Back to top

Reproductive Politics and the Making of Modern India

Beginning in the late nineteenth century, India played a pivotal role in global conversations about population and reproduction. In Reproductive Politics and the Making of Modern India, Mytheli Sreenivas demonstrates how colonial administrators, postcolonial development experts, nationalists, eugenicists, feminists, and family planners all aimed to reform reproduction to transform both individual bodies and the body politic.

"A Survey on Bias and Fairness in Machine Learning"

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains.

"Artificial Intelligence: the global landscape of ethics guidelines"

In the past five years, private companies, research institutions and public sector organizations have issued principles and guidelines for ethical artificial intelligence (AI). However, despite an apparent agreement that AI should be ‘ethical’, there is debate about both what constitutes ‘ethical AI’ and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analysed the current corpus of principles and guidelines on ethical AI.

"Bias in computer systems"

From an analysis of actual cases, three categories of bias in computer systems have been developed: preexisting, technical, and emergent. Preexisting bias has its roots in social institutions, practices, and attitudes. Technical bias arises from technical constraints of considerations. Emergent bias arises in a context of use. Although others have pointed to bias inparticular computer systems and have noted the general problem, we know of no comparable work that examines this phenomenon comprehensively and which offers a framework for understanding and remedying it.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.

Fairness and Machine Learning

This book gives a perspective on machine learning that treats fairness as a central concern rather than an afterthought. We’ll review the practice of machine learning in a way that highlights ethical challenges. We’ll then discuss approaches to mitigate these problems.

We’ve aimed to make the book as broadly accessible as we could, while preserving technical rigor and confronting difficult moral questions that arise in algorithmic decision making.

Water Is...: The Indispensability Of Water In Society And Life

People are increasingly aware of the role that water has in shaping society and how it impacts quality of life. This is the first book to provide a holistic perspective on water, capturing the full breadth of the science, technology, policy, history, and future outlook for the most important substance on earth — written at a level accessible to non-experts in each of these areas.