Ethics of Artificial Intelligence and Robotics Stanford Encyclopedia of Philosophy

Such freedom implies, for example, not being subject to technological experiments, manipulation, or surveillance (Jobin et al., 2019). We want the values of freedom and autonomy to guide the development and use of AI, because we want to ensure that this new technology is emancipatory and empowering. They first and foremost study relational power, particularly systemic issues in society, in order to emancipate certain societal groups.

So far, some papers have been published on the subject of teaching ethics to data scientists (Garzcarek and Steuer 2019; Burton et al. 2017; Goldsmith and Burton 2017; Johnson 2017) but by and large very little to nothing has been written about the tangible implementation of ethical goals and values. In a first step, 22 of the major guidelines of AI ethics will be analyzed and compared. In a second step, I compare the principles formulated in the guidelines with the concrete practice of research and development of AI systems. In a third and final step, I will work out ideas on how AI ethics can be transformed from a merely discursive phenomenon into concrete directions for action. The participants in this debate are united by being technophiles in

the sense that they expect technology to develop rapidly and bring

broadly welcome changes—but beyond that, they divide into those

who focus on benefits (e.g., Kurzweil) and those who focus on risks

(e.g., Bostrom).

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AI Ethics is now being taught in high school and middle school as well as in Responsible AI practices in professional business courses. As laws like the AI Act become more prevalent, one can expect AI Ethics knowledge to become mainstream. Experience with AI has demonstrated that following good AI Ethics is not just responsible behavior, it is required to get good business value out of AI.

So what happens is that AI research and development takes place in “closed-door industry settings”, where “user consent, privacy and transparency are often overlooked in favor of frictionless functionality that supports profit-driven business models” (Campolo et al. 2017, 31 f.). Despite this dispensation of ethical principles, AI systems are used in areas of high societal significance such as health, police, mobility or education. Thus, in the AI Now Report 2018, it is repeated that the AI industry “urgently needs new approaches to governance”, since, “internal governance structures at most technology companies are failing to ensure accountability for AI systems” (Whittaker et al. 2018, 4). Thus, ethics guidelines often fall into the category of a “’trust us’ form of [non-binding] corporate self-governance” (Whittaker et al. 2018, 30) and people should “be wary of relying on companies to implement ethical practices voluntarily” (Whittaker et al. 2018, 32). In the past five years, private companies, research institutions and public sector organizations have issued principles and guidelines for ethical artificial intelligence (AI). However, despite an apparent agreement that AI should be ‘ethical’, there is debate about both what constitutes ‘ethical AI’ and which ethical requirements, technical standards and best practices are needed for its realization.

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Examples of AI ethics issues include data responsibility and privacy, fairness, explainability, robustness, transparency, environmental sustainability, inclusion, moral agency, value alignment, accountability, trust, and technology misuse. UNESCO produced the first-ever global standard on AI ethics – the ‘Recommendation on the Ethics of Artificial Intelligence’ in November 2021. Jobin et al. (2019) point out that AI ethics guidelines discuss privacy both as a value to uphold and a right that should be protected. Moreover, privacy is often discussed in relation to data protection, which is in line with the common definitions of privacy as “informational control” or “restricted access” (DeCew, 2018). Under both definitions, privacy is understood as a dispositional power, more precisely, as the capacity to control what happens to one’s information and to determine who has access to one’s information or other aspects of the self. AI, then, is perceived as a potential threat to this capacity because it entails the collection and analysis of large quantities and new types of personal data.

There will need to be individuals to help manage these systems as data grows and changes every day. There will still need to be resources to address more complex problems within the industries that are most likely to be affected by job demand shifts, like customer service. The important aspect of artificial intelligence and its effect on the job market will be helping individuals transition to these new areas of market demand. Paul Jones, professor emeritus of information science at the University of North Carolina, Chapel Hill, observed, “Unless, as I hope happens, the idea that all tech is neutral is corrected, there is little hope or incentive to create ethical AI.

I conclude that a critical theory studies the power structures and relations in a society, with the goal of protecting and promoting human emancipation, and seeks not only to diagnose, but also to change society. 3, I take a more detailed look at the concept of power and explain how a pluralist understanding of the concept allows us to analyze power structures and relations, on the one hand, and (dis)empowerment, on the other hand. 4, I argue that the vast majority of the established AI ethics principles and topics in the field are fundamentally aimed at realizing human emancipation and empowerment, by defining these issues in terms of power. 5, I propose that AI ethics should be seen as a critical theory, given that the discipline is fundamentally concerned with emancipation and empowerment, and meant not only to analyze the impact of emerging technologies on individuals and society, but also to change it. In their AI Now 2017 Report, Kate Crawford and her team state that ethics and forms of soft governance “face real challenges” (Campolo et al. 2017, 5). This is mainly due to the fact that ethics has no enforcement mechanisms reaching beyond a voluntary and non-binding cooperation between ethicists and individuals working in research and industry.

Making mandatory to deposit these algorithms in a database owned and operated by this entrusted super-partes body could ease the development of this overall process. Creating more ethical AI requires a close look at the ethical implications of policy, education, and technology. Regulatory frameworks can ensure that technologies benefit society rather than harm it. Globally, governments are beginning to enforce policies for ethical AI, including how companies should deal with legal issues if bias or other harm arises. Early on, it was popularly assumed that the future of AI would involve the automation of simple repetitive tasks requiring low-level decision-making. But AI has rapidly grown in sophistication, owing to more powerful computers and the compilation of huge data sets.

In classical control engineering, distributed control is often

achieved through a control hierarchy plus control loops across these

hierarchies. First Law—A robot may not injure a human being or, through

inaction, allow a human being to come to harm. Second Law—A

robot must obey the orders given it by human beings except where such

orders would conflict with the First Law.

An organization’s approach to AI ethics can be guided by principles that can be applied to products, policies, processes, and practices throughout the organization to help enable trustworthy AI. These principles should be structured around and supported by focus areas, such as explainability or fairness, around which standards can be developed and practices can be aligned. At IBM, the AI Ethics Board is comprised of diverse leaders from across the business. It provides a centralized governance, review, and decision-making process for IBM ethics policies and practices. While values and principles are crucial to establishing a basis for any ethical AI framework, recent movements in AI ethics have emphasised the need to move beyond high-level principles and toward practical strategies.

Fair, user-centered and accessible AI products can speed up processes in various industries and simplify many tasks for consumers. As a result, companies that do follow AI ethics can create technologies that enhance the quality of life for diverse groups and society as a whole. And if a team doesn’t take the time to understand its AI product before releasing it, engineers and other personnel may not be able to explain decisions AI makes, is ai ethical reduce bias and fix other errors. These mistakes can further weaken a company’s credibility and transparency, making it much more difficult to regain the public’s trust moving forward. In what follows I define the most-mentioned AI ethics principles (Sects. 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, and 4.7) in terms of power. By doing so, I show that the fundamental concerns that underly these principles are emancipation, empowerment, or both.

So, the important question is whether their human users will employ ethical principles focused primarily on the public good. Just like now, most users of AI systems will be for-profit corporations, and just like now, they will be focused on profit rather than social good. These AI systems will certainly enable corporations to do a much better job of extracting profit, likely with a corresponding decrease in public good, unless the public itself takes action to better align the profit-interests of these corporations with the public good.

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For instance, choosing over the death of car occupants, pedestrians, or occupants of other vehicles, et cetera. While such extreme situations may be a simplification of reality, one cannot exclude that the algorithms driving an autonomous-vehicle may find themselves in circumstances where their decisions may result in harming some of the involved parties (Bonnefon et al., 2019). Higher transparency is a common refrain when discussing ethics of algorithms, in relation to dimensions such as how an algorithmic decision is arrived at, based on what assumptions, and how this could be corrected to incorporate feedback from the involved parties.