scholarly journals Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers

2021 ◽  
Vol 71 ◽  
Author(s):  
Baobao Zhang ◽  
Markus Anderljung ◽  
Lauren Kahn ◽  
Noemi Dreksler ◽  
Michael C. Horowitz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
International Governance ◽  
The Public ◽  
Scientific Organizations ◽  
Safety Research ◽  
The Us ◽  
Military Applications ◽  
Governance Strategies ◽  
Technical Solutions

Machine learning (ML) and artificial intelligence (AI) researchers play an important role in the ethics and governance of AI, including through their work, advocacy, and choice of employment. Nevertheless, this influential group's attitudes are not well understood, undermining our ability to discern consensuses or disagreements between AI/ML researchers. To examine these researchers' views, we conducted a survey of those who published in two top AI/ML conferences (N = 524). We compare these results with those from a 2016 survey of AI/ML researchers (Grace et al., 2018) and a 2018 survey of the US public (Zhang & Dafoe, 2020). We find that AI/ML researchers place high levels of trust in international organizations and scientific organizations to shape the development and use of AI in the public interest; moderate trust in most Western tech companies; and low trust in national militaries, Chinese tech companies, and Facebook. While the respondents were overwhelmingly opposed to AI/ML researchers working on lethal autonomous weapons, they are less opposed to researchers working on other military applications of AI, particularly logistics algorithms. A strong majority of respondents think that AI safety research should be prioritized and that ML institutions should conduct pre-publication review to assess potential harms. Being closer to the technology itself, AI/ML researchers are well placed to highlight new risks and develop technical solutions, so this novel attempt to measure their attitudes has broad relevance. The findings should help to improve how researchers, private sector executives, and policymakers think about regulations, governance frameworks, guiding principles, and national and international governance strategies for AI. This article appears in the special track on AI & Society.

scholarly journals Before and beyond trust: reliance in medical AI

2021 ◽  
pp. medethics-2020-107095
Author(s):  
Charalampia (Xaroula) Kerasidou ◽  
Angeliki Kerasidou ◽  
Monika Buscher ◽  
Stephen Wilkinson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New Technology ◽  
Public Trust ◽  
Learning Technologies ◽  
Data Driven ◽  
Policy Makers ◽  
The Public ◽  
Regulatory Frameworks ◽  
Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

scholarly journals How to translate artificial intelligence? Myths and justifications in public discourse

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172091996 ◽  
Author(s):  
Jonathan Roberge ◽  
Marius Senneville ◽  
Kevin Morin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Science And Technology Studies ◽  
Public Discourse ◽  
Social Expectations ◽  
Specific Language ◽  
The Public ◽  
Gradual Introduction ◽  
Congressional Hearing ◽  
The Many

Automated technologies populating today’s online world rely on social expectations about how “smart” they appear to be. Algorithmic processing, as well as bias and missteps in the course of their development, all come to shape a cultural realm that in turn determines what they come to be about. It is our contention that a robust analytical frame could be derived from culturally driven Science and Technology Studies while focusing on Callon’s concept of translation. Excitement and apprehensions must find a specific language to move past a state of latency. Translations are thus contextual and highly performative, transforming justifications into legitimate claims, translators into discursive entrepreneurs, and power relations into new forms of governance and governmentality. In this piece, we discuss three cases in which artificial intelligence was deciphered to the public: (i) the Montreal Declaration for a Responsible Development of Artificial Intelligence, held as a prime example of how stakeholders manage to establish the terms of the debate on ethical artificial intelligence while avoiding substantive commitment; (ii) Mark Zuckerberg’s 2018 congressional hearing, where he construed machine learning as the solution to the many problems the platform might encounter; and (iii) the normative renegotiations surrounding the gradual introduction of “killer robots” in military engagements. Of interest are not only the rational arguments put forward, but also the rhetorical maneuvers deployed. Through the examination of the ramifications of these translations, we intend to show how they are constructed in face of and in relation to forms of criticisms, thus revealing the highly cybernetic deployment of artificial intelligence technologies.

scholarly journals Review on Existing Fire Monitoring and Extinguishing Systems In Various Domain Verticals

2021 ◽  
Vol 23 (3) ◽  
Author(s):  
Naina Mahile ◽  
◽  
Dipali Chakole ◽  
Nikita Kotangale ◽  
Mitali Charde ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fire Safety ◽  
Public Awareness ◽  
Fire Control ◽  
Main Motivation ◽  
Fire Monitoring ◽  
The Public ◽  
Drastic Increase ◽  
Safety Systems

Fire is one of the most frequently occurring and destructive disasters and it is extremely serious hazard to people life safety. It is an undesirable mishap which emits heat, smoke or flame and gets converted in the huge fire. Over the last few years, the demand of fire safety systems has taken a drastic increase due to the public awareness. The main motivation of this paper is to review the existing fire monitoring and extinguishing systems in various verticals of the working domains. Also it gives the brief about the design of automatic sensor based fire alerts, and extinguishing system inferring the Artificial Intelligence and machine learning. The system will be able to locate the victim location and intimation to various stations to be included in the fire control the fire exposures. By implementing the proposed system in a particular area, it is possible to spot the fire within small course of time, and extinguish it without risking human lives.

scholarly journals ‘Ai Theory of Justice’: Using Rawlsian Approaches to Legislate Better on Machine Learning in Government

Amicus Curiae ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 338-360
Author(s):  
Jamie Grace ◽  
Roxanne Bamford
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
John Rawls ◽  
Data Analytics ◽  
The Public ◽  
Theory Of Justice ◽  
Big Data Technologies ◽  
Applications Of Machine Learning

Policymaking is increasingly being informed by ‘big data’ technologies of analytics, machine learning and artificial intelligence (AI). John Rawls used particular principles of reasoning in his 1971 book, A Theory of Justice, which might help explore known problems of data bias, unfairness, accountability and privacy, in relation to applications of machine learning and AI in government. This paper will investigate how the current assortment of UK governmental policy and regulatory developments around AI in the public sector could be said to meet, or not meet, these Rawlsian principles, and what we might do better by incorporating them when we respond legislatively to this ongoing challenge. This paper uses a case study of data analytics and machine-learning regulation as the central means of this exploration of Rawlsian thinking in relation to the redevelopment of algorithmic governance.

scholarly journals The "coming day" of digital government in the United States: a strategy for artificial intelligence

2020 ◽  
Vol 8 (1 SI) ◽  
pp. 103-106
Author(s):  
Oleksii Onufriienko
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Public Administration ◽  
Department Of Defense ◽  
Pilot Project ◽  
The United States ◽  
Digital Government ◽  
Public Governance ◽  
The Public ◽  
The Us

The US Department of Defense Artificial Intelligence Strategy (2018) as a pilot project of promising e-modernization of the public sector of this country is analyzed, its place among other initiatives on digitalization of public administration of the current US Presidential Administration is determined, its specific public-administrative logic is clarified. the specifics of this project through the prism of the tasks of modernization of public governance in transforming societies.

The Robots are Coming...But Aren't Here Yet: The Use of Artificial Intelligence Technologies in the Public Accounting Profession

2020 ◽  
Author(s):  
Kathleen M. Bakarich ◽  
Patrick O'Brien
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Large Scale ◽  
Process Automation ◽  
Public Accounting ◽  
Accounting Firms ◽  
The Public ◽  
Extensive Training ◽  
Qualitative Responses ◽  
The Impact

In this paper, we survey public accounting professionals to gauge the extent to which Artificial Intelligence (AI), specifically Robotic Process Automation (RPA) and Machine Learning (ML), are currently being utilized by the profession, as well as perceptions about the impact and receptiveness to this technology. Quantitative and qualitative responses from ninety participants, representing various firms, service lines, and positions, indicate that both RPA and ML are currently not being used extensively by public accountants nor by clients of public accounting firms, and firms are conducting some, but not extensive training on these technologies for employees. However, respondents strongly indicated that AI will significantly impact their daily responsibilities in five years and that employees in the profession are very receptive to these changes. Additionally, we find that firm size appears to be the most significant factor impacting differences in responses. These results indicate that while large-scale AI adoption has not yet come to public accounting, substantial changes are on the horizon.

Refusal to Mourn

2005 ◽  
Vol 1 (1) ◽  
pp. 9-45 ◽  
Author(s):  
Erin Runions
Keyword(s):  
American Politics ◽  
Psychoanalytic Theory ◽  
Civil Religion ◽  
Judith Butler ◽  
American People ◽  
Recent Book ◽  
The Public ◽  
American Civil Religion ◽  
The Us ◽  
Robert Bellah

In her recent book Precarious Life, Judith Butler points out that not more than ten days after 9/11, on 20 September 2001, George W. Bush urged the American people to put aside their grief; she suggests that such a refusal to mourn leads to a kind of national melancholia. Using psychoanalytic theory on melancholia, this article diagnoses causes and effects of such national melancholia. Further, it considers how a refusal to mourn in prophetic and apocalyptic texts and their interpretations operates within mainstream US American politics like the encrypted loss of the melancholic, thus creating the narcissism, guilt, and aggression that sustain the pervasive disavowal of loss in the contemporary moment. This article explore the ways in which the texts of Ezekiel, Micah, Revelation, and their interpreters exhibit the guilt and aggression of melancholia, in describing Israel as an unfaithful and wicked woman whose pain should not be mourned. These melancholic patterns are inherited by both by contemporary apocalyptic discourses and by the discourse of what Robert Bellah calls ‘American civil religion’, in which the US is the new Christian Israel; thus they help to position the public to accept and perpetuate the violence of war, and not to mourn it.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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