AI News, Preliminary study on the Ethics of Artificial Intelligence artificial intelligence

Introduction

Introduction Much has been written about the ways in which artificial intelligence (AI) systems have a part to play in our societies, today and in the future.

Another possible explanation is that there is no true understanding of why and how some systems are flawed – some algorithms are inherently inscrutable and opaque,[2] and/or operate on spurious correlations that make no sense to an observer.[3] But there is a fourth cross-cutting explanation that concerns the global power relations in which these systems are built.

Given the subjectivity that pervades this field, we focus on jurisdictions that have been hitherto excluded from mainstream conversations and deliberations around this technology, in the hope that we can work towards a well-informed, nuanced and truly global conversation.

The need to address the imbalance in the global narrative Over 60 years after the term was officially coined, AI is firmly embedded in the fabric of our public and private lives in a variety of ways: from deciding our creditworthiness,[4] to flagging problematic content online,[5] from diagnosis in health care,[6] to assisting law enforcement with the maintenance of law and order.[7] AI systems today use statistical methods to learn from data, and are used primarily for prediction, classification, and identification of patterns.

AI is broadly defined as the ability of computers to exhibit intelligent behavior.[8] Much of what is referred to as “AI” in popular media is one particular technique that has garnered significant attention in the last few years – machine learning (ML).

As the name suggests, ML is the process by which an algorithm learns and improves performance over time by gaining greater access to data.[9] Given the ability of ML systems to operate at scale and produce data-driven insights, there has been an aggressive embracing of its ability to solve problems and predict outcomes.

While the expected potential public benefits of ML are often conjectural, as this GISWatch shows, its tangible impact on rights is becoming increasingly clear across the world.[10] Yet a historical understanding of AI and its development leads to a systemic approach to explanation and mitigation of its negative impact.

It is not simply a matter of ensuring accuracy and perfection in a technical system, but rather a reckoning with the fundamentally imperfect, discriminatory and unfair world from which these systems arise, and the underlying structural and historical legacy in which these systems are applied.

While on one end, AI is seen as a silver bullet technical solution to complex societal problems,[11] on the other, images of sex robots and superintelligent systems treating humans like “housecats” have been conjured.[12] Global deliberations are also lacking in “global” perspectives.

Complexity of governance frameworks and form Given the increasingly consequential impact that AI has in societies across the world, there has been a significant push towards articulating the ways in which these systems will be governed, with various frameworks of reference coming to the fore.

The extent to which existing regulations in national, regional and international contexts apply to these technologies is unclear, although a closer analysis of data protection regulation,[14] discrimination law[15] and labour law[16] is necessary.  There has been a significant push towards critiquing and regulating these systems on the basis of international human rights standards.[17] Given the impact on privacy, freedom of expression and freedom of assembly, among others, the human rights framework is a minimum requirement to which AI systems must adhere.[18] This can be done by conducting thorough human rights impact assessments of systems prior to deployment,[19] including assessing the legality of these systems against human rights standards, and by industry affirming commitment to the United Nations Guiding Principles on Business and Human Rights.[20] Social justice is another dominant lens through which AI systems are understood and critiqued.

While human rights provide an important minimum requirement for AI systems to adhere to, an ongoing critique of human rights is that they are “focused on securing enough for everyone, are essential – but they are not enough.”[21] Social justice advocates are concerned that people are treated in ways consistent with ideals of fairness, accountability, transparency,[22] inclusion, and are free from bias and discrimination.

third strand of governance emerges from a development perspective, to have the United Nations' (UN) Sustainable Development Goals (SDGs) guide responsible AI deployment (and in turn use AI to achieve the SDGs),[24] and to leverage AI for economic growth, particularly in countries where technological progress is synonymous with economic progress.

There is a pervasive anxiety among countries that they will miss the AI bus, and in turn give up the chance to have unprecedented economic and commercial gain, to “exploit the innovative potential of AI.”[25] The form these various governance frameworks take also varies.

Multiple UN mechanisms are currently studying the implications of AI from a human rights and development perspective, including but not limited to the High-level Panel on Digital Cooperation,[26] the Human Rights Council,[27] UNESCO’s World Commission on the Ethics of Scientific Knowledge and Technology,[28] and also the International Telecommunication Union’s AI for Good Summit.[29] Regional bodies like the European Union High-Level Expert Group on Artificial Intelligence[30] also focus on questions of human rights and principles of social justice like fairness, accountability, bias and exclusion.

The problem that an algorithm should solve, the data that an algorithm is exposed to, the training that an algorithm goes through, who gets to design and oversee the algorithm’s training, the context within which an algorithmic system is built, the context within which an algorithm is deployed, and the ways in which the algorithmic system’s findings are applied in imperfect and unequal societies are all political decisions taken by humans.

This link between private companies and public function power was most visibly called out through the #TechWontBuildIt movement, where engineers at the largest technology companies refused to build problematic technology that would be used by governments to undermine human rights and dignity.[34] The design and development of AI systems is also concentrated in large companies (mostly from the United States and increasingly from China).[35] However, deployment of technology is often imposed on jurisdictions in the global South, either on the pretext of pilot projects,[36] or economic development[37] and progress.

It is a South that also exists in the geographic North (Europe and North America), in the form of excluded, silenced and marginalised populations, such as undocumented immigrants, the unemployed, ethnic or religious minorities, and victims of sexism, homophobia, racism and Islamophobia.[38] The “global South” is thus dispersed across geography, demographics and opportunity.

Startup developing AI for TB detection; secures federal business innovation grant

IMAGE:Diascopic's iON platform detects the tuberculosis bacterium digitally in less than 60 seconds from a single sample.

Credit: Diascopic, LLC Cleveland--Diascopic LLC, a Cleveland-based medical research company that develops diagnostic technology, will use a highly competitive federal grant to develop and apply new artificial intelligence (AI) and digital pathology tools for detecting tuberculosis (TB).

Company principals Cary Serif, chief executive officer, and Jim Uhlir, vice president of research and engineering, are focused on rapid, mobile, low-cost and accurate digital pathology solutions for pressing health problems.

Diascopic combines low-magnification, high-resolution imaging with digital-analysis software for a portable, simple and flexible digital diagnostic platform that allows for immediate inspection of microscopic specimens.

Unlike traditional methods for TB diagnosis--which require highly trained technicians, laboratory equipment and several hours to several days--Diascopic's iON platform detects the TB bacterium digitally in less than 60 seconds from a single sample.

'Just as importantly, the digitization allows us to build a massive reference library to which we can apply artificial intelligence and data analytics to continue improving the test's accuracy.'

The company seeks to reduce the cost and technical skill of diagnostic tests while increasing the speed and accuracy of diagnosis, in order to allow for near point-of-care results in high-burden, low-resource environments.

Maxim Fedorov Takes Part in a Debate on the Ethics of AI at the UNESCO General Conference

The program document titled, “Preliminary study on a possible standard-setting instrument on the ethics of artificial intelligence,” was the main topic of debate.

The delegates highlighted the need for the consultation process to include all interested parties, particularly civil society organizations.

The delegates also placed emphasis on considering developing countries in order to avoid inequalities, and called for a human rights based approach alignment with ongoing initiatives on the ethics of artificial intelligence conducted by other international bodies.

AI Ethics 관련 세계적 동향 및 대응 방안

발표자: 김종욱 (동아대 교수) 발표월: 2019.7. 더욱 다양한 영상을 보시려면 NAVER Engineering TV를 참고하세요. ○ 개요 최근..

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