AI Policy Matters is a regular column in the ACM SIGAI AI Matters newsletter featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog. Facial recognition (FR) issues continue to appear in the news, as well as in scholarly journal articles, while FR systems are being banned and some research is shown to be bad science. AI system researchers who try to associate facial technology output with human characteristics are sometimes referred to as machine-assisted phrenologists. Problems with FR research have been demonstrated in machine learning research such as work by Steed and Caliskan in "A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild." Meanwhile many examples of harmful products and misuses have been identified in areas such as criminality, video interviewing, and many others. Some communities have considered bans.

A composition of 10,000 unique cat images generated by the artificial intelligence called GAN (generative adversarial network), no human was involved in the creation of these cats, a machine created them by machine learning and artificial intelligence algorithms. The owner will receive the full resolution at 10,000 x 10,000 pixels (100 megapixels) and can ask me to send the full resolution cats images.

A collage of 10,000 unique nonexistent persons generated by the artificial intelligence called GAN (generative adversarial network), no human was involved in the creation of these person images, a machine created them by machine learning and artificial intelligence algorithms. The owner will receive the full resolution at 10,000 x 10,000 pixels (100 megapixels) and can ask me to send the full resolution person images.

Do you find artificial intelligence and its capabilities fascinating? Then you'll want to check out these sci-fi novels with stories about robots and AI bots. The novels in this list include anti-hero tales about robot uprising and stories about AI bots co-existing with humans and falling in love with them too. Sci-fi AI novels offer an innovative and imaginative insight into this disruptive technology that we see around in our day-to-day lives. Every recommendation in this list is unique, which raises many questions in curious minds.

The United Nations should follow the European Commission in establishing a regulatory framework for artificial intelligence and biometrics to protect people subject to the technologies, build trust in their use and take the pressure off data scientists to constantly justify the ethics, writes Eleonore Fournier-Tombs of McGill University for The Conversation. The European Commission (EC) put forward proposals in April 2021 that seek to harmonize rules on artificial intelligence and create mechanisms which Fournier-Tombs likens to the process for seeking approval for a new drug. Developers of a new high-risk application of AI would have to submit it for regulatory approval. They would also have to provide details on how the models and data are used and how impacts on privacy or discrimination would be addressed. Areas of risk include biometric identification, categorization and evaluation of the eligibility of people for accessing welfare and services, including in emergency response situations.

Explainable AI (XAI) fully describes an AI model, its expected impact and any potential biases. It helps you understand the steps taken by an AI technique to arrive at a decision. In this article, we will take a look at XAI in detail and explore how you can implement it in your organisation. "About half (46%) of South African companies indicate that they are already implementing AI within their organisations." Why is explainable AI important for your business?

Lossy compression algorithms are typically designed to achieve the lowest possible distortion at a given bit rate. However, recent studies show that pursuing high perceptual quality would lead to increase of the lowest achievable distortion (e.g., MSE). This paper provides nontrivial results theoretically revealing that, \textit{1}) the cost of achieving perfect perception quality is exactly a doubling of the lowest achievable MSE distortion, \textit{2}) an optimal encoder for the "classic" rate-distortion problem is also optimal for the perceptual compression problem, \textit{3}) distortion loss is unnecessary for training a perceptual decoder. Further, we propose a novel training framework to achieve the lowest MSE distortion under perfect perception constraint at a given bit rate. This framework uses a GAN with discriminator conditioned on an MSE-optimized encoder, which is superior over the traditional framework using distortion plus adversarial loss. Experiments are provided to verify the theoretical finding and demonstrate the superiority of the proposed training framework.

Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences. In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. The SCM proposes that stereotypes can be understood along two primary dimensions: warmth and competence. We present a method for defining warmth and competence axes in semantic embedding space, and show that the four quadrants defined by this subspace accurately represent the warmth and competence concepts, according to annotated lexicons. We then apply our computational SCM model to textual stereotype data and show that it compares favourably with survey-based studies in the psychological literature. Furthermore, we explore various strategies to counter stereotypical beliefs with anti-stereotypes. It is known that countering stereotypes with anti-stereotypical examples is one of the most effective ways to reduce biased thinking, yet the problem of generating anti-stereotypes has not been previously studied. Thus, a better understanding of how to generate realistic and effective anti-stereotypes can contribute to addressing pressing societal concerns of stereotyping, prejudice, and discrimination.

We study the problem of estimating a rank-$1$ signal in the presence of rotationally invariant noise-a class of perturbations more general than Gaussian noise. Principal Component Analysis (PCA) provides a natural estimator, and sharp results on its performance have been obtained in the high-dimensional regime. Recently, an Approximate Message Passing (AMP) algorithm has been proposed as an alternative estimator with the potential to improve the accuracy of PCA. However, the existing analysis of AMP requires an initialization that is both correlated with the signal and independent of the noise, which is often unrealistic in practice. In this work, we combine the two methods, and propose to initialize AMP with PCA. Our main result is a rigorous asymptotic characterization of the performance of this estimator. Both the AMP algorithm and its analysis differ from those previously derived in the Gaussian setting: at every iteration, our AMP algorithm requires a specific term to account for PCA initialization, while in the Gaussian case, PCA initialization affects only the first iteration of AMP. The proof is based on a two-phase artificial AMP that first approximates the PCA estimator and then mimics the true AMP. Our numerical simulations show an excellent agreement between AMP results and theoretical predictions, and suggest an interesting open direction on achieving Bayes-optimal performance.

Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated sequence of tasks arising from the coupled population dynamics. By leveraging auto-curricula to induce a population of distinct emergent strategies, PB-MARL has achieved impressive success in tackling multi-agent tasks. Despite remarkable prior arts of distributed RL frameworks, PB-MARL poses new challenges for parallelizing the training frameworks due to the additional complexity of multiple nested workloads between sampling, training and evaluation involved with heterogeneous policy interactions. To solve these problems, we present MALib, a scalable and efficient computing framework for PB-MARL. Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms. Experiments on a series of complex tasks such as multi-agent Atari Games show that MALib achieves throughput higher than 40K FPS on a single machine with $32$ CPU cores; 5x speedup than RLlib and at least 3x speedup than OpenSpiel in multi-agent training tasks. MALib is publicly available at https://github.com/sjtu-marl/malib.