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:  Legislative influence 
1. Abstract of Cited Paper (BIB001):  The article discusses the governance of the digital as the new challenge posed by technological innovation. It then introduces a new distinction between soft ethics, which applies after legal compliance with legislation, such as the General Data Protection Regulation in the European Union, and hard ethics, which precedes and contributes to shape legislation. It concludes by developing an analysis of the role of digital ethics with respect to digital regulation and digital governance. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’. 
2. Abstract of Cited Paper (BIB002):  As concerns about unfairness and discrimination in “black box” machine learning systems rise, a legal “right to an explanation” has emerged as a compellingly attractive approach for challenge and redress. We outline recent debates on the limited provisions in European data protection law, and introduce and analyze newer explanation rights in French administrative law and the draft modernized Council of Europe Convention 108. While individual rights can be useful, in privacy law they have historically unreasonably burdened the average data subject. “Meaningful information” about algorithmic logics is more technically possible than commonly thought, but this exacerbates a new “transparency fallacy”—an illusion of remedy rather than anything substantively helpful. While rights-based approaches deserve a firm place in the toolbox, other forms of governance, such as impact assessments, “soft law,” judicial review, and model repositories deserve more attention, alongside catalyzing agencies acting for users to control algorithmic system design.

Literature Review:
In Europe, the introduction of the GDPR in May 2018, with its threat of large fines for inappropriate collection or processing of data 18 and supposed right to an explanation BIB001 ) has clearly had a significant impact on the ML research community, concentrating attention on methods to ensure non-maleficence (privacy and security), autonomy (consent), and post-hoc explanations. This is understandable but concerning. The GDPR may, like all regulation, prove to have unintended consequences in terms of incentivising some types of research over others and promoting minimum adherence over best practice. For example, it has clearly encouraged a focus on privacy and explicability over the promotion of autonomy in design choices and done very little to encourage competition to be the most ethical system (Luciano BIB002 ). The risk is that being compliant may be seen to be good enough. This is evident in the case of the 'the right to an explanation', where a focus on achieving what can be termed a minimum-viable-explanation has encouraged the ML research 16 The word "failed" is used because it is important to note that this does not mean that such tools do not exist, but that they did not come up in our search, are available only as proprietary solutions, or are currently only available in theory and not in practice. 17 The GDPR can be viewed in full here: https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1532348683434&uri=CELEX:02016R0679-20160504 18 A guide to the implications of the GDPR is provided by the ICO here: https://ico.org.uk/for-organisations/guide-to-data-protection/guideto-the-general-data-protection-regulation-gdpr/ community to focus on mechanisms that can inform users BIB001 in a simplified manner of how the inputs are related to the outputs. This may be necessary but is not sufficient, because such mechanisms-e.g. LIME  , SHAP  , Sensitivity Analysis (Oxborough and colleagues, 2018)-do not really succeed in helping developers provide meaningful BIB003 explanations that give individuals greater control over what is being inferred about them from their data.