In machine learning, quantification (variously called learning to quantify, or supervised prevalence estimation, or class prior estimation) is the task of using supervised learning in order to train models (quantifiers) that estimate the relative frequencies (also known as prevalence values) of the classes of interest in a sample of unlabelled data items. For instance, in a sample of 100,000 unlabelled tweets known to express opinions about a certain political candidate, a quantifier may be used to estimate the percentage of these tweets which belong to class `Positive' (i.e., which manifest a positive stance towards this candidate), and to do the same for classes `Neutral' and `Negative'. Quantification may also be viewed as the task of training predictors that estimate a (discrete) probability distribution, i.e., that generate a predicted distribution that approximates the unknown true distribution of the items across the classes of interest. Quantification is different from classification, since the goal of classification is to predict the class labels of individual data items, while the goal of quantification it to predict the class prevalence values of sets of data items. Quantification is also different from regression, since in regression the training data items have real-valued labels, while in quantification the training data items have class labels. It has been shown in multiple research works that performing quantification by classifying all unlabelled instances and then counting the instances that have been attributed to each class (the 'classify and count' method) usually leads to suboptimal quantification accuracy. This suboptimality may be seen as a direct consequence of 'Vapnik's principle', which states: If you possess a restricted amount of information for solving some problem, try to solve the problem directly and never solve a more general problem as an intermediate step. It is possible that the available information is sufficient for a direct solution but is insufficient for solving a more general intermediate problem. In our case, the problem to be solved directly is quantification, while the more general intermediate problem is classification. As a result of the suboptimality of the 'classify and count' method, quantification has evolved as a task in its own right, different (in goals, methods, techniques, and evaluation measures) from classification. == Quantification tasks == === Quantification tasks according to the set of classes === The main variants of quantification, according to the characteristics of the set of classes used, are: Binary quantification, corresponding to the case in which there are only n = 2 {\displaystyle n=2} classes and each data item belongs to exactly one of them; Single-label multiclass quantification, corresponding to the case in which there are n > 2 {\displaystyle n>2} classes and each data item belongs to exactly one of them; Multi-label multiclass quantification, corresponding to the case in which there are n ≥ 2 {\displaystyle n\geq 2} classes and each data item can belong to zero, one, or several classes at the same time; Ordinal quantification, corresponding to the single-label multiclass case in which a total order is defined on the set of classes. Regression quantification, a task which stands to 'standard' quantification as regression stands to classification. Strictly speaking, this task is not a quantification task as defined above (since the individual items do not have class labels but are labelled by real values), but has enough commonalities with other quantification tasks to be considered one of them. Most known quantification methods address the binary case or the single-label multiclass case, and only few of them address the multi-label, ordinal, and regression cases. Binary-only methods include the Mixture Model (MM) method, the HDy method, SVM(KLD), and SVM(Q). Methods that can deal with both the binary case and the single-label multiclass case include probabilistic classify and count (PCC), adjusted classify and count (ACC), probabilistic adjusted classify and count (PACC), the Saerens-Latinne-Decaestecker EM-based method (SLD), and KDEy. Methods for multi-label quantification include regression-based quantification (RQ) and label powerset-based quantification (LPQ). Methods for the ordinal case include ordinal versions of the above-mentioned ACC, PACC, and SLD methods, and ordinal versions of the above-mentioned HDy method. Methods for the regression case include Regress and splice and Adjusted regress and sum. === Quantification tasks according to the type of data === Several subtasks of quantification may be identified according to the type of data involved. Example such tasks are: Quantification of networked data. This task consists of performing quantification when the datapoints are members of a relation, i.e., are interlinked. As such, this task is a strict relative of collective classification. Quantification over time. This task consists of performing quantification on sets that become available in a temporal sequence, i.e., as a data stream, and finds application in contexts in which class prevalence values must be monitored over time. == Evaluation measures for quantification == Several evaluation measures can be used for evaluating the error of a quantification method. Since quantification consists of generating a predicted probability distribution that estimates a true probability distribution, these evaluation measures are ones that compare two probability distributions. Most evaluation measures for quantification belong to the class of divergences. Evaluation measures for binary quantification, single-label multiclass quantification, and multi-label quantification, are Absolute Error Squared Error Relative Absolute Error Kullback–Leibler divergence Pearson Divergence Evaluation measures for ordinal quantification are Normalized Match Distance (a particular case of the Earth Mover's Distance) Root Normalized Order-Aware Distance == Applications == Quantification is of special interest in fields such as the social sciences, epidemiology, market research, allocating resources, and ecological modelling, since these fields are inherently concerned with aggregate data. However, quantification is also useful as a building block for solving other downstream tasks, such as improving the accuracy of classifiers on out-of-distribution data, measuring classifier bias and ranker bias, and estimating the accuracy of classifiers on out-of-distribution data. == Resources == LQ 2021: the 1st International Workshop on Learning to Quantify LQ 2022: the 2nd International Workshop on Learning to Quantify LQ 2023: the 3rd International Workshop on Learning to Quantify LQ 2024: the 4th International Workshop on Learning to Quantify LQ 2025: the 5th International Workshop on Learning to Quantify LeQua 2022: the 1st Data Challenge on Learning to Quantify LeQua 2024: the 2nd Data Challenge on Learning to Quantify QuaPy: An open-source Python-based software library for quantification QuantificationLib: A Python library for quantification and prevalence estimation
Biometric device
A biometric device is a security identification and authentication device. Such devices use automated methods of verifying or recognising the identity of a living person based on a physiological or behavioral characteristic. These characteristics include fingerprints, facial images, iris and voice recognition. == History == Biometric devices have been in use for thousands of years. Non-automated biometric devices have been in use since 500 BC, when ancient Babylonians would sign their business transactions by pressing their fingertips into clay tablets. Automation in biometric devices was first seen in the 1960s. The Federal Bureau of Investigation (FBI) in the 1960s, introduced the Indentimat, which started checking for fingerprints to maintain criminal records. The first systems measured the shape of the hand and the length of the fingers. Although discontinued in the 1980s, the system set a precedent for future Biometric Devices. == Subgroups == The characteristic of the human body is used to access information by the users. According to these characteristics, the sub-divided groups are Chemical biometric devices: Analyses the segments of the DNA to grant access to the users. Visual biometric devices: Analyses the visual features of the humans to grant access which includes iris recognition, face recognition, Finger recognition, and Retina Recognition. Behavioral biometric devices: Analyses the Walking Ability and Signatures (velocity of sign, width of sign, pressure of sign) distinct to every human. Olfactory biometric devices: Analyses the odor to distinguish between varied users. Auditory biometric devices: Analyses the voice to determine the identity of a speaker for accessing control. == Uses == === Workplace === Biometrics are being used to establish better and accessible records of the hour's employee's work. With the increase in "Buddy Punching" (a case where employees clocked out coworkers and fraudulently inflated their work hours) employers have looked towards new technology like fingerprint recognition to reduce such fraud. Additionally, employers are also faced with the task of proper collection of data such as entry and exit times. Biometric devices make for largely fool proof and reliable ways of enabling to collect data as employees have to be present to enter biometric details which are unique to them. === Immigration === As the demand for air travel grows and more people travel, modern-day airports have to implement technology in such a way that there are no long queues. Biometrics are being implemented in more and more airports as they enable quick recognition of passengers and hence lead to lower volume of people standing in queues. One such example is of the Dubai International Airport which plans to make immigration counters a relic of the past as they implement IRIS on the move technology (IOM) which should help the seamless departures and arrivals of passengers at the airport. === Handheld and personal devices === Fingerprint sensors can be found on mobile devices. The fingerprint sensor is used to unlock the device and authorize actions, like money and file transfers, for example. It can be used to prevent a device from being used by an unauthorized person. It is also used in attendance in number of colleges and universities. == Present day biometric devices == === Personal signature verification systems === This is one of the most highly recognised and acceptable biometrics in corporate surroundings. This verification has been taken one step further by capturing the signature while taking into account many parameters revolving around this like the pressure applied while signing, the speed of the hand movement and the angle made between the surface and the pen used to make the signature. This system also has the ability to learn from users as signature styles vary for the same user. Hence by taking a sample of data, this system is able to increase its own accuracy. === Iris recognition system === Iris recognition involves the device scanning the pupil of the subject and then cross referencing that to data stored on the database. It is one of the most secure forms of authentication, as while fingerprints can be left behind on surfaces, iris prints are extremely hard to be stolen. Iris recognition is widely applied by organisations dealing with the masses, one being the Aadhaar identification system issued by the Government of India to keep records of its population. The reason for this is that iris recognition makes use of iris prints of humans, which change little over the course of one's lifetime. == Problems with present day biometric devices == === Biometric spoofing === Biometric spoofing is a method of fooling a biometric identification management system, where a counterfeit mold is presented in front of the biometric scanner. This counterfeit mold emulates the unique biometric attributes of an individual so as to confuse the system between the artifact and the real biological target and gain access to sensitive data/materials. One such high-profile case of Biometric spoofing came to the limelight when it was found that German Defence Minister, Ursula von der Leyen's fingerprint had been successfully replicated by Chaos Computer Club. The group used high quality camera lenses and shot images from 6 feet away. They used a professional finger software and mapped the contours of the Ministers thumbprint. Although progress has been made to stop spoofing. Using the principle of pulse oximetry — the liveliness of the test subject is taken into account by measure of blood oxygenation and the heart rate. This reduces attacks like the ones mentioned above, although these methods aren't commercially applicable as costs of implementation are high. This reduces their real world application and hence makes biometrics insecure until these methods are commercially viable. === Accuracy === Accuracy is a major issue with biometric recognition. Passwords are still extremely popular, because a password is static in nature, while biometric data can be subject to change (such as one's voice becoming heavier due to puberty, or an accident to the face, which could lead to improper reading of facial scan data). When testing voice recognition as a substitute to PIN-based systems, Barclays reported that their voice recognition system is 95 percent accurate. This statistic means that many of its customers' voices might still not be recognised even when correct. This uncertainty revolving around the system could lead to slower adoption of biometric devices, continuing the reliance of traditional password-based methods. == Benefits of biometric devices over traditional methods of authentication == Biometric data cannot be lent and hacking of Biometric data is complicated hence it makes it safer to use than traditional methods of authentication like passwords which can be lent and shared. Passwords do not have the ability to judge the user but rely only on the data provided by the user, which can easily be stolen while Biometrics work on the uniqueness of each individual. Passwords can be forgotten and recovering them can take time, whereas Biometric devices rely on biometric data which tends to be unique to a person, hence there is no risk of forgetting the authentication data. A study conducted among Yahoo! users found that at least 1.5 percent of Yahoo users forgot their passwords every month, hence this makes accessing services more lengthy for consumers as the process of recovering passwords is lengthy. These shortcomings make Biometric devices more efficient and reduces effort for the end user. == Future == Researchers are targeting the drawbacks of present-day biometric devices and developing to reduce problems like biometric spoofing and inaccurate intake of data. Technologies which are being developed are- The United States Military Academy are developing an algorithm that allows identification through the ways each individual interacts with their own computers; this algorithm considers unique traits like typing speed, rhythm of writing and common spelling mistakes. This data allows the algorithm to create a unique profile for each user by combining their multiple behavioral and stylometric information. This can be very difficult to replicate collectively. A recent innovation by Kenneth Okereafor and, presented an optimized and secure design of applying biometric liveness detection technique using a trait randomization approach. This novel concept potentially opens up new ways of mitigating biometric spoofing more accurately, and making impostor predictions intractable or very difficult in future biometric devices. A simulation of Kenneth Okereafor's biometric liveness detection algorithm using a 3D multi-biometric framework consisting of 15 liveness parameters from facial print, finger print and iris pattern traits resulted in a system efficiency of the 99.2% over a cardinality of 125 distinct randomization combinat
Is an AI Marketing Tool Worth It in 2026?
Trying to pick the best AI marketing tool? An AI marketing tool is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI marketing tool slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
The Best Free AI Code Generator for Beginners
In search of the best AI code generator? An AI code generator is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI code generator slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
FrameNet
FrameNet is a group of online lexical databases based upon the theory of meaning known as Frame semantics, developed by linguist Charles J. Fillmore. The project's fundamental notion is simple: most words' meanings may be best understood in terms of a semantic frame, which is a description of a certain kind of event, connection, or item and its actors. As an illustration, the act of cooking usually requires the following: a cook, the food being cooked, a container to hold the food while it is being cooked, and a heating instrument. Within FrameNet, this act is represented by a frame named Apply_heat, and its components (Cook, Food, Container, and Heating_instrument), are referred to as frame elements (FEs). The Apply_heat frame also lists a number of words that represent it, known as lexical units (LUs), like fry, bake, boil, and broil. Other frames are simpler. For example, Placing only has an agent or cause, a theme—something that is placed—and the location where it is placed. Some frames are more complex, like Revenge, which contains more FEs (offender, injury, injured party, avenger, and punishment). As in the examples of Apply_heat and Revenge below, FrameNet's role is to define the frames and annotate sentences to demonstrate how the FEs fit syntactically around the word that elicits the frame. == Concepts == === Frames === A frame is a schematic representation of a situation involving various participants, props, and other conceptual roles. Examples of frame names are Being_born and Locative_relation. A frame in FrameNet contains a textual description of what it represents (a frame definition), associated frame elements, lexical units, example sentences, and frame-to-frame relations. === Frame elements === Frame elements (FE) provide additional information to the semantic structure of a sentence. Each frame has a number of core and non-core FEs which can be thought of as semantic roles. Core FEs are essential to the meaning of the frame while non-core FEs are generally descriptive (such as time, place, manner, etc.) For example: The only core FE of the Being_born frame is called Child; non-core FEs Time, Place, Means, etc. Core FEs of the Commerce_goods-transfer frame include the Seller, Buyer, and Goods, while non-core FEs include a Place, Purpose, etc. FrameNet includes shallow data on syntactic roles that frame elements play in the example sentences. For example, for a sentence like "She was born about AD 460", FrameNet would mark She as a noun phrase referring to the Child frame element, and "about AD 460" as a noun phrase corresponding to the Time frame element. Details of how frame elements can be realized in a sentence are important because this reveals important information about the subcategorization frames as well as possible diathesis alternations (e.g. "John broke the window" vs. "The window broke") of a verb. === Lexical units === Lexical units (LUs) are lemmas, with their part of speech, that evoke a specific frame. In other words, when an LU is identified in a sentence, that specific LU can be associated with its specific frame(s). For each frame, there may be many LUs associated to that frame, and also there may be many frames that share a specific LU; this is typically the case with LUs that have multiple word senses. Alongside the frame, each lexical unit is associated with specific frame elements by means of the annotated example sentences. For example, lexical units that evoke the Complaining frame (or more specific perspectivized versions of it, to be precise), include the verbs complain, grouse, lament, and others. === Example sentences === Frames are associated with example sentences and frame elements are marked within the sentences. Thus, the sentence She was born about AD 460 is associated with the frame Being_born, while She is marked as the frame element Child and "about AD 460" is marked as Time. From the start, the FrameNet project has been committed to looking at evidence from actual language use as found in text collections like the British National Corpus. Based on such example sentences, automatic semantic role labeling tools are able to determine frames and mark frame elements in new sentences. === Valences === FrameNet also exposes statistics on the valence of each frame; that is, the number and position of the frame elements within example sentences. The sentence She was born about AD 460 falls in the valence pattern NP Ext, INI --, NP Dep which occurs twice in the FrameNet's annotation report for the born.v lexical unit, namely: She was born about AD 460, daughter and granddaughter of Roman and Byzantine emperors, whose family had been prominent in Roman politics for over 700 years. He was soon posted to north Africa, and never met their only child, a daughter born 8 June 1941. === Frame relations === FrameNet additionally captures relationships between different frames using relations. These include the following: Inheritance: When one frame is a more specific version of another, more abstract, parent frame. Anything that is true about the parent frame must also be true about the child frame, and a mapping is specified between the frame elements of the parent and the frame elements of the child. Perspectivization: A neutral frame is connected to a frame with a specific perspective of the same scenario. For example, Commerce_transfer-goods is considered from the perspective of the buyer in Commerce_buy and from that of the seller in Commerce_sell. Subframe: Some frames refer to complex scenarios that consist of several individual states or events that can be described by separate frames. For example, Criminal_process is composed of Arrest, Trial, and so on. Precedence: This relation captures the temporal order that holds between subframes of a complex frame. For example, within the Cycle_of_life_and_death frame, the subframe Death is preceded by the subframe Being_born. Causative and Inchoative: These two relations mark, for causative- and inchoative-aspect frames, the separate stative frame they refer to. For example, the stative Position_on_a_scale (e.g. "She had a high salary") is described by the causative Cause_change_of_scalar_position (e.g. "She raised his salary") and by the inchoative Change_position_on_a_scale frame (e.g. "Her salary increased"). Using: This relation marks a frame that in some way involves another frame. For example, Judgment_communication uses both Judgment and Statement, but does not inherit from either of them because there is no clear correspondence of frame elements. See also: Connects frames that bear some resemblance but need to be distinguished carefully. == Applications == FrameNet has proven to be useful in a number of computational applications, because computers need additional knowledge in order to recognize that "John sold a car to Mary" and "Mary bought a car from John" describe essentially the same situation, despite using two quite different verbs, different prepositions and a different word order. FrameNet has been used in applications like question answering, paraphrasing, recognizing textual entailment, and information extraction, either directly or by means of Semantic Role Labeling tools. The first automatic system for Semantic Role Labeling (SRL, sometimes also referred to as "shallow semantic parsing") was developed by Daniel Gildea and Daniel Jurafsky based on FrameNet in 2002. Semantic Role Labeling has since become one of the standard tasks in natural language processing, with the latest version (1.7) of FrameNet now fully supported in the Natural Language Toolkit. Since frames are essentially semantic descriptions, they are similar across languages, and several projects have arisen over the years that have relied on the original FrameNet as the basis for additional non-English FrameNets, for Spanish, Japanese, German, and Polish, among others.
Text-to-image personalization
Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model), is adapted such that it can generate images of novel, user-provided concepts. These concepts are typically unseen during training, and may represent specific objects (such as the user's pet) or more abstract categories (new artistic style or object relations). Text-to-Image personalization methods typically bind the novel (personal) concept to new words in the vocabulary of the model. These words can then be used in future prompts to invoke the concept for subject-driven generation, inpainting, style transfer and even to correct biases in the model. To do so, models either optimize word-embeddings, fine-tune the generative model itself, or employ a mixture of both approaches. == Technology == Text-to-Image personalization was first proposed during August 2022 by two concurrent works, Textual Inversion and DreamBooth. In both cases, a user provides a few images (typically 3–5) of a concept, like their own dog, together with a coarse descriptor of the concept class (like the word "dog"). The model then learns to represent the subject through a reconstruction based objective, where prompts referring to the subject are expected to reconstruct images from the training set. In Textual Inversion, the personalized concepts are introduced into the text-to-image model by adding new words to the vocabulary of the model. Typical text-to-image models represent words (and sometimes parts-of-words) as tokens, or indices in a predefined dictionary. During generation, an input prompt is converted into such tokens, each of which is converted into a ‘word-embedding’: a continuous vector representation which is learned for each token as part of the model's training. Textual Inversion proposes to optimize a new word-embedding vector for representing the novel concept. This new embedding vector can then be assigned to a user-chosen string, and invoked whenever the user's prompt contains this string. In DreamBooth, rather than optimizing a new word vector, the full generative model itself is fine-tuned. The user first selects an existing token, typically one which rarely appears in prompts. The subject itself is then represented by a string containing this token, followed by a coarse descriptor of the subject's class. A prompt describing the subject will then take the form: "A photo of
Black in AI
Black in AI, formally called the Black in AI Workshop, is a technology research organization and affinity group, founded by computer scientists Timnit Gebru and Rediet Abebe in 2017. It started as a conference workshop, later pivoting into an organization. Black in AI increases the presence and inclusion of Black people in the field of artificial intelligence (AI) by creating space for sharing ideas, fostering collaborations, mentorship, and advocacy. == History == Black in AI was created in 2017 to address issues of lack of diversity in AI workshops, and was started as its own workshop within the Conference on Neural Information Processing Systems (NeurIPS) conference. Because of algorithmic bias, ethical issues, and underrepresentation of Black people in AI roles; there has been an ongoing need for unity within the AI community to have focus on these issues. Black in AI has strived to continue the progress of improving the presence of people of color in the field of artificial intelligence. In 2018 and 2019, the Black in AI workshop had many immigration visa issues to Canada, which spurred the conference to be planned for 2020 in Addis Ababa, Ethiopia. On December 7, 2020, Black in AI held its fourth annual workshop and first virtual workshop (due to the COVID-19 pandemic). In 2021, Black in AI, alongside the groups Queer in AI and Widening NLP, released a public statement refusing funding from Google in an act of protest of Google's treatment of Timnit Gebru, Margaret Mitchell, and April Christina Curley in the events that occurred in December 2020. == Founders == Rediet Abebe is an Ethiopian computer scientist who specializes in algorithms and artificial intelligence. She is a Computer Science Assistant Professor at the University of California, Berkeley. She was previously a Junior Fellow at Harvard's Society of Fellows. She was the first Black woman to receive a Ph.D. in computer science at Cornell University. She "designs and analyzes algorithms, discrete optimizations, network-based, [and] computational strategies to increase access to opportunity for historically disadvantaged populations," according to her web bio. Timnit Gebru was born in Ethiopia and moved to the United States at the age of fifteen. She got her B.S. and M.S. in electrical engineering from Stanford University, as well as a PhD from the Stanford Artificial Intelligence Laboratory, where she studied computer vision under Fei-Fei Li. She formerly worked as a postdoctoral researcher at Microsoft Research in the Fairness Accountability Transparency, and Ethics (FATE) division. She's also worked with Apple, where she assisted in the development of signal-processing algorithms for the original iPad. == Grants == Black in AI received grants and support from private foundations like MacArthur Foundation and Rockefeller Foundation. The organization received $10,000 in 2018 for its annual workshop and $150,000 in 2019 for its long-term organizational planning. In 2020, during the pandemic, the organization received a grant of $300,000 by MacArthur Foundation in order to provide broad organizational support. In 2022, Rockefeller Foundation announced $300,000 to fight prejudice in artificial intelligence (AI) across the globe and incorporate equity into this rapidly expanding field. == Programs == "Black in AI works in academics, advocacy, entrepreneurship, financial support, and summer research programs." The Black in AI Academic Program is a resource for Black junior researchers applying to graduate schools, navigating graduate school, and transitioning into the postgraduate employment market. They provide online education sessions, offer scholarships to cover application fees, pair participants with peer and senior mentors, and distribute crowdsourced papers that simplify the application process. They also undertake research projects to investigate and highlight the difficulties that Black young researchers face, as well as push for structural reforms to eliminate these barriers and build equitable research settings. Moses Namara is a Facebook Research Fellow at Clemson University and a PhD candidate in Human-Centered Computing (HCC). He is the mentor for the new Black in AI Academic Program. During the graduate school admissions season in 2021, Black in AI served more than 200 potential graduate program candidates in some capacity. Furthermore, the organization's study identified greater problems encountered by Black graduate school candidates, such as the high cost of graduate school admissions examinations (GREs), which are known to be biased against those from low-income backgrounds. Black in AI's attempts to encourage institutions to eliminate the obstacles were supported by the findings. Black in AI is also developing a program to help and connect Black tech startups with investors. Black in AI also mentors early-career Black AI academics and is forming relationships with Historically Black Colleges and Universities to extend its academic program. In 2021, Black in AI launched two summer research programs, one for undergraduate internships and another for unconstrained research mentorship, including one aimed explicitly at empowering Black women's AI research projects. == Conferences and workshops == At NeurIPS 2017, the first Black in AI event took place in December 8, 2017 in Long Beach, California. The goal was to bring together experts in the area to share ideas and debate efforts aimed at increasing the participation of Black people in artificial intelligence, both for diversity and to avoid data bias. Black AI researchers had the opportunity to share their work at the workshop's oral and poster sessions. The second workshop was hosted in Montréal, Canada, on December 7, 2018. According to AI experts, visa issues stymie efforts to make their area more inclusive, making technology that discriminates or disadvantages individuals who aren't white or Western less likely. Hundreds of participants who were supposed to attend or present work at the Black in AI session on Friday were unable to fly to Canada; many of the participants were from African countries. The third workshop was held in NeurIPS 2019, one of the premier machine learning conferences Vancouver, Canada. The workshop was able to give travel scholarships and visa support to hundreds of academics who would not have been able to attend NeurIPS without the help of sponsors. For instance, Ramon Vilarino of the University of Sao Paulo, who presented a poster at the conference on his study of geographical and racial prejudice in credit scoring in Brazil, would not have been able to attend NeurIPS without the help of Black in AI. Twenty-four academics from Africa and South America were denied visas to attend this session during the conference, according to Victor Silva, the workshop organizer. He noted that, less than a month before the conference, 40 applicants from both continents had been given visas but that more than 70 applications were still waiting. For the second year in a row, visa restrictions have stopped several African scholars from attending the 2018 meeting in Montreal. The AAAI announced the first Black in AI lunch, which was held in conjunction with AAAI-19. The lunch was hosted on Tuesday, January 29, 2019. This event was intended to promote networking, discussion of various AI career options, and the exchange of ideas in order to boost the number of Black researchers in the area. The fourth Black in AI workshop, which was held in conjunction with NeurIPS 2020, took place the week of December 7, 2020. The workshop was scheduled to take place in Vancouver, British Columbia. Due to the pandemic, the session was held for the first time in a virtual format. Victor Silva, an AI4Society student, served as the event's chair. The fifth annual Black in AI workshop was also held virtually in 2021. Oral presentations, guest keynote speakers, a combined poster session with other affinity groups, sponsored sessions, and startup showcases was all featured. The goal of the session was to raise the visibility of black scholars at NeurIPS.