Any attempt to regulate artificial intelligence (AI) must not rely solely on technical measures to mitigate potential harms, and should instead move to address the fundamental power imbalances between those who develop or deploy the technology and those who are subject to it, says a report commissioned by European Digital Rights (EDRi). Published on 21 September 2021, the 155-page report Beyond debiasing: regulating AI and its inequalities specifically criticised the European Union's (EU) "technocratic" approach to AI regulation, which it said was too narrowly focused on implementing technical bias mitigation measures, otherwise known as "debiasing", to be effective at preventing the full range of AI-related harms. The European Commission's (EC) proposed Artificial Intelligence Act (AIA) was published in April 2021 and sought to create a risk-based, market-led approach to regulating AI through the establishment of self-assessments, transparency procedures and various technical standards. Digital civil rights experts and organisations have previously told Computer Weekly that although the regulation is a step in the right direction, it will ultimately fail to protect people's fundamental rights and mitigate the technology's worst abuses because it does not address the fundamental power imbalances between tech firms and those who are subject to their systems. The EDRi-commissioned report said that while European policymakers have publicly recognised that AI can produce a broad range of harms across different domains – including employment, housing, education, health and policing – their laser focus on algorithmic debiasing stems from a misunderstanding of the existing techniques and their effectiveness.

Earlier this month, the Equal Employment Opportunity Commission (EEOC) held a webinar on artificial intelligence (AI) in the workplace. Commissioner Keith Sonderling explained that the EEOC is monitoring employers' use of such technology in the workplace to ensure compliance with anti-discrimination laws. The agency recognizes the potential for AI to mitigate unlawful human bias, but is wary of rapid, undisciplined implementation that may perpetuate or accelerate such bias. Sonderling remarked that the EEOC may use Commissioner charges--agency-initiated investigations unconnected to an employee's charge of discrimination--to ensure employers' are not using AI in an unlawful manner, particularly under the rubric of disparate impact claims. The EEOC's interest in this topic is not new.

The pandemic time impacted many aspects of both our private and professional lives. Without any doubt we all faced significant challenges while required immediately to move fully into remote working. We stopped working from the office, meeting our colleagues and clients face to face and stopped travelling. That sudden shift changed not only the way we work, but also changed all our daily activities both business and private ones and significantly reduced our social interactions in the real-world, impacting also our mental sphere. And probably not everyone was taking enough care about the right work-life-balance and activities to keep both physical and mental health.

Analyzing the effect of business cycle on rating transitions has been a subject of great interest these last fifteen years, particularly due to the increasing pressure coming from regulators for stress testing. In this paper, we consider that the dynamics of rating migrations is governed by an unobserved latent factor. Under a point process filtering framework, we explain how the current state of the hidden factor can be efficiently inferred from observations of rating histories. We then adapt the classical Baum-Welsh algorithm to our setting and show how to estimate the latent factor parameters. Once calibrated, we may reveal and detect economic changes affecting the dynamics of rating migration, in real-time. To this end we adapt a filtering formula which can then be used for predicting future transition probabilities according to economic regimes without using any external covariates. We propose two filtering frameworks: a discrete and a continuous version. We demonstrate and compare the efficiency of both approaches on fictive data and on a corporate credit rating database. The methods could also be applied to retail credit loans.

There remain many open questions pertaining to the scaling behaviour of Transformer architectures. These scaling decisions and findings can be critical, as training runs often come with an associated computational cost which have both financial and/or environmental impact. The goal of this paper is to present scaling insights from pretraining and finetuning Transformers. While Kaplan et al. presents a comprehensive study of the scaling behaviour of Transformer language models, the scope is only on the upstream (pretraining) loss. Therefore, it is still unclear if these set of findings transfer to downstream task within the context of the pretrain-finetune paradigm. The key findings of this paper are as follows: (1) we show that aside from only the model size, model shape matters for downstream fine-tuning, (2) scaling protocols operate differently at different compute regions, (3) widely adopted T5-base and T5-large sizes are Pareto-inefficient. To this end, we present improved scaling protocols whereby our redesigned models achieve similar downstream fine-tuning quality while having 50\% fewer parameters and training 40\% faster compared to the widely adopted T5-base model. We publicly release over 100 pretrained checkpoints of different T5 configurations to facilitate future research and analysis.

Modern quantitative finance is based around the approach of pattern recognition in historical data. This approach requires teams of scientists to work in a collaborative and regulated setting in order to develop models that can be used to make trading predictions. With the growing influence of this field, both participants and regulators are looking to put in place mechanisms to understand how and why models have been developed, for reasons such as regulatory compliance and model reproducibility. We refer to this tractability problem as lineage. The challenge of reproducibility and lineage in machine learning (ML) is three-fold: code lineage, data lineage, and model lineage.

Activision Blizzard has confirmed an investigation by US regulators following allegations of sexual misconduct and discrimination at one of the world's most high-profile video game companies. The California-based company said on Tuesday that it was complying with a recent Securities and Exchange Commission subpoena sent to current and former employees and executives and the company itself on "employment matters and related issues". The Wall Street Journal had reported on Monday that the SEC was investigating how the company had treated complaints of sexual misconduct and workplace discrimination and had subpoenaed senior executives including the CEO, Bobby Kotick, a well-known tech billionaire. An SEC spokesman declined to comment. Activision Blizzard – the maker of popular video games including Candy Crush, Call of Duty, Overwatch and World of Warcraft – also said on Tuesday that it had cooperated with an Equal Employment Opportunity Commission investigation into employment practices and that it was working with multiple regulators "on addressing and resolving workplace complaints it has received" and that it was committed to making the company "one of the best, most inclusive places to work".

During the pandemic, technology companies have been pitching their emotion-recognition software for monitoring workers and even children remotely. Take, for example, a system named 4 Little Trees. Developed in Hong Kong, the program claims to assess children's emotions while they do classwork. It maps facial features to assign each pupil's emotional state into a category such as happiness, sadness, anger, disgust, surprise and fear. It also gauges'motivation' and forecasts grades.

AI systems are compute-intensive: the AI lifecycle often requires long-running training jobs, hyperparameter searches, inference jobs, and other costly computations. They also require massive amounts of data that might be moved over the wire, and require specialized hardware to operate effectively, especially large-scale AI systems. All of these activities require electricity -- which has a carbon cost. There are also carbon emissions in ancillary needs like hardware and datacenter cooling [1]. Thus, AI systems have a massive carbon footprint[2]. This carbon footprint also has consequences in terms of social justice as we will explore in this article.

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.