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Ai Law and Society

18According to this, it can be concluded that values and normativity can be found on both sides of the design process; that is, in the use of structurally distorted data retrieved by individuals and society, as well as in the design and development of applications and services. This raises complex but necessary questions about who should be held accountable for what in the autonomous systems applied in society. Artificial intelligence (AI) and robotics (intelligent) are changing our society. This poses legal, ethical, social and political challenges that are not yet clear at this stage. RAILS wants to address these challenges and actively shape the discussion on current and future national and international legal framework conditions for AI and robotics by identifying the need for regulatory action and developing concrete recommendations. 38An inevitable question on the subject of service developers learning from intrinsic, structural values and social conditions concerns the management of social prejudices: should they reproduce the world in its current state or how would we prefer the world? And who decides which future is most desirable?  [82] Data-dependent AI, which draws on concrete examples from human activities, can be understood as a mirror of social structures, raising questions of responsibility for those who develop the mirror, its reproductive and amplifying capabilities. There can be a number of algorithm-dependent situations in which these algorithms lead not only to automated decisions, but also to normative decisions. It is important to recognize that apps that use data from social contexts can not only produce “personalized” and individually relevant products and services, but may also contain a number of structural biases and imbalances that societies in general struggle with, such as inequality, injustice, discrimination, and racism. These can lead to normative questions for the design side, meaning that data-driven platforms or applications that use and automate self-learning technologies will ultimately face the normative question of what the application should or should not reproduce. And therefore be held responsible for the agency he represents when he interacts with a biased company and reproduces it. Conversely, this means that AI-based analytical methods can reveal biases in already present and historical decision-making that can at best be used as a detection tool, which in some cases can also be an unpleasant surprise. 3 Indeed, alongside society`s increasing use and dependence on AI and machine learning, there is a growing societal need to understand the potentially negative consequences and risks, how different interests and powers are distributed, and what kinds of legal and ethical frameworks, standards, certifications or procedural attitudes might become necessary.

The literature dealing with artificial intelligence, endowed with different levels of autonomy and ability to act, has a long tradition in formulating normative rules and principles. Perhaps the most famous are Isaac Asimov`s Three Laws of Robotics of 1942, followed later by a number of others in the field of robotics research.  [6] In previous years, any concern about regulation and ethics often referred to an imaginary and somewhat unspecified form of artificial intelligence that, based on its instinctive and analytical ability, could revolt against humanity. Today, such concerns are sometimes expressed in relation to potential and future superintelligence and the fear that technological progress could lead to evolutionary and self-improving artificial intelligence – a kind of “singularity” in which humanity as we know it is disappearing.  [7] 12Value-based discussions around machine learning and AI are often conducted in relation to “ethics,” as in the Ethically Aligned Design report published by the global technical organization IEEE.  [21] In this context, such discussions on the theme of “ethics” and artificial intelligence reflect a general understanding that we, as a society, need to reflect on values and norms in AI developments, as well as – and this understanding is gaining strength in the social science literature – what effects AI has on us, on society and values. Culture, power and possibilities reproduced and amplified by autonomous systems. Therefore, the use of the concept of “ethics” in contemporary discourse on AI governance can arguably be seen as a kind of proxy; that is, it represents a conceptual platform capable of meeting the different groups that develop these methods and technologies – that is, mathematicians and computer scientists – with the groups that market and implement them on the market, as well as the groups that study these methods and technologies and their role in society from a social and human point of view, Match.

to better understand their effects. Discussions about ethics in AI are likely to develop over time through more clearly defined concepts in the areas of regulation, industry standards, certifications, and a deeper analysis of culture, power, market theory, standards, and more. in the main areas of traditional scientific fields. For many years, legal sociologists have been studying legitimacy in relation to social norms, in accordance with the “social facts” of Émile Durkheim [22] or the “living law” of Eugen Erlich [23], the “Law in action” of Roscoe Pound [24], which sees social norms as an empirically measurable object, is structurally widely dispersed, but has not necessarily been formalized in terms of law “in books”.  [25] Based on social concerns about the importance of digital and increasingly autonomous technologies for law and society [3], this article describes some of the legal and societal challenges posed by the use of AI and machine learning. In particular, the main argument is the emphasis on normativity in design, social biases in autonomous and algorithmic systems, and difficulties in distributing responsibility and responsibility. Regarding the close relationship between accountability and transparency, the article proposes seven “nuances” or aspects of transparency proposed as a social contribution to the already existing concept of explainability in AI research (XAI).  [4] The objective of this article is therefore not primarily to clearly define what AI is from the point of view of computing, but to show the social meaning of a daily and applied AI practically from the point of view of social law and to highlight the need to keep society “informed”.  [5] This is crucial from the point of view of defining technological advances and applications that should be considered fair and normatively equitable – which should probably be seen as an ongoing assessment. In addition, and perhaps of particular socio-legal value, this is crucial, also from the point of view that self-learning and autonomous technologies that depend on data derived from human values, behaviors and social structures will confront and reproduce not only the balanced sides of humanity, but also the biased, distorted and discriminatory sides.

It`s a kind of mirror effect with great normative implications for designers and developers, which I`ll talk about in more detail below. 41The objective of this text was to contribute to a broad social orientation by describing some of the legal and normative challenges of AI. I drew on social law theory regarding growing concerns about the fairness, accountability, and transparency of applied AI and machine learning in society to highlight the need for AI research and development to keep society “informed” by leveraging knowledge from areas such as law and society.  [86] In particular, the argument focused on normativity in design, societal biases in autonomous and algorithmic systems, and difficulties in the allocation of responsibility and responsibility, particularly with respect to transparency issues. 1The field of artificial intelligence (AI), especially machine learning, has seen significant developments in recent years.  [2] The underlying technologies and methods are useful in a number of application areas and interactive spaces in markets and society, and are particularly useful in information-intensive and digitized environments. It can be used, for example, for automated differentiated pricing methods for hotel reservations and airline tickets, for targeted and personalized marketing online and in customer card systems, for individual relevance in search engines, music recommendation systems or to understand and respond in voice conversations. Our homes are increasingly equipped with self-learning thermostats, other “real estate technologies” and virtual assistants included in smart speakers. AI is also directly applied to real-life and death issues. Currently, self-driving cars and other vehicles with varying degrees of autonomy are being developed, as are AI-powered tools used for cancer diagnosis, predictive risk analysis by insurance companies and creditors, image recognition algorithms used in social media, police and security services, or for military purposes, such as drones designed for long-range warfare.

Contact: bolgado@uci.edu Benedict Salazar Olgado (Bono) is a PhD student in computer science at the University of California, Irvine. Bono, who is jointly advised by Dr. Geoffrey C. Bowker and Dr. Roderic Crooks, is largely located at the intersection of memory, technology, and documentary studies, particularly with respect to transitional justice and transnational technopolitics. He is currently associated with the UCI Steckler Center for Responsible, Ethical, and Accessible Technology (CREATe) and the Evoke Lab & Studio. Bono`s scholarship is based on his work as a community organizer and human rights activist, while criticizing and designing computerized archiving technologies to serve those fighting for a more just and humane society.