From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> Misguided Evidence <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> Misguided Evidence <s> 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. <s> BIB002 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> Misguided Evidence <s> 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. <s> BIB003 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> Misguided Evidence <s> 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. <s> BIB004 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> Misguided Evidence <s> 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’. <s> BIB005
Conclusions can only be as reliable (but also as neutral) as the data they are based on, and this can lead to bias. For example, BIB004 found that the COMPAS recidivism algorithm commonly used in pretrial, parole, and sentencing decisions in the United States, is no more accurate or fair than predictions made by people with little or no criminal justice expertise. Unfair outcomes An action could be found to be discriminatory if it has a disproportionate impact on one group of people. For instance, BIB002 articulates how the adoption of predictive policing tools is leading to more people of colour being arrested, jailed or physically harmed by police. Transformative effects Algorithmic activities, like profiling, can lead to challenges for autonomy and informational privacy. For example,  examined the level of access required to personal data by more than 1000 apps listed in the 'most popular' free and paid for categories on the Google Play Store. They found that free apps requested significantly more data than paid-for apps, suggested that the business model of these 'free' apps is the exploitation of the personal data. Traceability It is hard to assign responsibility to algorithmic harms and this can lead to issues with moral responsibility. For example, it may be unclear who (or indeed what) is responsible for autonomous car fatalities. An in depth ethical analysis of this specific issue is provided by  Figure 1: Ethical concerns related to algorithmic use based on the 'map' created by Mittelstadt and colleagues (2016) . This might seem like an incredibly tall order. However, rising to the challenge is both essential and possible. Indeed, those that claim that it is impossible are falling foul of the is-ism fallacy where they confuse the way things are with the way things can be  , or indeed should be. It is possible to design an algorithmically-enhanced society pro-ethically 1 BIB003 , so that it protects the values, principles, and ethics that society thinks are fundamental BIB005 . This is the message that social scientists, ethicists, philosophers, policymakers, technologists and civil society have been delivering in a collective call for the development of appropriate governance mechanisms (D'Agostino & Durante, 2018) that will enable society to capitalise on the opportunities, whilst ensuring that human rights are respected BIB001 , and fair and ethical decision-making is maintained  . The purpose of the following pages is to highlight the part that technologists, or ML developers, can take in this broader conversation. Specifically, section 2 discusses how efforts to date have been too focused on the 'what' of ethical AI (i.e. debates about principles and codes of conduct) and not enough on the 'how' of applied ethics. Section 3 outlines the research planned to contribute to closing this gap between principles and practice through the creation of an 'applied ethical AI typology', and the methodology for its creation. Section 4, summarises what the typology shows about the low availability and maturity, as well as skewed distribution, of tools and methodologies available for practical AI ethics. Section 5, argues that there is a need for a more coordinated effort, from multi-disciplinary researchers, innovators, policymakers, citizens, developers and designers, to create and evaluate new tools and methodologies, in order to ensure that there is a 'how' for every 'what' at each stage of the Machine Learning pipeline. Finally, section 6, concludes that this will be challenging to achieve, but it would be imprudent not to try. 1 The difference between ethics by design and pro-ethical design is the following: ethics by design can be paternalistic in ways that constrain the choices of agents, because it makes some options less easily available or not at all; instead, pro-ethical design still forces agents to make choices, but this time the nudge is less paternalistic because it does not preclude a course of action but requires agents to make up their mind about it. A simple example can clarify the difference. A speed camera is a form of nudging (drivers should respect the speed limits) but it is pro-ethical insofar as it leaves to the drivers the freedom to choose to pay a ticket, for example in case of an emergency. On the contrary, in terms of ethics by design, speed bumps are a different kind of traffic calming measure designed to slow down vehicles and improve safety. They may seem like a good idea, but they involve a physical alteration of the road, which is permanent and leaves no real choice to the driver. This means that emergency vehicles, such as a medical ambulance, a police car, or a fire engine, must also slow down, even when responding to an emergency.