Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. When discussing the threats of artificial intelligence, the first thing that comes to mind are images of Skynet, The Matrix, and the robot apocalypse. The runner up is technological unemployment, the vision of a foreseeable future in which AI algorithms take over all jobs and push humans into a struggle for meaningless survival in a world where human labor is no longer needed. Whether any or both of those threats are real is hotly debated among scientists and thought leaders. But AI algorithms also pose more imminent threats that exist today, in ways that are less conspicuous and hardly understood.

The US is using methods of violence against domestic protests it has repeatedly used in its imperial adventures abroad. As the world was gripped by the shocking scenes of police brutality against the Black community in the United States and the aggressive posture adopted by President Donald Trump against the protestors, an important development was missed by many observers. On May 29, the US Customs and Border Protection (CBP) agency flew a Predator drone, the machine used to kill suspected terrorists around the world, over the protestors in Minneapolis. The use of the drone led to immediate condemnations from civil rights groups on the ground, as the city of Minneapolis lies outside the 100-air-mile border zone where the CBP has jurisdiction. The incident is significant because it reflects the willingness of the US authorities to use technology developed to propagate imperial designs abroad against their own citizens.

Amazon is implementing a one-year moratorium on police use of its artificial intelligence software Rekognition amid a growing backlash over the tech company's ties to law enforcement. The company has recently stated its support for the Black Lives Matter movement, which advocates for police reform – using Twitter to call for an end to "the inequitable and brutal treatment of black people" in the US and has putting a "Black lives matter" banner at the top of its home page. But the company has been criticized as hypocritical because it sells its facial recognition software to police forces. Amazon has not said how many police forces use the technology, or how it is used, but marketing materials have promoted Rekognition being used in conjunction with police body cameras in real time. When it was first released, Amazon's Rekognition software was criticized by human rights groups as "a powerful surveillance system" that is available to "violate rights and target communities of color".

Existing methods for reducing disparate performance of a classifier across different demographic groups assume that one has access to a large data set, thereby focusing on the algorithmic aspect of optimizing overall performance subject to additional constraints. However, poor data collection and imbalanced data sets can severely affect the quality of these methods. In this work, we consider a setting where data collection and optimization are performed simultaneously. In such a scenario, a natural strategy to mitigate the performance difference of the classifier is to provide additional training data drawn from the demographic groups that are worse off. In this paper, we propose to consistently follow this strategy throughout the whole training process and to guide the resulting classifier towards equal performance on the different groups by adaptively sampling each data point from the group that is currently disadvantaged. We provide a rigorous theoretical analysis of our approach in a simplified one-dimensional setting and an extensive experimental evaluation on numerous real-world data sets, including a case study on the data collected during the Flint water crisis.

Online hate speech is a newborn problem in our modern society which is growing at a steady rate exploiting weaknesses of the corresponding regimes that characterise several social media platforms. Therefore, this phenomenon is mainly cultivated through such comments, either during users' interaction or on posted multimedia context. Nowadays, giant companies own platforms where many millions of users log in daily. Thus, protection of their users from exposure to similar phenomena for keeping up with the corresponding law, as well as for retaining a high quality of offered services, seems mandatory. Having a robust and reliable mechanism for identifying and preventing the uploading of related material would have a huge effect on our society regarding several aspects of our daily life. On the other hand, its absence would deteriorate heavily the total user experience, while its erroneous operation might raise several ethical issues. In this work, we present a protocol for creating a more suitable dataset, regarding its both informativeness and representativeness aspects, favouring the safer capture of hate speech occurrence, without at the same time restricting its applicability to other classification problems. Moreover, we produce and publish a textual dataset with two variants: binary and multi-label, called `ETHOS', based on YouTube and Reddit comments validated through figure-eight crowdsourcing platform. Our assumption about the production of more compatible datasets is further investigated by applying various classification models and recording their behaviour over several appropriate metrics.

In this article, we propose a new variational approach to learn private and/or fair representations. This approach is based on the Lagrangians of a new formulation of the privacy and fairness optimization problems that we propose. In this formulation, we aim at generating representations of the data that keep a prescribed level of the relevant information that is not shared by the private or sensitive data, while minimizing the remaining information they keep. The proposed approach (i) exhibits the similarities of the privacy and fairness problems, (ii) allows us to control the trade-off between utility and privacy or fairness through the Lagrange multiplier parameter, and (iii) can be comfortably incorporated to common representation learning algorithms such as the VAE, the $\beta$-VAE, the VIB, or the nonlinear IB.

The news: IBM has said the company will stop developing or selling facial recognition software due to concerns the technology is used to promote racism. In a letter to Congress, IBM's CEO Arvind Krishna said the tech giant opposes any technology used "for mass surveillance, racial profiling, violations of basic human rights and freedoms." He called for a "national dialogue" on whether and how it is appropriate for facial recognition technology to be used by domestic law enforcement agencies. The letter also called for new federal rules to crack down on police misconduct, and more training and education for in-demand skills to improve economic opportunities for people of color. Not a new concern: Activists and experts have been pointing out for years that facial recognition systems are biased, and flagging concerns about its potential for abuse.

Reddit has announced its replacement for Alexis Ohanian, who stepped down from the company's board of directors last week. Ohanian said he wanted his position filled by a black candidate. That person is Michael Seibel, the CEO of startup accelerator Y Combinator. Seibel was also the founder of Justin.tv, He was Y Combinator's first black partner before he became CEO, and has supported donating to causes that promote the Black Lives Matter movement.

To some in the tech industry, facial recognition increasingly looks like toxic technology. IBM is the latest company to declare facial recognition too troubling. CEO Arvind Krishna told members of Congress Monday that IBM would no longer offer the technology, citing the potential for racial profiling and human rights abuse. In a letter, Krishna also called for police reforms aimed at increasing scrutiny and accountability for misconduct. "We believe now is the time to begin a national dialogue on whether and how facial recognition technology should be employed by domestic law enforcement agencies," wrote Krishna, the first non-white CEO in the company's 109-year history.

Artificial intelligence (AI) and Machine Learning(ML) is becoming increasingly important for mobility. That is why Continental has now developed a code of ethics for AI/ML. It applies to all Continental locations worldwide and serves as a guide for all collaboration partners of the company. "Artificial intelligence can and must only be programmed and used in accordance with clear ethical principles," explains Dirk Abendroth, chief technology officer of Continental Automotive. "Smart algorithms play a huge role in the automotive industry, such as in the case of autonomous driving. As a technology company, we are responsible for ensuring that all our product developments and internal processes are in keeping with ethical standards. This is why AI-based decision-making must always be nondiscriminatory."