As AI systems are moving from being perceived as a tool to being perceived as autonomous agents and team-mates, an important focus of research and development is understanding the ethical impact of these systems. The above considerations show that ethics and AI are related at several levels: Ethics by Design: the technical/algorithmic integration of ethical reasoning capabilities as part of the behaviour of artificial autonomous system; Ethics in Design: the regulatory and engineering methods that support the analysis and evaluation of the ethical implications of AI systems as these integrate or replace traditional social structures; Ethics for Design: the codes of conduct, standards and certification processes that ensure the integrity of developers and users as they research, design, construct, employ and manage artificial intelligent systems. In "Patiency Is Not a Virtue: The Design of Intelligent Systems and Systems of Ethics", Joanna Bryson contends that the place of AI in society is a matter of normative, rather than descriptive ethics. Finally, the paper "The Big Red Button Is Too Late: An Alternative Model for the Ethical Evaluation of AI Systems", by Thomas Arnold and Matthias Scheutz presents existing proposals for an emergency button in AI systems, and discuss the viability of emergency stop mechanisms that enable human operators to interrupt or divert a system while preventing the system from learning that such an intervention is a threat to its own existence.

Questions of algorithm validation, interoperability, translation of bias, security, and patient privacy protections abound. Section snippets Bias and the black box effect The confound of selection bias in datasets used to develop AI algorithms is common. These include developing image reconstruction and automated image annotation methods that limit the effect of human bias, ways to penetrate the "Black box" of machine learning algorithms, and safe, validated ways to de-identify patient data for large-scale Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors Declaration of Competing Interest None. Health information ownership: legal theories and policy implications Vand.

Setting the future of digital and social media marketing research: Perspectives and research propositions International Journal of Information Management Y.K. Dwivedi et al. Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda International Journal of Information Management S. Du et al. The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions International Journal of Information Management C. Barlow Oncology research: Clinical trial management systems, electronic medical record, and artificial intelligence Seminars in Oncology Nursing S. Akter et al. Conversational commerce: entering the next stage of AI-powered digital assistants Annals of Operations Research Y. Benkler The wealth of networks: How social production transforms markets and freedom Berger, S., Denner, M.-S., & Roeglinger, M. The nature of digital technologies-development of a multi-layer... Berryhill, J., Heang, K. K., Clogher, R., & McBride, K. Hello, World! Artificial Intelligence and its Use in... L. Bornmann What is societal impact of research and how can it be assessed? A literature survey Journal of the American Society for Information Science and Technology N. Bostrom et al.

The debate surrounding the ethics and legality of AI is growing, with concerns being raised about both areas. Although the two are separate issues and fields of knowledge, they often get mixed and confused in discussions. Ethical considerations focus on the idea and content of ethics, while the functional aspect is concerned with the relationship between law and ethics. Juridical analysis uses a non-formalistic scientific methodology to define the legal paradigm of AI, taking into account its nature and characteristics. Two key issues in this regard include the relationship between human and artificial intelligence and the question of the unitary or diverse nature of AI. Based on this theoretical and practical foundation, the study of the legal system examines its foundations, governance model, and regulatory bases. International Law is identified as the main legal framework for the regulation of AI. In conclusion, the debate around the ethics and legality of AI is complex, with various issues to consider. However, a clear legal paradigm needs to be established to regulate AI, taking into account its unique nature and characteristics. The role of International Law in this regard highlights the need for a global approach to regulating AI, ensuring consistency and uniformity across different jurisdictions.

Winfield & Jirotka [9 ] specifically consider the role of technical standards in the ethical and agile governance of robotics and AI systems. Various academics expertly questioned the imaginaries underlying data-driven technologies like AI [21 ] in current debates and highlighted the risks of the use of AI systems [22 -24 ]. More work needs to be done to apply these critical lenses to the ethical, legal and technical solutions proposed for AI governance. To situate the various articles, a brief overview of recent developments in AI governance and how agendas for defining AI regulation, ethical frameworks and technical approaches are set, will be given. There are clear advantages to having open norm-setting venues that aim to address AI governance by developing technical standards, ethical principles and professional codes of conducts.

Finally, anticipating potential ethical pitfalls, identifying possible solutions, and offering policy recommendations will be of benefit to physicians adopting AI technology in their practice as well as the patients who receive their care. There is benefit to swiftly integrating AI technology into the health care system, as AI poses the opportunity to improve the efficiency of health care delivery and quality of patient care. Finally, in responding to a case that considers the use of an artificially intelligent robot during surgery, Daniel Schiff and Jason Borenstein affirm the importance of proper informed consent and responsible use of AI technology, stressing that the potential harms related to the use of AI technology must be transparent to all involved. Finally, Elliott Crigger and Christopher Khoury report on the American Medical Association's recent adoption of policy on AI in health care , which calls for the development of thoughtfully designed, high-quality, and clinically validated AI technology, which can serve as a prototypical policy for the medical system.

A has Confidence in B to do X One component of trust is that there is typically a confidence placed in the trustee to do somethingFootnote 3 : A trusts B to do X. For example, I trust my partner to be faithful to me; or I trust my friend to keep my secret. If one only focuses on reliability, then in certain situations we may not be able to trust; for example, establishing amnesties, peace treaties, and agreements with those who have broken our trust in the past: 'We can see that knowledge of others' reliability is not necessary for trust by the fact that we can place trust in someone with an indifferent record for reliability, or continue to place trust in others in the face of some past unreliability'. Multi-agent Systems, Trust, and AI It must be noted here that I am not excluding a trust directed towards individual human beings behind the development, deployment, and integration of AI, or the possibility of trusting the organisations developing, deploying and integrating AI. However, I am disputing the claim that the technology itself can be trusted or considered trustworthy. These mixed trust relationships, or multi-agent trust relationships, may take the form of trusting groups of individuals, organisations, and perhaps, AI technologies within that network of trust.

Standards often formalize ethical principles into a structure which could be used either to evaluate the level of compliance or, more usefully perhaps for ethical standards, to provide guidelines for designers on how to reduce the likelihood of ethical harms arising from their product or service. There is a growing consensus that near-future robots will, as a minimum, need to be designed to reflect the ethical and cultural norms of their users and societies [18 ,35 ], and an important consequence of ethical governance is that all robots and AIs should be designed as implicit ethical agents. Rather the opposite: robots and AIs that are explicit moral agents are likely to require a greater level of operational oversight given the consequences of such systems making the wrong ethical choice [59 ]. Explicitly, ethical machines remain, at present, the subject of basic research; if and when they become a practical reality, there is no doubt that radical new approaches to regulating such systems will be needed. What would we expect of robotics and AI companies or organizations who claim to practice ethical governance? As a starting point for discussion we propose five pillars of good ethical governance, as follows: - Publish an ethical code of conduct , so that everyone in the organization understands what is expected of them.

The fairness of the algorithm has been questioned in an investigative report, that examined a pool of cases where a recidivism score was attributed to >18,000 criminal defendants in Broward County, Florida and flagged up a potential racial bias in the application of the algorithm. The decision-making rests again on the assumptions the algorithm developers have adopted, e.g., on the relative importance of false positive and false negative to train his/her algorithm to attribute, e.g., a five/ten/twenty times higher weight for a false negative in comparison with a false positive. Decision-making-based algorithms rest inevitably on assumptions, even silent ones, such as the quality of data the algorithm is trained on, or the actual modelling relations adopted, with all the implied consequences. Tools for technically scrutinising the potential behaviour of an algorithm and its uncertainty already exist and could be included in the workflow of algorithm development.

The critical claim of artificial intelligence advocates is that there is no distinction between mind and machines and thus they argue that there are possibilities for machine ethics, just as human ethics. Unlike computer ethics, which has traditionally focused on ethical issues surrounding human use of machines, AI or machine ethics is concerned with the behaviour of machines towards human users and perhaps other machines as well, and the ethicality of these interactions. The ultimate goal of machine ethics, according to the AI scientists, is to create a machine that itself follows an ideal ethical principle or a set of principles; that is to say, it is guided by this principle or these principles in decisions it makes about possible courses of action it could takea. Second, if we are ascribing mind to machines, it gives rise to ethical issues regarding machines.