Back in 2019, Ben Lorica and I wrote about deepfakes. Ben and I argued (in agreement with The Grugq and others in the infosec community) that the real danger wasn't "Deep Fakes." The real danger is cheap fakes, fakes that can be produced quickly, easily, in bulk, and at virtually no cost. Tactically, it makes little sense to spend money and time on expensive AI when people can be fooled in bulk much more cheaply. I don't know if The Grugq has changed his thinking, but there was an obvious problem with that argument.

Physicists are increasingly developing artificial intelligence and machine learning techniques to advance our understanding of the physical world but there is a rising concern about the bias in such systems and their wider impact on society at large. In 2011, during her undergraduate degree at Georgia Institute of Technology, Ghanaian-US computer scientist Joy Buolamwini discovered that getting a robot to play a simple game of peek-a-boo with her was impossible – the machine was incapable of seeing her dark-skinned face. Later, in 2015, as a Master's student at Massachusetts Institute of Technology's Media Lab working on a science–art project called Aspire Mirror, she had a similar issue with facial analysis software: it detected her face only when she wore a white mask. Buolamwini's curiosity led her to run one of her profile images across four facial recognition demos, which, she discovered, either couldn't identify a face at all or misgendered her – a bias that she refers to as the "coded gaze". She then decided to test 1270 faces of politicians from three African and three European countries, with different features, skin tones and gender, which became her Master's thesis project "Gender Shades: Intersectional accuracy disparities in commercial gender classification" (figure 1).

Fujitsu Ltd. on Thursday released a multilingual speech translation service that does not require users to operate devices by hand. The service is designed for settings in which multilingual communication is needed amid a rise in the domestic population of non-Japanese speakers, such as medical facilities. It automatically translates speech after identifying the voices and locations of users on the basis of sound picked up by directional microphones connected to tablet devices. Fujitsu said that the voice recognition is highly accurate thanks to technology limiting the effects of background noise. In addition to medical settings, the service is expected to be used at tourist sites.

The interaction patterns of employees in social and professional networks play an important role in the success of employees and organizations as a whole. However, in many fields there is a severe under-representation of minority groups; moreover, minority individuals may be segregated from the rest of the network or isolated from one another. While the problem of increasing the representation of minority groups in various fields has been well-studied, diver- sification in terms of numbers alone may not be sufficient: social relationships should also be considered. In this work, we consider the problem of assigning a set of employment candidates to positions in a social network so that diversity and overall fitness are maximized, and propose Fair Employee Assignment (FairEA), a novel algorithm for finding such a matching. The output from FairEA can be used as a benchmark by organizations wishing to evaluate their hiring and assignment practices. On real and synthetic networks, we demonstrate that FairEA does well at finding high-fitness, high-diversity matchings.

In AI and law, systems that are designed for decision support should be explainable when pursuing justice. In order for these systems to be fair and responsible, they should make correct decisions and make them using a sound and transparent rationale. In this paper, we introduce a knowledge-driven method for model-agnostic rationale evaluation using dedicated test cases, similar to unit-testing in professional software development. We apply this new method in a set of machine learning experiments aimed at extracting known knowledge structures from artificial datasets from fictional and non-fictional legal settings. We show that our method allows us to analyze the rationale of black-box machine learning systems by assessing which rationale elements are learned or not. Furthermore, we show that the rationale can be adjusted using tailor-made training data based on the results of the rationale evaluation.

The field of explainable AI (XAI) has quickly become a thriving and prolific community. However, a silent, recurrent and acknowledged issue in this area is the lack of consensus regarding its terminology. In particular, each new contribution seems to rely on its own (and often intuitive) version of terms like "explanation" and "interpretation". Such disarray encumbers the consolidation of advances in the field towards the fulfillment of scientific and regulatory demands e.g., when comparing methods or establishing their compliance with respect to biases and fairness constraints. We propose a theoretical framework that not only provides concrete definitions for these terms, but it also outlines all steps necessary to produce explanations and interpretations. The framework also allows for existing contributions to be re-contextualized such that their scope can be measured, thus making them comparable to other methods. We show that this framework is compliant with desiderata on explanations, on interpretability and on evaluation metrics. We present a use-case showing how the framework can be used to compare LIME, SHAP and MDNet, establishing their advantages and shortcomings. Finally, we discuss relevant trends in XAI as well as recommendations for future work, all from the standpoint of our framework.

AI may benefit or represent a threat to humanity in many ways in numerous fields such as education, environment, health, defense, transportation, space exploration and so on. To avoid potential drifts of AI and benefit as much as possible from its advantages, AI must be controlled by normative frameworks. Yet, setting legal norms is a difficult and time-consuming process. Therefore, ethics is seen as a convenient and acceptable alternative to laws, since conversely to laws, it is flexible, easy, and quickly adjustable, and less constraining than formal rules. The number of Ethics code that have been issued around the world demonstrates the need to regulate AI while avoiding formal legal constraint.

Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.

It has been predicted that if recent advancement in machine learning continue uninterrupted, human-level or even superintelligent Artificially Intelligent (AI) systems will be designed at some point in the near future [1]. Currently available (and near-term predicted) AI software is subhuman in its general intelligence capability but it is already capable of being hazardous in a number of narrow domains [2], mostly with regard to privacy, discrimination [3, 4], crime automation or armed conflict [5]. Superintelligent AI, predicted to be developed in the longer term, is widely anticipated [6] to be far more dangerous and is potentially capable of causing a lot of harm including an existential risk event for the humanity as a whole [7, 8]. Together the short-term and long-term concerns are known as AI Risk [9]. An infinite number of pathways exists to a state of the world in which a dangerous AI is unleashed [10].

Artificial intelligence (AI) techniques are used increasingly in many areas of applications, including banking and finance. They have several advantages over traditional statistical methods: i) ability to handle new data types such as text, audio, and images; ii) flexible models that yield excellent predictive performance; and iii) ability to automate many of the routine, and time-consuming, tasks in model development. However, the use of these algorithms also raise several challenges. A well-known problem is the opaqueness of ML models and the difficulties in understanding and interpreting the model results. In this paper, we focus on a related and equally important challenge: potential for bias and lack of fairness when using AI/ML techniques.