Many recent datasets contain a variety of different data modalities, for instance, image, question, and answer data in visual question answering (VQA). When training deep net classifiers on those multi-modal datasets, the modalities get exploited at different scales, i.e., some modalities can more easily contribute to the classification results than others. This is suboptimal because the classifier is inherently biased towards a subset of the modalities. To alleviate this shortcoming, we propose a novel regularization term based on the functional entropy. Intuitively, this term encourages to balance the contribution of each modality to the classification result. However, regularization with the functional entropy is challenging.

The CV or cover letter needs to be reviewed, and from there, they may have two or three rounds of interviews. If offered a job, conversations around salary and onboarding and training begin. Then it's the employers' job to foster and retain loyalty. All these steps aren't quick, easy, or cheap to go through and a business may need to go through at least 20 candidates before finding the right person. This is where Artificial Intelligence (AI) comes in – a solution that can reduce the hours and energy spent in the initial recruitment process.

The origin of the word'bias' has never been quite certain. Linguists reckon that the antecedent of bias is the Old French word'biasi' which meant at an angle or oblique. It came to mean'a one-sided tendency of the mind'. In the old English game of bowls, the ball had asymmetrical weight or bias, which made it roll in a curved line. This is how bias came to be the favoured word for having a disproportionate weight in favour of or against an idea or person.

In a move that can change the way exams are conducted as well as scored, the Indian Statistical Institute (ISI) is planning to use an artificial intelligence-powered method (AI/ML) to automate the evaluation of the students' answer scripts. According to a noted national newspaper, the ISI is planning to use machine learning-based (AI/ML) system that will be programmed to analyse even subjective questions like essay writing, where a student's creative writing skills can be assessed. Officials have also suggested that one of the main objectives behind this move is to make sure that these exams are free of biases. Under the new system, the students will have to write the answers on tablets using a special type of pen, instead of submitting physical papers. A two-day workshop on machine learning and its application to pattern recognition started at the ISI on Thursday.

As more apps come to market that rely on Artificial Intelligence, software developers and data scientists can unwittingly (or perhaps even knowingly) inject their personal biases into these solutions. This can cause a variety of problems ranging from a poor user experience to major errors in critical decision-making. We at IBM have created a solution specifically to address AI bias. Because flaws and biases may not be easy to detect without the right tool, IBM is deeply committed to delivering services that are unbiased, explainable, value-aligned and transparent. Thus, we are pleased to back up that commitment with the launch of AI Fairness 360, an open-source library to help detect and remove bias in Machine Learning models and data sets.