You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices 
Chapter Title:  Moving from Principles to Practice 
1. Abstract of Cited Paper (BIB001):  Privacy by Design has emerged as a proactive approach for embedding privacy into the early stages of the design of information and communication technologies, but it is no ‘silver bullet’. Challenges involved in engineering Privacy by Design include a lack of holistic and systematic methodologies that address the complexity and variability of privacy issues and support the translation of its principles into engineering activities. A consequence is that its principles are given at a high level of abstraction without accompanying tools and guidelines to address these challenges. We analyse three privacy requirements engineering methods from which we derive a set of criteria that aid in identifying data-processing activities that may lead to privacy violations and harms and also aid in specifying appropriate design decisions. We also present principles for engineering Privacy by Design that can be developed upon these criteria. Based on these, we outline some preliminary thoughts on the form of a principled framework that addresses the plurality and contextuality of privacy issues and supports the translation of the principles of Privacy by Design into engineering activities. 
2. Abstract of Cited Paper (BIB002):  To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems. 
3. Abstract of Cited Paper (BIB003):  Artificial Intelligence (AI) technology presents a multitude of ethical concerns, many of which are being actively considered by organizations ranging from small groups in civil society to large corporations and governments. However, it also presents ethical concerns which are not being actively considered. This paper presents a broad overview of twelve topics in ethics in AI, including function, transparency, evil use, good use, bias, unemployment, socio-economic inequality, moral automation and human de-skilling, robot consciousness and rights, dependency, social-psychological effects, and spiritual effects. Each of these topics will be given a brief discussion, though each deserves much deeper consideration. 
4. Abstract of Cited Paper (BIB004):  Contrary to the criticism that mysterious, unaccountable black-box software systems threaten to make the logic of critical decisions inscrutable, we argue that algorithms are fundamentally understandable pieces of technology. Software systems are designed to interact with the world in a controlled way and built or operated for a specific purpose, subject to choices and assumptions. Traditional power structures can and do turn systems into opaque black boxes, but technologies can always be understood at a higher level, intensionally in terms of their designs and operational goals and extensionally in terms of their inputs, outputs and outcomes. The mechanisms of a system's operation can always be examined and explained, but a focus on machinery obscures the key issue of power dynamics. While structural inscrutability frustrates users and oversight entities, system creators and operators always determine that the technologies they deploy are fit for certain uses, making no system wholly inscrutable. We investigate the contours of inscrutability and opacity, the way they arise from power dynamics surrounding software systems, and the value of proposed remedies from disparate disciplines, especially computer ethics and privacy by design. We conclude that policy should not accede to the idea that some systems are of necessity inscrutable. Effective governance of algorithms comes from demanding rigorous science and engineering in system design, operation and evaluation to make systems verifiably trustworthy. Rather than seeking explanations for each behaviour of a computer system, policies should formalize and make known the assumptions, choices, and adequacy determinations associated with a system.This article is part of the theme issue 'Governing artificial intelligence: ethical, legal, and technical opportunities and challenges'. 
5. Abstract of Cited Paper (BIB005):  Codes of ethics provide a critical foundation for an organization to define the tenets that best reflect their values. These codes provide a touchstone for employees to follow that establishes trust between an organization and their customers and key stakeholders. The rapid acceleration of autonomous and intelligent systems (A/IS) has created an imperative for technologists to complement these codes with new methodologies focused on the widespread implementation of ethical considerations in all levels of design and manufacture. This paper describes IEEE’s work in this arena, including the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, the IEEE TechEthics program, the IEEE P7000 Standards Series focused on A/IS issues, Digital Inclusion through Trust and Agency, and the ongoing work of the IEEE Society on Social Implications of Technology. This paper also includes a table of approximately 50 activities within the IEEE related to ethics.

Literature Review:
As the call for 'AI governance' has got louder, the number of ). The hope is that these principles, as abstractions  , can act as normative constraints BIB001 on the 'do's' and 'don'ts' of algorithmic use in society. This is a worthwhile aim, and a necessary building block in the creation of an environment that fosters ethical, responsible, and beneficial AI. However, the mere existence of these principles does little to bring about actual change in the design of algorithmic systems, leading to accusations of 'ethics washing' and feelings of 'ethics fatigue'  ). The issue is that, whilst these principles-which (at least in Europe) are largely based on the bioethical principles of beneficence, non-maleficence, justice and autonomy, and the concept of 'explicability' (Floridi and colleagues, 2018  . Avoiding these opportunity costs is crucial, and so the social need for means of translating between the 'what' (ethical principles) and the 'how' (technical requirements)  ) is growing. Complexity, variability, subjectivity, and lack of standardisation, including variable interpretation of the 'components' of each of the ethical principles, make this challenging BIB002 . However, it is achievable if the right questions are asked BIB004 ) BIB003 ) and closer attention is payed to how the design process can influence BIB005 whether an algorithm is more or less 'ethically-aligned.' Thus, this is the aim of this research project: to identify the methods and tools available to help developers, engineers and designers of ML specifically (but we hope the results of the this research may be easily applicable to other branches of AI) reflect on and apply 'ethics' BIB006 ) so that they may know not only what to do or not to do, but also how to do it, or avoid doing it BIB002 .