The Algorithm S Blind Spot
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Ethics of Algorithms
Algorithmic ethics is an attempt to specify moral-philosophical questions arising from the processes of change and transformation brought about by digitisation. As a scientific endeavour, its primary goal is to identify and justify ethical criteria and principles for sustainable value creation through the responsible use of data. The aim of this book is to contribute to the positioning and extensive development potential of an independent algorithmic ethics. The central task is the conceptual description of the problem of transparency and control in the context of digital technologies. This competence or empowerment problem is made accessible to an ethical approach in order to assess it and to highlight design perspectives.
Algorithms for Decision Making
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Algorithms and Ethics: Ensuring Fairness in the Future of Lending
Are you concerned about the fairness and equity of algorithmic lending? This insightful book, Algorithms and Ethics: Ensuring Fairness in the Future of Lending, explores the critical intersection of artificial intelligence, lending practices, and ethical considerations. Discover how algorithms are reshaping credit scoring, the potential for bias and discrimination embedded within these systems, and the disproportionate impact on underserved communities. You'll delve into the sources of algorithmic bias, examining data representation, design flaws, and the perpetuation of inequality through feedback loops. The book analyzes existing regulations, their limitations, and proposed reforms, while also presenting ethical frameworks for responsible algorithm design and deployment. Learn about Explainable AI (XAI) and its importance in promoting transparency, the crucial role of data privacy and security, and the necessity of human oversight in mitigating bias. Explore practical strategies for auditing and monitoring lending systems, promoting financial inclusion, and building public trust. Real-world case studies illuminate the challenges and successes in striving for a fairer financial system. This essential guide offers a path towards more equitable access to credit for all, making it a must-read for anyone involved in or affected by algorithmic lending.