Data creation has exploded in the 21st century. There's a camera and recording device in every pocket, which that makes it increasingly difficult for human moderators to stay on top of the explosion in user-generated content - especially when some people purposefully post illegal or harmful images and video. "When we consider that it's been estimated that it would take someone 950 years to check all of the Snaps uploaded to SnapChat every 24 hours, it's obvious that companies cannot moderate this volume of images using human power alone," says Cris Pikes, CEO and co-founder of Image Analyzer. Even massive social media firms like Facebook, which outsource content moderation, struggle to keep up with the growth in harmful, extremist and false content. It has reached the point that the individuals who work in moderation are starting to sue for burn-out, and even post-traumatic stress.

Back in 2018, the American Civil Liberties Union found out that Amazon's Rekognition, face surveillance technology used by police and courting departments across the US, shows AI bias. During the test, the software incorrectly matched 28 members of Congress with the mugshots of people who have been arrested for committing a crime, and 40% of the false matches were people of color. Following mass protests wherein Amazon's employees refused to contribute to AI tools that reproduce facial recognition bias, the tech giant has announced a one-year moratorium on law enforcement agencies using the platform. The incident has stirred new debate about bias in artificial intelligence algorithms and made companies search for new solutions to the AI bias paradox. In this article, we'll dot the i's, zooming in on the concept, root causes, types, and ethical implications of AI bias, as well as list practical debiasing techniques shared by our AI consultants that worth including in your AI strategy.

The emotion recognition software segment is projected to grow dramatically in coming years, spelling success for companies that have established a beachhead in the market, while causing some who are skeptical about its accuracy and fairness to raise red flags. The global emotion detection and recognition market is projected to grow to $37.1 billion by 2026, up from an estimated $19.5 billion in 2020, according to a recent report from MarketsandMarkets. North America is home to the largest market. Software suppliers covered in the report include: NEC Global (Japan), IBM (US), Intel (US), Microsoft (US), Apple (US), Gesturetek (Canada), Noldus Technology (Netherlands), Google (US), Tobii (Sweden), Cognitec Systems (Germany), Cipia Vision Ltd (Formerly Eyesight Technologies) (Israel), iMotions (Denmark), Numenta (US), Elliptic Labs (Norway), Kairos (US), PointGrab (US), Affectiva (US), nViso (Switzerland), Beyond Verbal (Israel), Sightcorp (Holland), Crowd Emotion (UK), Eyeris (US), Sentiance (Belgium), Sony Depthsense (Belgium), Ayonix (Japan), and Pyreos (UK). Some question whether emotion recognition software is effective, and whether its use is ethical.

'To be or not to be'- the ethical dilemma is a constant in human life whenever it comes to taking a decision. In the world of technology, artificial intelligence comes closest to human-like attributes. It aims to imitate the automation of human intelligence in times of operation or taking a decision. However, the AI machine can't take an independent decision and the mentality of the programmer reflects upon the operation of the AI Machine. While driving an autonomous car, in the chance of an accident, the car intelligence might have to decide whom to save first or should a child be saved before an adult.

In this post, we look at how we can automate the detection of anomalies in a manufactured product using Amazon Lookout for Vision. Using Amazon Lookout for Vision, you can notify operators in real time when defects are detected, provide dashboards for monitoring the workload, and get visual insights from the process for business users. Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Defect and anomaly detection during manufacturing processes is a vital step to ensure the quality of the products. The timely detection of faults or defects and taking appropriate actions is important to reduce operational and quality-related costs. According to Aberdeen's research, "Many organizations will have true quality-related costs as high as 15 to 20 percent of sales revenue, in extreme cases some going as high as 40 percent." Manual inspection, either in-line or end-of-line, is a time-consuming and expensive task.

Artificial Intelligence (AI) has augmented human activities and unlocked opportunities for many sectors of the economy. It is used for data management and analysis, decision making, and many other aspects. As with most rapidly advancing technologies, law is often playing a catch up role so the study of how law interacts with AI is more critical now than ever before. This book provides a detailed qualitative exploration into regulatory aspects of AI in industry. Offering a unique focus on current practice and existing trends in a wide range of industries where AI plays an increasingly important role, the work contains legal and technical analysis performed by 15 researchers and practitioners from different institutions around the world to provide an overview of how AI is being used and regulated across a wide range of sectors, including aviation, energy, government, healthcare, legal, maritime, military, music, and others.

Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.

Maine has just passed the nation's toughest law restricting the use of facial recognition technology. LD 1585 was unanimously approved by the Maine House and Senate on June 16 and 17, respectively, and became law without the signature of Gov. Janet Mills. The bill's sponsor, Rep. Grayson Lookner, D-Portland, hopes that Maine's new law--which goes into effect Oct. 1--will "provide an example to other states that want to rein in the government's ability to use facial recognition and other invasive biometric technologies." The country's only other statewide law regulating facial recognition was passed in Washington in 2020, and it authorized state police to use facial recognition technology for "mass surveillance of people's public movements, habits, and associations." The Washington law--written by state Sen. and Microsoft employee Joe Nguyen-- was opposed by the ACLU.

Compliance regulations and labor laws vary by state, county, and even city by city. Managing and maintaining compliance is critical – not only to avoid costly fines but, more importantly, to maintain a safe and healthy work environment for employees.. Watch our webcast to learn how Legion's AI-powered Workforce Management platform has helped best-in-class organizations reduce compliance violations. We'll show you how to use built-in and configurable compliance checks and manage specific compliance challenges, including: This on-demand webcast is available now, so sign-up to watch it.

Systemic bias with respect to gender, race and ethnicity, often unconscious, is prevalent in datasets involving choices among individuals. Consequently, society has found it challenging to alleviate bias and achieve diversity in a way that maintains meritocracy in such settings. We propose (a) a novel optimization approach based on optimally flipping outcome labels and training classification models simultaneously to discover changes to be made in the selection process so as to achieve diversity without significantly affecting meritocracy, and (b) a novel implementation tool employing optimal classification trees to provide insights on which attributes of individuals lead to flipping of their labels, and to help make changes in the current selection processes in a manner understandable by human decision makers. We present case studies on three real-world datasets consisting of parole, admissions to the bar and lending decisions, and demonstrate that the price of diversity is low and sometimes negative, that is we can modify our selection processes in a way that enhances diversity without affecting meritocracy significantly, and sometimes improving it.