From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> Introduction <s> This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation,... <s> BIB001 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> Introduction <s> Fears about the future impacts of artificial intelligence are distracting researchers from the real risks of deployed systems, argue Kate Crawford and Ryan Calo. <s> BIB002 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> Introduction <s> Models for understanding and holding systems accountable have long rested upon ideals and logics of transparency. Being able to see a system is sometimes equated with being able to know how it work... <s> BIB003 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> Introduction <s> This article is based on a roundtable held at the Association of Internet Researchers (AoIR) annual conference in 2017, in Tartu, Estonia. The roundtable was organized by the Oxford Internet Institute’s (OII) Digital Ethics Lab (DELab). It was entitled “Artificial Intelligence and the Good Society”. It brought together four scholars—Michael Zimmer, Stine Lomborg, Ben Zevenbergen, and Corinne Cath—to discuss the promises and perils of artificial intelligence (AI), in particular what ethical frameworks are needed to guide AI’s rapid development and increased use in societies. The paper covers three case studies. They give a distinct overview of the ethical issues raised by the use of AI at different levels of analysis: top-down application of AI, bottom-up use of AI, and how academics and governments have reacted to these new challenges. From the case studies, four areas emerged. They represent some of the most topical ethical questions related to AI: (1) its uses, (2) its users, (3) its designers, and (4) the data that fuel it. Each of them provided a specific subset of ethical concerns that need further investigation. In conclusion, three recommendations are formulated for researchers and regulators to ensure the AI has a net-positive impact on society. <s> BIB004
As the availability of data on almost every aspect of life, and the sophistication of machine learning (ML) techniques, has increased  so have the opportunities for improving both public and private life BIB001 . Society has greater control than it has ever had over outcomes related to: (1) who people can become; (2) what people can do; (3) what people can achieve; and (4) how people can interact with the world (Floridi and colleagues, 2018a ). Yet, growing concerns about the ethical challenges posed by the increased use of ML in particular, and Artificial Intelligence (AI) more generally, in society, threaten to put a halt to the advancement of beneficial applications, including in data science  , unless handled proportionately. Balancing this tension between the need to support innovation, so that society's right to benefit from science is protected  , with the need to limit the potential harms associated with poorly-designed AI (and specifically ML in this context), (summarised in figure 1) is challenging. ML algorithms are powerful BIB003 socio-technical constructs that raise concerns that are as much (if not more) about people as they are about code BIB002 . Enabling the so-called dual advantage of 'ethical ML'-so that the opportunities are capitalised on, whilst the harms are foreseen and minimised or prevented (Floridi and colleagues. 2018 )-requires asking difficult questions about design, development, deployment, practices, uses and users, as well as the data that fuel the whole process BIB004 . Lessig was right all along: code is both our greatest threat and our greatest promise  .