Being good is easy, what is difficult is being just. We need to defend the interests of those whom we've never met and never will. Note: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learning algorithms. As such, I have tried to explain concepts in an accessible way with minimal use of mathematics, in the hope that everyone can get something out of reading this. Supervised machine learning algorithms are inherently discriminatory. They are discriminatory in the sense that they use information embedded in the features of data to separate instances into distinct categories -- indeed, this is their designated purpose in life. This is reflected in the name for these algorithms which are often referred to as discriminative algorithms (splitting data into categories), in contrast to generative algorithms (generating data from a given category). When we use supervised machine learning, this "discrimination" is used as an aid to help us categorize our data into distinct categories within the data distribution, as illustrated below. Whilst this occurs when we apply discriminative algorithms -- such as support vector machines, forms of parametric regression (e.g. For example, using last week's weather data to try and predict the weather tomorrow has no moral valence attached to it.

Test, Evaluation, Verification, and Validation (TEVV) for Artificial Intelligence (AI) is a challenge that threatens to limit the economic and societal rewards that AI researchers have devoted themselves to producing. A central task of TEVV for AI is estimating brittleness, where brittleness implies that the system functions well within some bounds and poorly outside of those bounds. This paper argues that neither of those criteria are certain of Deep Neural Networks. First, highly touted AI successes (eg. image classification and speech recognition) are orders of magnitude more failure-prone than are typically certified in critical systems even within design bounds (perfectly in-distribution sampling). Second, performance falls off only gradually as inputs become further Out-Of-Distribution (OOD). Enhanced emphasis is needed on designing systems that are resilient despite failure-prone AI components as well as on evaluating and improving OOD performance in order to get AI to where it can clear the challenging hurdles of TEVV and certification.

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainable AI (XAI) -- arguably one of the biggest concerns today for the AI community. Work on explainable MR as well as on MR approaches to explainability in other areas of AI has continued ever since. It is especially potent in modern MR branches, such as argumentation, constraint and logic programming, planning. We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape. This document reports our work in-progress on MR explainability.

Vibraimage is a digital system that quantifies a subject's mental and emotional state by analysing video footage of the movements of their head. Vibraimage is used by police, nuclear power station operators, airport security and psychiatrists in Russia, China, Japan and South Korea, and has been deployed at an Olympic Games, FIFA World Cup, and G7 Summit. Yet there is no reliable evidence that the technology is actually effective; indeed, many claims made about its effects seem unprovable. What exactly does vibraimage measure, and how has it acquired the power to penetrate the highest profile and most sensitive security infrastructure across Russia and Asia? I first trace the development of the emotion recognition industry, before examining attempts by vibraimage's developers and affiliates scientifically to legitimate the technology, concluding that the disciplining power and corporate value of vibraimage is generated through its very opacity, in contrast to increasing demands across the social sciences for transparency. I propose the term 'suspect AI' to describe the growing number of systems like vibraimage that algorithmically classify suspects / non-suspects, yet are themselves deeply suspect. Popularising this term may help resist such technologies' reductivist approaches to 'reading' -- and exerting authority over -- emotion, intentionality and agency.

Routine drone deliveries to U.S. consumers are still years away, partly because the FAA needs to complete rules for remote identification of more than 400,000 drones currently registered for commercial operations, and issue separate rules permitting drones to fly regularly over populated areas. Despite the investments and interest in potential drone deliveries by startups as well as deep-pocketed early adapters such as Amazon, package deliveries won't proceed beyond limited trials in the U.S. until new federal regulations go into effect. Amazon has now joined United Parcel Service Inc. UPS 0.78% and Wing, a unit of Google parent Alphabet Inc., GOOG -0.62% in gaining approval to operate unmanned air fleets in the U.S. for tests involving customer deliveries. Amazon has sought regulatory approval for a broader range of drones and over a larger geographic area than its competitors. The company said Monday that the approval from the FAA isn't tied to a specific drone model but operations of a fleet.

At the beginning of 2021, the European Commission will propose legislation on AI that will be, at first instance, horizontal (as opposed to sectoral) and risk-based, with mandatory requirements for high-risk AI applications. The new rules will aim at ensuring transparency, accountability and consumer protection, including safety, through robust AI governance and data quality requirements. Europe's approach to regulating technology is based on the precautionary principle, which enables rapid regulatory intervention in the face of possible danger to human, animal or plant health, or to protect the environment. This perspective has helped Europe to become a global leader in the shaping of the digital technology market. Particularly, with the introduction of the General Data Protection Regulation (GDPR) in 2018, Europe considers it has gained a competitive advantage through the creation of a trust mark for increased privacy protection. Australia and New Zealand have a close relationship with the European Union (EU) and its member countries historically.

In April 2020, Amazon, the world's wealthiest technology company, received a shipment of 1,500 heat-mapping camera systems from the Chinese surveillance company Dahua. Many of these systems will be installed in Amazon warehouses to monitor the heat signatures of employees and alert managers if workers exhibit Covid-19-like symptoms. Other cameras included in the shipment will be distributed to IBM and Chrysler, among other buyers. While Amazon's move to protect workers from Covid-19 is welcome, it acquired this technology from a company from a company researchers have shown is involved in human rights abuses. As Sanjana Varghese noted recently, the "humanitarian experimentation" work in pandemic surveillance of companies like Dahua doubles as technologies of population management.

UK EDITION Ethics Guide to Artificial Intelligence in PR 2. The AIinPR panel and the authors are grateful for the endorsements and support from the following: In May 2020 the Wall Street Journal reported that 64 per cent of all signups to extremist groups on Facebook were due to Facebook's own recommendation algorithms. There could hardly be a simpler case study in the question of AI and ethics, the intersection of what is technically possible and what is morally desirable. CIPR members who find an automated/AI system used by their organisation perpetrating such online harms have a professional responsibility to try and prevent it. For all PR professionals, this is a fundamental requirement of the ability to practice ethically. The question is – if you worked at Facebook, what would you do? If you're not sure, this report guide will help you work out your answer. Alastair McCapra Chief Executive Officer CIPR Artificial Intelligence is quickly becoming an essential technology for ...

While there are many training programs in Nigeria related to developing AI talent, it is usually difficult to point out AI applications that are locally developed and are already available in the marketplace. This article will hopefully be the first of a series of articles; I will be uncovering several locally developed AI applications in Nigeria. Some you might know, some you won't. All in all, my goal in this series is to dispel beliefs that AI is an imported technology in Nigeria. Nigeria may not be pulling its weight compared to Ghana, South Africa, and Kenya in the African AI space, but there are notable strides that we should be aware of.

Rapid evolution in artificial intelligence (AI) applications, as well as improvements in computing power and the increasing availability of data, have led to significant growth in AI across most industries, write Leanne Mostert, a Partner and Wendy Tembedza, a Senior Associate at Webber Wentzel. The key developments in AI over the past few years have been driven by machine learning which, in turn, is fuelled by data. As more and more data is being gathered, so AI enables more sophisticated analysis of large data volumes. As the importance of data rises, so do the associated legal issues. In some cases businesses are free to use the data they hold for whatever purpose they want, including developing AI algorithms.