlying values and value trade-offs, theway in which they affect individual and collective decision-making and action, and effective and legitimate mechanisms by and through which algorithmic power is held to account.

Download or read book Practical Fairness written by Aileen Nielsen and published by "O'Reilly Media, Inc.". This book was released on 2020-12-01 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms. There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.

Download or read book Fair and Unbiased Algorithmic Decision Making written by Songül Tolan and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that 'objective' machines base their decisions solely on facts and remain unaffected by human cognitive biases, discriminatory tendencies or emotions. Yet, there is overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are based on data that contains biased human decisions. This has led to a call for fairness-aware machine learning. However, fairness is a complex concept which is also reflected in the attempts to formalize fairness for algorithmic decision making. Statistical formalizations of fairness lead to a long list of criteria that are each flawed (or harmful even) in different contexts. Moreover, inherent tradeoffs in these criteria make it impossible to unify them in one general framework. Thus, fairness constraints in algorithms have to be specific to the domains to which the algorithms are applied. In the future, research in algorithmic decision making systems should be aware of data and developer biases and add a focus on transparency to facilitate regular fairness audits.

Download or read book The Algorithmic Odyssey A Comprehensive Guide to AI Research written by Dr. Prakash Arumugam and published by Inkbound Publishers. This book was released on 2021-02-10 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embark on an extraordinary journey through the cutting-edge world of artificial intelligence with The Algorithmic Odyssey. This comprehensive guide serves as both a map and a compass for navigating the complex and rapidly evolving landscape of AI research. From the foundational principles of machine learning to the latest advancements in neural networks, this book offers a detailed exploration of the algorithms that are reshaping our world. Whether you are a seasoned researcher, a curious student, or a tech enthusiast, The Algorithmic Odyssey provides invaluable insights into the methodologies, challenges, and breakthroughs that define contemporary AI research. Discover the intricacies of supervised and unsupervised learning, delve into the depths of deep learning, and understand the transformative impact of reinforcement learning. Each chapter is meticulously crafted to offer clear explanations, practical examples, and thought-provoking discussions, making complex concepts accessible without sacrificing depth. Beyond the technicalities, The Algorithmic Odyssey also addresses the ethical, societal, and philosophical implications of AI. What does it mean to create intelligent systems? How do we ensure that these technologies benefit humanity? These questions and more are explored with rigor and sensitivity, encouraging readers to think critically about the future of AI. With contributions from leading experts in the field and a wealth of resources for further study, The Algorithmic Odyssey is an essential addition to the library of anyone passionate about the future of technology and its impact on our world. Join us on this odyssey and unlock the mysteries of artificial intelligence.

Download or read book Algorithmic Institutionalism written by Ricardo Fabrino Mendonca and published by Oxford University Press. This book was released on 2023-11-14 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic Institutionalism is the first book to conceive algorithms as institutions in contemporary societies, focusing on different dimensions of how they structure decision-making and enact power relations. In many situations in contemporary societies, algorithms structure social interactions, resulting in patterns of action and human behavior in collective contexts. Almeida, Filgueiras, and Mendonca discuss how algorithms are gradually occupying an institutional space in societies, deciding on different aspects of social life and shaping collective and individual human behaviors. As institutions, algorithms work as decision systems that define what is allowed, hindered, facilitated, or made impossible as well as positions within society's organizational structures. Algorithmic institutionalism uses the perspective of institutional theories to explain the functioning of these decision systems and how they establish patterns and norms that affect human behavior and lead to deep changes in contemporary society. The book points to the challenges of political orders that are gradually institutionalized with algorithms, comprising new dynamics of interaction between humans and machines. These disruptive dynamics of interaction between humans and machines create new challenges related to the democratization of algorithms and the impasses that emerge with technological advancement through digital technologies. Providing an analytical framework for an adequate comprehension of the social and political implications of algorithmic systems, Algorithmic institutionalism applies this framework to make sense of recommendation systems, the platformization of governments, and the deployment of algorithms in security. It then addresses the challenge of developing approaches to democratize the new political order influenced by the global expansion of algorithmic decision-making, pointing to key democratic values that are relevant once we consider the construction of legitimate decisions in contemporary societies.

Download or read book Standards for Control of Algorithmic Bias written by Maura R. Grossman and published by . This book was released on 2023-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Governments around the world use machine learning in automated decision-making systems for a broad range of functions, however algorithmic bias in machine learning can result in automated decisions that produce disparate impact and may compromise Charter guarantees of substantive equality. This book seeks to answer the question: what standards should be applied to machine learning to mitigate disparate impact in automated decision-making? The regulatory landscape for automated decision-making, in Canada and across the world, is far from settled. Legislative and policy models are emerging, and the role of standards is evolving to support regulatory objectives. While acknowledging the contributions of leading standards development organizations, the authors argue that the rationale for standards must come from the law, and that implementing such standards would help not only to reduce future complaints, but more importantly would proactively enable human rights protections for those subject to automated decision-making. The book presents a proposed standards framework for automated decision-making and also provides recommendations for implementation in the context of Canada's Directive on Automated Decision-Making. As such, this book can assist public agencies around the world in deploying and developing automated decision-making equitably, as well as being of interest to businesses that utilize Automated Decision-Making processes"--