Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. Given that "bias in, bias out", one cannot expect AI-based solutions to have equitable outcomes for societal applications, without addressing issues such as representation bias. While there has been extensive study of fairness in machine learning models, including several review papers, bias in the data has been less studied. This paper reviews the literature on identifying and resolving representation bias as a feature of a data set, independent of how consumed later. The scope of this survey is bounded to structured (tabular) and unstructured (e.g., image, text, graph) data. It presents taxonomies to categorize the studied techniques based on multiple design dimensions and provides a side-by-side comparison of their properties. There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains.

We've spent the past several years researching artificial intelligence (AI) for healthcare -- exploring how it can help detect diseases early, expand access to care and more. We've taken a "move slow and test things" approach to prove efficacy, equity, helpfulness and safety above all. Today, at our annual health event, The Check Up, we shared health AI updates including our progress on our medical large language model (LLM) research, partnerships that are bringing solutions into real-world settings, and new ways AI can help with disease detection. Recent progress in large language models (LLMs) -- AI tools that demonstrate capabilities in language understanding and generation -- has opened up new ways to use AI to solve real-world problems. However, unlike some other LLM use cases, applications of AI in the medical field require the utmost focus on safety, equity, and bias to protect patient well-being.

Spark Foundry is one of four global media agency brands within Publicis Media. The force of acceleration, or the speed of change is so strong today that it has the potential to leave industries including our own behind. So, at Spark Foundry we are working towards driving positive change. We are an Acceleration Agency. At Spark Foundry acceleration applies to every layer of our business.

Ocorian is a global leader in corporate and fiduciary services, fund administration and capital markets. Wherever our clients hold financial interests, or however they are structured, we provide compliant, tailored solutions that are individual to their needs. We manage over 17,000 structures for 8000 clients with a global footprint operating from 18 locations. Our scale offers all our people great opportunities to develop their knowledge and skills and to progress their careers. To supervise a team of officers who will carry out the review and updates of client files as per the AML/CFT and other applicable regulations of the jurisdiction of domiciliation of the client entity, input relevant information on the Enterprise Resource Planning ('ERP') software and Document Management System ('DMS'), and complete the file reviews as per the defined process and quality and within agreed timeline.

In October last year, a Russian news site published a short video of Yevgeny Prigozhin, founder of the Wagner Group, the Russian mercenary army, sitting with four men on a rooftop terrace in the resort town of Gelendzhik, on Russia's Black Sea coast. Two are missing parts of a leg. A third lost an arm. They are identified as pardoned former convicts, returned from the front in Ukraine after joining Wagner from prison. "You were an offender, now you're a war hero," Prigozhin tells one man in the clip. It was the first video to depict the return of some of the thousands of convicts who joined Wagner in return for the promise of a pardon if they survived six months of the war. Reuters news agency used facial recognition software to examine this video and more than a dozen others and photographs of homecoming convict fighters, published between October 2022 and February 2023.

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech.

Malaria can be prevented, diagnosed, and treated; however, every year, there are more than 200 million cases and 200.000 preventable deaths. Malaria remains a pressing public health concern in low- and middle-income countries, especially in sub-Saharan Africa. We describe how by means of mobile health applications, machine-learning-based adaptive interventions can strengthen malaria surveillance and treatment adherence, increase testing, measure provider skills and quality of care, improve public health by supporting front-line workers and patients (e.g., by capacity building and encouraging behavioral changes, like using bed nets), reduce test stockouts in pharmacies and clinics and informing public health for policy intervention.

Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data. On-device ML is highly context dependent, and sensitive to user, usage, hardware and environment attributes. This sensitivity and the propensity towards bias in ML makes it important to study bias in on-device settings. Our study is one of the first investigations of bias in this emerging domain, and lays important foundations for building fairer on-device ML. We apply a software engineering lens, investigating the propagation of bias through design choices in on-device ML workflows. We first identify reliability bias as a source of unfairness and propose a measure to quantify it. We then conduct empirical experiments for a keyword spotting task to show how complex and interacting technical design choices amplify and propagate reliability bias. Our results validate that design choices made during model training, like the sample rate and input feature type, and choices made to optimize models, like light-weight architectures, the pruning learning rate and pruning sparsity, can result in disparate predictive performance across male and female groups. Based on our findings we suggest low effort strategies for engineers to mitigate bias in on-device ML.

Ernest Mwebaze and his team have developed a mobile application for farmers to help diagnose diseases in their cassava crops. We spoke to Ernest to find out more about this project, how it developed, and plans for further work. The work really targets improving the livelihoods of smallholder farmers in Sub-Saharan Africa. The society in Sub-Saharan Africa is predominantly agricultural, with the livelihoods of over 70% of people depending on agriculture. We targeted the cassava plant, one of the key crops here; it's second after maize, and it's one of major sources of carbohydrates for people here in Sub-Saharan Africa.

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive. When you join Visa, you join a culture of purpose and belonging – where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world – helping unlock financial access to enable the future of money movement.