From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices
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paper_title: What is data ethics?

paper_content:

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,...
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paper_title: Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability:

paper_content:

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...
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paper_title: Association of Internet Researchers (AoIR) Roundtable Summary: Artificial Intelligence and the Good Society Workshop Proceedings

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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.
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paper_title: There is a blind spot in AI research

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Fears about the future impacts of artificial intelligence are distracting researchers from the real risks of deployed systems, argue Kate Crawford and Ryan Calo.
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paper_title: Soft ethics, the governance of the digital and the General Data Protection Regulation

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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’.
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paper_title: What is data ethics?

paper_content:

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,...
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paper_title: Disparate Impact in Big Data Policing

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Police departments large and small have begun to use data mining techniques to predict, prevent, and investigate crime. But data mining systems have the potential for adverse impacts on vulnerable communities, and predictive policing is no different. Reviewing the technical process of predictive policing, the Article begins by illustrating how the different uses of this technology may result in disparate impact on communities of color. Determining individuals’ threat levels by reference to commercial and social data can improperly link dark skin to higher threat levels or to greater suspicion of having committed a particular crime. Crime mapping based on historical data can lead to more arrests for nuisance crimes in minority neighborhoods. These effects are an artifact of the technology itself, and will likely occur even assuming good faith on the part of the police departments using it. This is particularly ironic because predictive policing, like a great deal of data mining solutions, is sold in part as a “neutral” method to counteract unconscious biases, when it is not simply sold to cash-strapped departments as a more cost-efficient way to do policing. The degree to which predictive policing systems have these discriminatory results is unclear to the public and unclear to the police themselves, largely because there is no incentive in place for a department focused solely on “crime control” to even ask the question. This is a problem for which existing law does not provide a solution. Assuming no intent to discriminate, the Fourteenth Amendment is no help. After evaluating the possibilities for Fourth Amendment regulation and finding them wanting, the Article turns toward a new regulatory proposal centered on “discrimination impact assessments.” Modeled on the environmental impact statements of the National Environmental Policy Act, discrimination impact assessments would require police departments to evaluate the efficacy and potential discriminatory effects of competing alternative algorithms and models and to consider mitigation procedures. The regulation would also allow the public to weigh in through a notice-and-comment process. Such a regulation would fill the knowledge gap that makes future policy discussions about the costs and benefits of predictive policing all but impossible. It balances the need for police expertise in the adoption of new crime control technologies with transparency and public input regarding the potential for harm. Such a public process will also serve to increase trust between police departments and the communities they serve.
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paper_title: Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation

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Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a ‘right to be informed’. Further, the ambiguity and limited scope of the ‘right not to be subject to automated decision-making’ contained in Article 22 (from which the alleged ‘right to explanation’ stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative and policy steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018.
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paper_title: The accuracy, fairness, and limits of predicting recidivism

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Algorithms for predicting recidivism are commonly used to assess a criminal defendant’s likelihood of committing a crime. These predictions are used in pretrial, parole, and sentencing decisions. Proponents of these systems argue that big data and advanced machine learning make these analyses more accurate and less biased than humans. We show, however, that the widely used commercial risk assessment software COMPAS is no more accurate or fair than predictions made by people with little or no criminal justice expertise. In addition, despite COMPAS’s collection of 137 features, the same accuracy can be achieved with a simple linear classifier with only two features.
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paper_title: The fallacy of inscrutability

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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'.
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paper_title: Ethical protocols design

paper_content:

The paper offers a solution to the problem of specifying computational systems that behave in accordance with a given set of ethical principles. The proposed solution is based on the concepts of ethical requirements and ethical protocols. A new conceptual tool, called the Control Closure of an operation, is defined and used to translate ethical principles into ethical requirements and protocols. The concept of Generalised Informational Privacy (GIP) is used as a paradigmatic example of an ethical principle. GIP is defined in such a way as to (i) discriminate specific cases in which an individual's GIP can be infringed without accessing the individual's data; (ii) separate unauthorised accesses to data that do not respect the right to GIP from access that do; and (iii) distinguish different degrees of GIP. Finally a camera phone is used to illustrate the proposed solution.
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paper_title: Designing a Value-Driven Future for Ethical Autonomous and Intelligent Systems

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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.
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paper_title: Towards a Principled Approach for Engineering Privacy by Design

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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.
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paper_title: Transparent, explainable, and accountable AI for robotics

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To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.
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paper_title: Ethical Reflections on Artificial Intelligence

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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.
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paper_title: From Machine Learning to Explainable AI

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The success of statistical machine learning (ML) methods made the field of Artificial Intelligence (AI) so popular again, after the last AI winter. Meanwhile deep learning approaches even exceed human performance in particular tasks. However, such approaches have some disadvantages besides of needing big quality data, much computational power and engineering effort; those approaches are becoming increasingly opaque, and even if we understand the underlying mathematical principles of such models they still lack explicit declarative knowledge. For example, words are mapped to high-dimensional vectors, making them unintelligible to humans. What we need in the future are context-adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real-world phenomena. This is the goal of explainable AI, which is not a new field; rather, the problem of explainability is as old as AI itself. While rule-based approaches of early AI were comprehensible “glass-box” approaches at least in narrow domains, their weakness was in dealing with uncertainties of the real world. Maybe one step further is in linking probabilistic learning methods with large knowledge representations (ontologies) and logical approaches, thus making results re-traceable, explainable and comprehensible on demand.
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paper_title: Privacy Principles: Towards a Common Privacy Audit Methodology

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A lot of privacy principles have been proposed in the literature with the aim to preserve users’ privacy through the protection of the personal data collected by service providers. Despite the fact that there were remarkable efforts to gather all privacy principles and use them on a common privacy-by-design system, to the best of our knowledge, there is no published methodology that combines in a clear and structured way the existing privacy principles for supporting the design of a Privacy Preserving System. The absence of a widely accepted structured representation of the privacy principles makes their adoption or/and satisfaction difficult and in some cases inconsistent. Considering that privacy protection on its own is not an easy task for an organisation, the “scattered” privacy principles impose significant additional complexity. Consequently, very frequently organizations fail to effectively protect the privacy of their users. In this paper a structured privacy audit methodology that consists of discrete steps that organizations can follow for deciding or/and auditing the privacy protection measures is proposed. Every step is based on the significance of a privacy principle and on the sequence of the audit procedure.
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paper_title: What Can Political Philosophy Teach Us about Algorithmic Fairness?

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What does it mean for an algorithmic decision-making system to be “fair” or “non-discriminatory” in terms that can be operationalized? Providing a rigorous understanding of these terms has long been a preoccupation of moral and political philosophers. This article draws on such work to elucidate emerging debates about fair algorithms.
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paper_title: Soft ethics, the governance of the digital and the General Data Protection Regulation

paper_content:

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’.
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paper_title: Enslaving the Algorithm: From a"Right to an Explanation"to a"Right to Better Decisions"?

paper_content:

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.
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paper_title: Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation

paper_content:

Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a ‘right to be informed’. Further, the ambiguity and limited scope of the ‘right not to be subject to automated decision-making’ contained in Article 22 (from which the alleged ‘right to explanation’ stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative and policy steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018.
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paper_title: Manipulating and Measuring Model Interpretability

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With the increased use of machine learning in decision-making scenarios, there has been a growing interest in creating human-interpretable machine learning models. While many such models have been proposed, there have been relatively few experimental studies of whether these models achieve their intended effects, such as encouraging people to follow the model's predictions when the model is correct and to deviate when it makes a mistake. We present a series of randomized, pre-registered experiments comprising 3,800 participants in which people were shown functionally identical models that varied only in two factors thought to influence interpretability: the number of input features and the model transparency (clear or black-box). Predictably, participants who were shown a clear model with a small number of features were better able to simulate the model's predictions. However, contrary to what one might expect when manipulating interpretability, we found no improvements in the degree to which participants followed the model's predictions when it was beneficial to do so. Even more surprisingly, increased transparency hampered people's ability to detect when the model makes a sizable mistake and correct for it, seemingly due to information overload. These counterintuitive results suggest that decision scientists creating interpretable models should harbor a healthy skepticism of their intuitions and empirically verify that interpretable models achieve their intended effects.
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paper_title: What is data ethics?

paper_content:

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,...
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paper_title: Ethics and Privacy in AI and Big Data: Implementing Responsible Research and Innovation

paper_content:

Emerging combinations of artificial intelligence, big data, and the applications these enable are receiving significant media and policy attention. Much of the attention concerns privacy and other ethical issues. In our article, we suggest that what is needed now is a way to comprehensively understand these issues and find mechanisms of addressing them that involve stakeholders, including civil society, to ensure that these technologies’ benefits outweigh their disadvantages. We suggest that the concept of responsible research and innovation (RRI) can provide the framing required to act with a view to ensuring that the technologies are socially acceptable, desirable, and sustainable. We draw from our work on the Human Brain Project, one potential driver for the next generation of these technologies, to discuss how RRI can be put in practice.
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paper_title: Why privacy is not enough privacy in the context of “ubiquitous computing” and “big data”

paper_content:

Purpose – Ubiquitous computing and “big data” have been widely recognized as requiring new concepts of privacy and new mechanisms to protect it. While improved concepts of privacy have been suggested, the paper aims to argue that people acting in full conformity to those privacy norms still can infringe the privacy of others in the context of ubiquitous computing and “big data”. Design/methodology/approach – New threats to privacy are described. Helen Nissenbaum's concept of “privacy as contextual integrity” is reviewed concerning its capability to grasp these problems. The argument is based on the assumption that the technologies work, persons are fully informed and capable of deciding according to advanced privacy considerations. Findings – Big data and ubiquitous computing enable privacy threats for persons whose data are only indirectly involved and even for persons about whom no data have been collected and processed. Those new problems are intrinsic to the functionality of these new technologies and...
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paper_title: Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability:

paper_content:

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...
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paper_title: European Union regulations on algorithmic decision-making and a"right to explanation"

paper_content:

We summarize the potential impact that the European Union’s new General Data Protection Regulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on user-level predictors) which “significantly affect” users. The law will also effectively create a “right to explanation,” whereby a user can ask for an explanation of an algorithmic decision that was made about them. We argue that while this law will pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks which avoid discrimination and enable explanation.
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paper_title: Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach

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In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: (a) the development of a ‘good AI society’; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.
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paper_title: The “big red button” is too late: an alternative model for the ethical evaluation of AI systems

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As a way to address both ominous and ordinary threats of artificial intelligence (AI), researchers have started proposing ways to stop an AI system before it has a chance to escape outside control and cause harm. A so-called “big red button” would enable human operators to interrupt or divert a system while preventing the system from learning that such an intervention is a threat. Though an emergency button for AI seems to make intuitive sense, that approach ultimately concentrates on the point when a system has already “gone rogue” and seeks to obstruct interference. A better approach would be to make ongoing self-evaluation and testing an integral part of a system’s operation, diagnose how the system is in error and to prevent chaos and risk before they start. In this paper, we describe the demands that recent big red button proposals have not addressed, and we offer a preliminary model of an approach that could better meet them. We argue for an ethical core (EC) that consists of a scenario-generation mechanism and a simulation environment that are used to test a system’s decisions in simulated worlds, rather than the real world. This EC would be kept opaque to the system itself: through careful design of memory and the character of the scenario, the system’s algorithms would be prevented from learning about its operation and its function, and ultimately its presence. By monitoring and checking for deviant behavior, we conclude, a continual testing approach will be far more effective, responsive, and vigilant toward a system’s learning and action in the world than an emergency button which one might not get to push in time.
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paper_title: Transparent to whom? No algorithmic accountability without a critical audience

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ABSTRACT Big data and data science transform organizational decision-making. We increasingly defer decisions to algorithms because machines have earned a reputation of outperforming us. As algorithms become embedded within organizations, they become more influential and increasingly opaque. Those who create algorithms may make arbitrary decisions in all stages of the ‘data value chain’, yet these subjectivities are obscured from view. Algorithms come to reflect the biases of their creators, can reinforce established ways of thinking, and may favour some political orientations over others. This is a cause for concern and calls for more transparency in the development, implementation, and use of algorithms in public- and private-sector organizations. We argue that one elementary – yet key – question remains largely undiscussed. If transparency is a primary concern, then to whom should algorithms be transparent? We consider algorithms as socio-technical assemblages and conclude that without a critical audience, algorithms cannot be held accountable.
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paper_title: Algorithmic Accountability and Public Reason

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The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system’s outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in terms of the democratic ideal of ‘public reason’. I argue that situating demands for algorithmic accountability within this justificatory framework enables us to better articulate their purpose and assess the adequacy of efforts toward them.
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paper_title: Establishing the rules for building trustworthy AI

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The European Commission’s report ‘Ethics guidelines for trustworthy AI’ provides a clear benchmark to evaluate the responsible development of AI systems, and facilitates international support for AI solutions that are good for humanity and the environment, says Luciano Floridi.
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paper_title: Tolerant Paternalism: Pro-ethical Design as a Resolution of the Dilemma of Toleration

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Toleration is one of the fundamental principles that inform the design of a democratic and liberal society. Unfortunately, its adoption seems inconsistent with the adoption of paternalistically benevolent policies, which represent a valuable mechanism to improve individuals’ well-being. In this paper, I refer to this tension as the dilemma of toleration. The dilemma is not new. It arises when an agent A would like to be tolerant and respectful towards another agent B’s choices but, at the same time, A is altruistically concerned that a particular course of action would harm, or at least not improve, B’s well-being, so A would also like to be helpful and seeks to ensure that B does not pursue such course of action, for B’s sake and even against B’s consent. In the article, I clarify the specific nature of the dilemma and show that several forms of paternalism, including those based on ethics by design and structural nudging, may not be suitable to resolve it. I then argue that one form of paternalism, based on pro-ethical design, can be compatible with toleration and hence with the respect for B’s choices, by operating only at the informational and not at the structural level of a choice architecture. This provides a successful resolution of the dilemma, showing that tolerant paternalism is not an oxymoron but a viable approach to the design of a democratic and liberal society.
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paper_title: Ethical protocols design

paper_content:

The paper offers a solution to the problem of specifying computational systems that behave in accordance with a given set of ethical principles. The proposed solution is based on the concepts of ethical requirements and ethical protocols. A new conceptual tool, called the Control Closure of an operation, is defined and used to translate ethical principles into ethical requirements and protocols. The concept of Generalised Informational Privacy (GIP) is used as a paradigmatic example of an ethical principle. GIP is defined in such a way as to (i) discriminate specific cases in which an individual's GIP can be infringed without accessing the individual's data; (ii) separate unauthorised accesses to data that do not respect the right to GIP from access that do; and (iii) distinguish different degrees of GIP. Finally a camera phone is used to illustrate the proposed solution.
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paper_title: Ethics and the Practice of Software Design

paper_content:

The paper offers an analysis of the problem of integrating ethical principles into the practice of software design. The approach is grounded on a review of the relevant literature from Computer Ethics and Professional Ethics. The paper is divided into four sections. The first section reviews some key questions that arise when the ethical impact of computational artefacts is analysed. The inner informational nature of such questions is used to argue in favour of the need for a specific branch of ethics called Information Ethics. Such ethics deal with a specific class of ethical problems and Informational Privacy is introduced as a paradigmatic example. The second section analyses the ethical nature of computational artefacts. This section highlights the fact that this nature is impossible to comprehend without first considering designers, users, and patients alongside the artefacts they create, use and are affected by. Some key ethical concepts are discussed, such as freedom, agency, control, autonomy and accountability. The third section illustrates how autonomous computational artefacts are rapidly changing the way in which computation is used an perceived. The description of the ethical challenges posed to software engineers by this shift in perspective closes the section. The fourth and last section of the paper is dedicated to a discussion of Professional Ethics for software engineers. After establishing the limits of the professional codes of practice, it is argued that ethical considerations are best embedded directly into software design practise. In this context, the Value Sensitive Design approach is considered and insight into how this is being integrated into current research in ethical design methodologies is given.
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paper_title: Governing artificial intelligence: ethical, legal and technical opportunities and challenges

paper_content:

This paper is the introduction to the special issue entitled: 'Governing artificial intelligence: ethical, legal and technical opportunities and challenges'. Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare and humanitarian aid, to the mundane like dating. AI, including embodied AI in robotics and techniques like machine learning, can improve economic, social welfare and the exercise of human rights. Owing to the proliferation of AI in high-risk areas, the pressure is mounting to design and govern AI to be accountable, fair and transparent. How can this be achieved and through which frameworks? This is one of the central questions addressed in this special issue, in which eight authors present in-depth analyses of the ethical, legal-regulatory and technical challenges posed by developing governance regimes for AI systems. It also gives a brief overview of recent developments in AI governance, how much of the agenda for defining AI regulation, ethical frameworks and technical approaches is set, as well as providing some concrete suggestions to further the debate on AI governance.This article is part of the theme issue 'Governing artificial intelligence: ethical, legal, and technical opportunities and challenges'.
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Title: From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices

Section 1: Introduction
Description 1: Provide an overview of the increasing opportunities and ethical challenges in AI, highlighting the need to balance innovation with ethical considerations.

Section 2: Ethical Concern
Description 2: Discuss the various ethical concerns associated with AI and ML, including inconclusive evidence, inscrutable evidence, misguided evidence, unfair outcomes, transformative effects, and traceability.

Section 3: Moving from Principles to Practice
Description 3: Address the shift from defining ethical principles to implementing them in practice, and the emerging need for practical tools to translate these principles into technical requirements.

Section 4: Methodology
Description 4: Detail the research design, including the creation of a typology of ethical AI tools and methods, and the review process of existing tools and methodologies.

Section 5: Initial Findings
Description 5: Summarize the initial findings, including the uneven distribution of tools across the AI pipeline and the influence of factors like legislation, individual focus, and lack of consistency in definitions.

Section 6: Legislative Influence
Description 6: Analyze the impact of regulations like GDPR on the focus areas of AI ethics research and the potential unintended consequences of such legislation.

Section 7: An Individual Focus
Description 7: Explore how the current focus on individual-level impacts leaves societal impacts underexplored and the potential risks this poses to the acceptance of AI.

Section 8: A Lack of Consistency
Description 8: Discuss the challenges posed by varying definitions and interpretations of ethical principles and the need for consistent, context-aware tools and methodologies.

Section 9: A Way Forward
Description 9: Propose steps for advancing the development and adoption of practical AI ethics tools, emphasizing the importance of multidisciplinary collaboration and flexible, reflective approaches.

Section 10: Conclusion
Description 10: Conclude with reflections on the necessity and difficulty of embedding ethical considerations into AI design, calling for coordinated efforts and constructive patience to address the challenges.