Fairness is crucial when training a deep-learning discriminative model, especially in the facial domain. Models tend to correlate specific characteristics (such as age and skin color) with unrelated attributes (downstream tasks), resulting in biases which do not correspond to reality. It is common knowledge that these correlations are present in the data and are then transferred to the models during training. This paper proposes a method to mitigate these correlations to improve fairness. To do so, we learn interpretable and meaningful paths lying in the semantic space of a pre-trained diffusion model (DiffAE) -- such paths being supervised by contrastive text dipoles. That is, we learn to edit protected characteristics (age and skin color). These paths are then applied to augment images to improve the fairness of a given dataset. We test the proposed method on CelebA-HQ and UTKFace on several downstream tasks with age and skin color as protected characteristics. As a proxy for fairness, we compute the difference in accuracy with respect to the protected characteristics. Quantitative results show how the augmented images help the model improve the overall accuracy, the aforementioned metric, and the disparity of equal opportunity. Code is available at: https://github.com/Moreno98/Vision-Language-Bias-Control.

As text-to-image systems continue to grow in popularity with the general public, questions have arisen about bias and diversity in the generated images. Here, we investigate properties of images generated in response to prompts which are visually under-specified, but contain salient social attributes (e.g., 'a portrait of a threatening person' versus 'a portrait of a friendly person'). Grounding our work in social cognition theory, we find that in many cases, images contain similar demographic biases to those reported in the stereotype literature. However, trends are inconsistent across different models and further investigation is warranted.

The UK health secretary, Sajid Javid, has announced a review into systemic racism and gender bias in medical devices in response to concerns it could contribute to poorer outcomes for women and people of colour. Writing in the Sunday Times, Javid said: "It is easy to look at a machine and assume that everyone's getting the same experience. But technologies are created and developed by people, and so bias, however inadvertent, can be an issue here too." We take a look at some of the gadgets used in healthcare where concerns over racial bias have been raised. Oximeters estimate the amount of oxygen in a person's blood, and are a crucial tool in determining which Covid patients may need hospital care – not least because some can have dangerously low levels of oxygen without realising.

ALGORITHMIC BIAS is often described as a thorny technical problem. Machine-learning models can respond to almost any pattern--including ones that reflect discrimination. Their designers can explicitly prevent such tools from consuming certain types of information, such as race or sex. Nonetheless, the use of related variables, like someone's address, can still cause models to perpetuate disadvantage. Ironing out all traces of bias is a daunting task. Yet despite the growing attention paid to this problem, some of the lowest-hanging fruit remains unpicked.

In March 2016, Microsoft sent its artificial intelligence (AI) bot Tay out into the wild to see how it interacted with humans. According to Microsoft Cybersecurity Field CTO Diana Kelley, the team behind Tay wanted the bot to pick up natural language and thought Twitter was the best place for it to go. "A great example of AI and ML going awry is Tay," Kelley told RSA Conference 2019 Asia Pacific and Japan in Singapore last week. Tay was targeted at American 18 to 24-year olds and was "designed to engage and entertain people where they connect with each other online through casual and playful conversation". Here's how it's related to artificial intelligence, how it works and why it matters.

Facial recognition systems developed for self-driving cars are better at identifying the faces of white people than those of darker skin tones, a study has revealed. Researchers say the inherent racism of these systems likely stems from a lack of dark-skinned individuals included in the training of the tech. The study found databases behind facial recognition technology being built for autonomous cars are up to 12 per cent worse at spotting people with darker skin. On average, the technology is 4.8 per cent more accurate at correctly spotting light-skinned individuals. A system was used with skin tones ranging from one to six, with a higher number linked to darker skin.

Technologies for amplifying, sequencing and matching DNA have created new opportunities in genomic science. In this series When DNA Talks we look at the ethical and social implications. Everywhere we go we leave behind bits of DNA. We can already use this DNA to predict some traits, such as eye, skin and hair colour. Soon it may be possible to accurately reconstruct your whole face from these traces.

Over the course of the last few years, artificial intelligence (AI) has become recognized as one of the keys to solving some of the world's most complex issues, unlocking a level of growth and innovation that has never been seen before. Governments across the globe are now shifting gear, actively designing investment approaches, incentives and discussing regulatory frameworks to help their nations maintain a top spot in this emerging industry. And much like global counterparts, Canadian policy makers and industries are grappling with the challenge of regulating without stifling innovation. AI was a central focus at the 2017 annual business of AI conference hosted by Rotman's Creative Destruction Lab (CDL), a Canadian accelerator that builds AI-powered startups. At the conference, Canada's Prime Minister Justin Trudeau said the following about AI: "Let's be part of it and help shape it, and let's make sure we're benefiting from the innovations – in both the designing of them and the applications and the jobs." Let's remember this isn't just a gender issue, we need to think broader and ensure our machines are learning about ethnicity, race, language, skin colour, and age.

Bengaluru: Last year, for the first time ever, an international beauty contest was judged by machines. Thousands of people from across the world submitted their photos to Beauty.AI, hoping that their faces would be selected by an advanced algorithm free of human biases, in the process accurately defining what constitutes human beauty. In preparation, the algorithm had studied hundreds of images of past beauty contests, training itself to recognize human beauty based on the winners. But what was supposed to be a breakthrough moment that would showcase the potential of modern self-learning, artificially intelligent algorithms rapidly turned into an embarrassment for the creators of Beauty.AI, as the algorithm picked the winners solely on the basis of skin colour. "The algorithm made a fairly non-trivial correlation between skin colour and beauty. A classic example of bias creeping into an algorithm," says Nisheeth K. Vishnoi, an associate professor at the School of Computer and Communication Sciences at Switzerland-based École Polytechnique Fédérale de Lausanne (EPFL).

There is an all-too familiar response when video game developers are asked if their latest project has any real-world meaning: hey, we're just making a game, we're not making a statement. It's a media-trained kneejerk defence against potential controversy, a line dragged out time and time again when a producer or creative director is asked about seemingly clear parallels with genuine wars, events or issues. Last year, for example, video game site Killscreen spoke to the makers of The Division, a game about an apocalyptic terrorist attack on New York City. When asked if 9/11 had in any way inspired the setting and narrative, associate creative director Julian Gerighty seemed aghast at the comparison – and at the connection between the game and an actual incident. "At the end of the day, it's a video game," he said.