The advent of powerful prediction algorithms led to increased automation of high-stake decisions regarding the allocation of scarce resources such as government spending and welfare support. This automation bears the risk of perpetuating unwanted discrimination against vulnerable and historically disadvantaged groups. Research on algorithmic discrimination in computer science and other disciplines developed a plethora of fairness metrics to detect and correct discriminatory algorithms. Drawing on robust sociological and philosophical discourse on distributive justice, we identify the limitations and problematic implications of prominent fairness metrics. We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority. We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.

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Anja Kaspersen is a senior fellow at Carnegie Council. She is the former director of the United Nations for Disarmament Affairs in Geneva and deputy secretary general of the Conference on Disarmament. Previously, she held the role as the head of strategic engagement and new technologies at the International Committee of the Red Cross (ICRC). Prior to joining the ICRC she served as a senior director for geopolitics and international security and a member of the executive committee at the World Economic Forum. Kaspersen is a Norwegian diplomat, former businessperson and academic.

Companies across the globe are exposed to a variety of risks. While some of them can be identified and avoided through strategic planning, others can not be even tracked. One of these dangers is a product recall, which normally occurs after a product or a service has been released, thereby adding huge costs to the company and irreversible damages for many. Fujitsu, a Japanese firm, recently developed an AI system capable of highlighting irregularity in the product's appearance to detect associated issues at an earlier stage, thereby providing the chance to correct them before the product is released in the market. The AI technology will be used for image inspection, which will allow for the extremely detailed identification of a wide range of external abnormalities on manufactured objects, such as scratches and production errors.

On 21 April 2021, the European Commission proposed a new, transformative legal framework to govern the use of artificial intelligence (AI) in the European Union. The proposal adopts a risk-based approach whereby the uses of artificial intelligence are categorised and restricted according to whether they pose an unacceptable, high, or low risk to human safety and fundamental rights. The policy is widely considered to be one of the first of its kind in the world which would, if passed, have profound and far-reaching consequences for organisations that develop or use technologies incorporating artificial intelligence. The European Commission's proposal has been in the making since 2017, when EU legislators enacted a resolution and a report with recommendations to the Commission on Civil Law Rules on Robotics. In 2020, the European Commission published a white paper on artificial intelligence.

Conference Table 1 – Democracy and artificial intelligence: In the last decade, politically stable countries with a long-held and firm commitment to freedom of expression have witnessed their public debates plunge into an abyss. On social network platforms, anonymous and insidious mass manipulation techniques have corrupted public opinion. Democracy has spawned its opposite, electing leaders who pit themselves against the Democratic Rule of Law. At the center of the global revolution, shady strategies implemented by ultra-complex algorithms have learned how to exploit the fears and desires of crowds and individuals to engender fanaticism and irrationality. Politics has lost connection with the knowledge of factual truth, while obscurantist leaders strive to undermine fundamental rights, science, and human dignity.

Influenced by the field of Computer Vision, Generative Adversarial Networks (GANs) are often adopted for the audio domain using fixed-size two-dimensional spectrogram representations as the "image data". However, in the (musical) audio domain, it is often desired to generate output of variable duration. This paper presents VQCPC-GAN, an adversarial framework for synthesizing variable-length audio by exploiting Vector-Quantized Contrastive Predictive Coding (VQCPC). A sequence of VQCPC tokens extracted from real audio data serves as conditional input to a GAN architecture, providing step-wise time-dependent features of the generated content. The input noise z (characteristic in adversarial architectures) remains fixed over time, ensuring temporal consistency of global features. We evaluate the proposed model by comparing a diverse set of metrics against various strong baselines. Results show that, even though the baselines score best, VQCPC-GAN achieves comparable performance even when generating variable-length audio. Numerous sound examples are provided in the accompanying website, and we release the code for reproducibility.

We argue that the phenomena of distributed responsibility, induced acceptance, and acceptance through ignorance constitute instances of imperfect delegation when tasks are delegated to computationally-driven systems. Imperfect delegation challenges human accountability. We hold that both direct public accountability via public transparency and indirect public accountability via transparency to auditors in public organizations can be both instrumentally ethically valuable and required as a matter of deontology from the principle of democratic self-government. We analyze the regulatory content of 16 guideline documents about the use of AI in the public sector, by mapping their requirements to those of our philosophical account of accountability, and conclude that while some guidelines refer to processes that amount to auditing, it seems that the debate would benefit from more clarity about the nature of the entitlement of auditors and the goals of auditing, also in order to develop ethically meaningful standards with respect to which different forms of auditing can be evaluated and compared.

Last week, the United States Senate played host to a number of social media company VPs during hearings on the potential dangers presented by algorithmic bias and amplification. While that meeting almost immediately broke down into a partisan circus of grandstanding grievance airing, Democratic senators did manage to focus a bit on how these recommendation algorithms might contribute to the spread of online misinformation and extremist ideologies. The issues and pitfalls presented by social algorithms are well-known and have been well-documented. So, really, what are we going to do about it? "So I think in order to answer that question, there's something critical that needs to happen: we need more independent researchers being able to analyze platforms and their behavior," Dr. Brandie Nonnecke, Director of the CITRIS Policy Lab at UC Berkeley, told Engadget. Social media companies "know that they need to be more transparent in what's happening on their platforms, but I'm of the firm belief that, in order for that transparency to be genuine, there needs to be collaboration between the platforms and independent peer reviewed, empirical research."

Rachel Aviv describes the way Elizabeth Loftus's psychology research has established the fallibility of personal memory, and shows how her testimony in court has helped to exculpate innocent defendants ("Past Imperfect," April 5th). The fact that there is limited experimental evidence for the emergence of memories of trauma long after it occurs does not prove that such memories are a fiction, of course. The malleability of memory, which Loftus's research has demonstrated, suggests that it is just as likely that memories can be forgotten and later remembered as it is that they can be implanted or distorted. In Aviv's account, Loftus's repudiation of unconscious repressed memories comes across as motivated as much by personal bias as by anything else. When Aviv astutely notes that it's "hard to avoid the thought" that Loftus's career was "shaped by the slipperiness of [the] foundational memory" of her mother's tragic death, Loftus vehemently denies it.