From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> A way forward <s> 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... <s> BIB001 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> A way forward <s> 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. <s> BIB002 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> A way forward <s> 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. <s> BIB003 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> A way forward <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> BIB004 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> A way forward <s> 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. <s> BIB005 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> A way forward <s> 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. <s> BIB006 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> A way forward <s> 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. <s> BIB007 </s> From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices <s> A way forward <s> 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. <s> BIB008
Social scientists BIB001 and political philosophers (from Rousseau and Kant, to Rawls and Habermas) BIB003 , are used to dealing with the kind of plurality outlined in section 4, and to thinking about the interaction between individual level and group level 'ethics.' This is why Nissenbaum argues for a contextual account of privacy, one that recognises the varying nature of informational norms BIB001 and BIB007 state that transparency is only meaningful in the context of a defined critical audience. However, the ML developer community may be less used to dealing with this kind of complexity, and more used to scenarios where there is at least a seemingly quantifiable relationship between input and output. As a result, the existing approaches to designing and programming ethical ML fail to resolve what  terms the moral-semantic trilemma, as almost all tools and methods highlighted in the typology are either too semantically strict, too semantically flexible, or overly unpredictable  . Overcoming this nervousness of social complexity, embracing uncertainty, and accepting that: (1) AI is built on assumptions; (2) human behaviour is complex; (3) algorithms can have unfair consequences; (4) algorithmic predictions can be hard to interpret  ; (5) trade-offs may be inevitable; and (6) positive, ethical features are open to progressive increase but are not bounded between 0 and 1 (e.g., an algorithm can be increasingly fair, and fairer than another algorithm or a previous version, but makes no sense to say that it is fair 100% in absolute terms, compare this to the case of speed: it makes sense to say that an object is moving quickly, or that it is fast or faster than another, but not that it is fast 100%), is likely to be highly beneficial for the development of applied ethical tools and methodologies for at least three reasons. First, embracing uncertainty will naturally encourage ML developers to ask more probing and open (i.e., philosophical) questions  ) that will lead to more nuanced and reasoned answers and hence decisions about why and when certain trade-offs, for example, between accuracy and interpretability BIB002 , are justified, based on factors such as proportionality to risk  . Second, it will encourage a more flexible and reflexive approach to applied ethics that is more in-keeping with the way ML systems are actually developed: it is not think then code, but rather think and code. In other words, it will encourage a move away from the 'move fast and break things' approach towards an approach of 'make haste slowly' (festina lente) BIB008 . Finally, it would also mitigate a significant risk posed by the current sporadic application of ethical-design tools and/or methods during different development stages, of the ethical principles having been written into the business and use-case, but coded out by the time a system gets to deployment. To enable developers to embrace this valuable uncertainty, it will be important to promote the development of tools, like DotEveryone's agile consequence scanning event, 22 that prompt developers to reflect on the impacts (both direct and indirect) of the solutions they are developing on the 'end user', and on how these impacts can be altered by seemingly minor Framework  ). Taking this approach recognises that, in a digital context, ethical principles are not simply either applied or not, but they are regularly re-applied or applied differently, or better, or ignored as algorithmic systems are developed, deployed, configured BIB004 tested, revised and re-tuned BIB005 . Although clearly beneficial, this approach to applied ML ethics of regular reflection and application will not be possible unless (i) the skewed distribution of tools in the typology is balanced out and (ii) acceleration of tools maturity level from research labs into production environments. To achieve (i)-(ii), society needs to come together in communities comprised of multi-disciplinary researchers BIB006 , innovators, 22 Full details of DotEveryone's Consequence Scanning Event: https://doteveryone.org.uk/project/consequence-scanning/ policymakers, citizens, developers and designers  to foster the development of: (1) common knowledge and understanding; and (2) a common goal to be achieved from the development of tools and methodologies for applied AI ethics  . These outputs will provide a reason, a mechanism, and a consensus to coordinate the efforts behind tool development. Ultimately, this will produce better results than the current approach, which allows a 'thousand flowers to bloom' but fails to create tools that fill in the gaps (this is a typical 'intellectual market' failure), and may encourage competition to produce preferable options. The opportunity that this presents is too great for us to wait, the ML research community should start collaborating now with a specific focus on: 3. commitment to reproducibility, openness, and sharing of knowledge and technical solutions (e.g., software), also in view of satisfying (1) and supporting (2); 4. evaluation and creation of pro-ethical business models and incentive structures that balance the costs and rewards of investing in ethical AI across society, also in view of supporting (1)-(3).