For my next Data Management class.
Solidifying Absolute and Relative Data Quality with Master Data Management
Contrary to popular belief, data are not the oil, fuel, energy, or life force coursing through the enterprise to inform decision-making, engender insights, and propel timely business action rooted in concrete facts.
Data quality is.
Without data quality, data science, big data, master data, and even metadata are all useless. Ensuring data quality is foundational to reducing risks and reaping any benefit from data themselves.
According to Profisee MDM Strategist Bill O’Kane, however, doing so is more difficult than it looks because there’s “absolute data quality and relative data quality. It could just be bad, unpopulated, mispopulated data.” Such common occurrences result in absolute data quality problems.
...but look at all the money we’re saving!
https://www.engadget.com/facial-recognition-failures-id-me-unemployment-benefits-172654494.html
Facial recognition systems are denying unemployment benefits across the US
A recent string of problems suggests facial recognition's reliability issues are hurting people in a moment of need. Motherboard reports that there are ongoing complaints about the ID.me facial recognition system at least 21 states use to verify people seeking unemployment benefits. People have gone weeks or months without benefits when the Face Match system doesn't verify their identities, and have sometimes had no luck getting help through a video chat system meant to solve these problems.
ID.me chief Blake Hall blamed the problems on users rather than the technology. Face Match algorithms have "99.9% efficacy," he said, and there was "no relationship" between skin tone and recognition failures. Hall instead suggested that people weren't sharing selfies properly or otherwise weren't following instructions.
(Related)
https://cadmus.eui.eu/handle/1814/71647
Development or dystopia? : an introduction to the accountability challenges of data processing by facial recognition technology
In parallel with the increasing deployment of Facial Recognition Technology (FRT), scholars are starting to pay attention to the inherent legal issues. We can identify a wide body of literature pointing out fairness concerns regarding the training and performance of these systems. Moreover, there is a scholarship stream that focuses on the threat that FRT might pose for the right to privacy in general and to data protection, in particular. However, most of these works do not provide an overview of the technology to prevent misunderstandings about how FRT works and what it can and cannot achieve. This analysis is necessary because it will inform a realistic and constructive debate around the legal implications of FRT deployment. For instance, the lack of consensus among sources when defining this technology, both from hard and soft law instruments, might reflect that different and separate stages of the actual process, or different functions, are being taken as FRT. Furthermore, although accountability has been spotted as a key feature to address the above mentioned fairness or privacy issues, there are no studies on how, and on what points through the FRT pipeline, accountability might arise. This results in positions ranging from risk assessment to extreme solutions such as banning.
How engineers view the issues surrounding AI?
https://ieeexplore.ieee.org/abstract/document/9456823
White Paper - IEEE Federated Machine Learning
Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements, such as the European Union’s General Data Protection Regulation. Many routine operations in big data applications, such as merging user data from various sources in order to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning is to provide a feasible solution that enables machine learning applications to utilize the data in a distributed manner that does not exchange raw data directly and does not allow any party to infer private information of other parties. This white paper intends to present an overview of the Federated Machine Learning (FML) technology that can be used as a basis for standards, certifications, laws, policies, and/or product ratings. This white paper targets an educated audience, including lawmakers, corporate and governmental policy makers, manufacturers, engineers, and standard setting bodies. However, this white paper is also easily understood by non-technical managers and policy makers as it provides system developers and manufacturers with an overview of Federated Machine Learning techniques.
(Related)
https://arxiv.org/abs/2106.08258
Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems
Artificial Intelligence (AI) systems are being deployed around the globe in critical fields such as healthcare and education. In some cases, expert practitioners in these domains are being tasked with introducing or using such systems, but have little or no insight into what data these complex systems are based on, or how they are put together. In this paper, we consider an AI system from the domain practitioner's perspective and identify key roles that are involved in system deployment. We consider the differing requirements and responsibilities of each role, and identify a tension between transparency and privacy that needs to be addressed so that domain practitioners are able to intelligently assess whether a particular AI system is appropriate for use in their domain.
Ethics vs ethics?
https://philpapers.org/rec/PETEGF
Ethical guidelines for the use of artificial intelligence and the challenges from value conflicts
The aim of this article is to articulate and critically discuss different answers to the following question: How should decision-makers deal with conflicts that arise when the values usually entailed in ethical guidelines – such as accuracy, privacy, non-discrimination and transparency – for the use of Artificial Intelligence clash with one another? To begin with, I focus on clarifying some of the general advantages of using such guidelines in an ethical analysis of the use of AI. Some disadvantages will also be presented and critically discussed. Second, I will show that we need to distinguish between three kinds of conflict that can exist for ethical guidelines used in the moral assessment of AI. This section will be followed by a critical discussion of different answers to the question of how to handle what we shall call internal and external values conflicts. Finally, I will wrap up with a critical discussion of three different strategies to resolve what is called a ‘genuine value conflict’. These strategies are: the ‘accepting the existence of irresolvable conflict’ view, the ranking view, and value monism. This article defends the ‘accepting the existence of irresolvable conflict’ view. It also argues that even though the ranking view and value monism, from a merely theoretical point of view, are better equipped to solve genuine value conflicts among values in ethical guidelines for artificial intelligence, this is not the case in real-life decision-making.
So tell me how to make money with AI…
http://www.jital.org/index.php/jital/article/view/223
Investigating the Socio-Economic Consequences of Artificial Intelligence: A Qualitative Research
It is without any doubt that artificial intelligence (AI) is set to radically disrupt humankind in various dimensions. The global economy is one of the dimensions in which AI is about to have an unsettling effect. Furthermore, AI is expected to change our societal structures in an unprecedented way. It might even change how humanity views concepts such as labour, education, and governance. However, there are conflicting predictions about those effects. Some scholars predict positive socio-economic effects. Conversely, other scholars remain fairly pessimistic. This research attempts to reconcile the gap between AI hype and AI reality by offering insight on AI’s potential impact on issues such as employment and economic growth and to highlight the ethical issues that already raise concerns for the future. This article determines the dimensions in which AI is about to have the most disruptive effect in a socioeconomic context. Semi-structured interviews with seasoned industry experts, academicians, and futurists with extensive knowledge of AI have been carried out for this purpose.
Tools & Techniques.
Facebook AI Open Sources AugLy: A New Python Library For Data Augmentation To Develop Robust Machine Learning Models
Github: https://github.com/facebookresearch/AugLy
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