Mario Klingemann (born 1970 in Laatzen, Lower Saxony) is a German artist best known for his work involving neural networks, code, and algorithms. Klingemann was a Google Arts and Culture resident from 2016 to 2018, and he is considered as a pioneer in the use of computer learning in the arts. His works examine creativity, culture, and perception through machine learning and artificial intelligence, and have appeared at the Ars Electronica Festival, the Museum of Modern Art New York, the Metropolitan Museum of Art New York, the Photographers’ Gallery London, the Centre Pompidou Paris, and the British Library. Today he lives in Munich, where, in addition to his art under the name "Dog & Pony", he still runs a creative free space between gallery and Wunderkammer with the paper artist Alexandra Lukaschewitz. In 2018 his work The Butcher's Son won the Lumen Prize Gold Award 2018 by working with figurative visual input. Mario Klingemann is part of ONKAOS, the new media artist support programme of SOLO. In collaboration with ONKAOS he has created works such as Memories of Passerby I, the first work made with AI to be auctioned at Sotheby's in 2019. In 2020, Mario Klingemann won an Honorary Mention in the Prix Ars Electronica with his AI installation Appropriate Response. In 2023, Klingemann presented A.I.C.C.A., a performative sculpture in the form of a dog capable of elaborating art critiques thanks to AI programming.
Image tracing
In computer graphics, image tracing, raster-to-vector conversion or raster vectorization is the conversion of raster graphics into vector graphics. == Background == An image does not have any structure: it is just a collection of marks on paper, grains in film, or pixels in a bitmap. While such an image is useful, it has some limits. If the image is magnified enough, its artifacts appear. The halftone dots, film grains, and pixels become apparent. Images of sharp edges become fuzzy or jagged. See, for example, pixelation. Ideally, a vector image does not have the same problem. Edges and filled areas are represented as mathematical curves or gradients, and they can be magnified arbitrarily (though of course the final image must also be rasterized in to be rendered, and its quality depends on the quality of the rasterization algorithm for the given inputs). The task in vectorization is to convert a two-dimensional image into a two-dimensional vector representation of the image. It is not examining the image and attempting to recognize or extract a three-dimensional model that may be depicted; i.e. it is not a vision system. For most applications, vectorization also does not involve optical character recognition; characters are treated as lines, curves, or filled objects without attaching any significance to them. In vectorization, the shape of the character is preserved, so artistic embellishments remain. Vectorization is the inverse operation corresponding to rasterization, as integration is to differentiation. And, just as with these other operations, while rasterization is fairly straightforward and algorithmic, vectorization involves the reconstruction of lost information and therefore requires heuristic methods. Synthetic images such as maps, cartoons, logos, clip art, and technical drawings are suitable for vectorization. Those images could have been originally made as vector images because they are based on geometric shapes or drawn with simple curves. Continuous tone photographs (such as live portraits) are not good candidates for vectorization. The input to vectorization is an image, but an image may come in many forms such as a photograph, a drawing on paper, or one of several raster file formats. Programs that do raster-to-vector conversion may accept bitmap formats such as TIFF, BMP and PNG. The output is a vector file format. Common vector formats are SVG, DXF, EPS, EMF and AI. Vectorization can be used to update images or recover work. Personal computers often come with a simple paint program that produces a bitmap output file. These programs allow users to make simple illustrations by adding text, drawing outlines, and filling outlines with a specific color. Only the results of these operations (the pixels) are saved in the resulting bitmap; the drawing and filling operations are discarded. Vectorization can be used to recapture some of the information that was lost. Vectorization is also used to recover information that was originally in a vector format but has been lost or has become unavailable. A company may have commissioned a logo from a graphic arts firm. Although the graphics firm used a vector format, the client company may not have received a copy of that format. The company may then acquire a vector format by scanning and vectorizing a paper copy of the logo. == Process == Vectorization starts with an image. === Manual === The image can be vectorized manually. A person could look at the image, make some measurements, and then write the output file by hand. That was the case for the vectorization of a technical illustration about neutrinos. The illustration has a few geometric shapes and a lot of text; it was relatively easy to convert the shapes, and the SVG vector format allows the text (even subscripts and superscripts) to be entered easily. The original image did not have any curves (except for the text), so the conversion is straightforward. Curves make the conversion more complicated. Manual vectorization of complicated shapes can be facilitated by the tracing function built into some vector graphics editing programs. If the image is not yet in machine readable form, then it has to be scanned into a usable file format. Once there is a machine-readable bitmap, the image can be imported into a graphics editing program (such as Adobe Illustrator, CorelDRAW, or Inkscape). Then a person can manually trace the elements of the image using the program's editing features. Curves in the original image can be approximated with lines, arcs, and Bézier curves. An illustration program allows spline knots to be adjusted for a close fit. Manual vectorization is possible, but it can be tedious. Although graphics drawing programs have been around for a long time, artists may find the freehand drawing facilities awkward even when a drawing tablet is used. Instead of using a program, Pepper recommends making an initial sketch on paper. Instead of scanning the sketch and tracing it freehand in the computer, Pepper states: "Those proficient with a graphic tablet and stylus could make the following changes directly in CorelDRAW by using a scan of the sketch as an underlay and drawing over it. I prefer to use pen and ink, and a light table"; most of the final image was traced by hand in ink. Later the line-drawing image was scanned at 600 dpi, cleaned up in a paint program, and then automatically traced with a program. Once the black and white image was in the graphics program, some other elements were added and the figure was colored. Similarly, Ploch recreated a design from a digital photograph. The JPEG was imported and some "basic shapes" were traced by hand and colored in the graphics drawing program; more complex shapes were handled differently. Ploch used a bitmap editor to remove the background and crop the more complex image components. He then printed the image and traced it by hand onto tracing paper to get a clean black and white line drawing. That drawing was scanned and then vectorized with a program. === Automatic === Some programs automate the vectorization process. Example programs are Adobe Illustrator, Inkscape, Corel's PowerTRACE, and Potrace. Some of these programs have a command line interface while others are interactive that allow the user to adjust the conversion settings and view the result. Adobe Streamline is not only an interactive program, but it also allows a user to manually edit the input bitmap and the output curves. Corel's PowerTRACE is accessed through CorelDRAW; CorelDRAW can be used to modify the input bitmap and edit the output curves. Adobe Illustrator has a facility to trace individual curves. Automated programs can have mixed results. A program (PowerTRACE) was used to convert a PNG map to SVG. The program did a good job on the map boundaries (the most tedious task in the tracing) and the settings dropped out all the text (small objects). The text was manually re-inserted. Other conversions may not go as well. The results depend on having high-quality scans, reasonable settings, and good algorithms. Scanned images often have a lot of noise, which can require additional work to clean up. == Options == There are many different image styles and possibilities, and no single vectorization method works well on all images. Consequently, vectorization programs have many options that influence the result. One issue is what the predominant shapes are. If the image is of a fill-in form, then it will probably have just vertical and horizontal lines of a constant width. The program's vectorization should take that into account. On the other hand, a CAD drawing may have lines at any angle, there may be curved lines, and there may be several line weights (thick for objects and thin for dimension lines). Instead of (or in addition to) curves, the image may contain outlines filled with the same color. Adobe Streamline allows users to select a combination of line recognition (horizontal and vertical lines), centerline recognition, or outline recognition. Streamline also allows small outline shapes to be thrown out; the notion is such small shapes are noise. The user may set the noise level between 0 and 1000; an outline that has fewer pixels than that setting is discarded. Another issue is the number of colors in the image. Even images that were created as black on white drawings may end up with many shades of gray. Some line-drawing routines employ anti-aliasing; a pixel completely covered by the line will be black, but a pixel that is only partially covered will be gray. If the original image is on paper and is scanned, there is a similar result: edge pixels will be gray. Sometimes images are compressed (e.g., JPEG images), and the compression will introduce gray levels. Many of the vectorization programs will group same-color pixels into lines, curves, or outlined shapes. If each possible color is grouped into its object, there can be an enormous number of objects. Instead, the user is asked to s
Knuth–Plass line-breaking algorithm
The Knuth–Plass algorithm is a line-breaking algorithm designed for use in Donald Knuth's typesetting program TeX. It integrates the problems of text justification and hyphenation into a single algorithm by using a discrete dynamic programming method to minimize a loss function that attempts to quantify the aesthetic qualities desired in the finished output. The algorithm works by dividing the text into a stream of three kinds of objects: boxes, which are non-resizable chunks of content, glue, which are flexible, resizeable elements, and penalties, which represent places where breaking is undesirable (or, if negative, desirable). The loss function, known as "badness", is defined in terms of the deformation of the glue elements, and any extra penalties incurred through line breaking. Making hyphenation decisions follows naturally from the algorithm, but the choice of possible hyphenation points within words, and optionally their preference weighting, must be performed first, and that information inserted into the text stream in advance. Knuth and Plass' original algorithm does not include page breaking, but may be modified to interface with a pagination algorithm, such as the algorithm designed by Plass in his PhD thesis. Typically, the cost function for this technique should be modified so that it does not count the space left on the final line of a paragraph; this modification allows a paragraph to end in the middle of a line without penalty. The same technique can also be extended to take into account other factors such as the number of lines or costs for hyphenating long words. == Computational complexity == A naive brute-force exhaustive search for the minimum badness by trying every possible combination of breakpoints would take an impractical O ( 2 n ) {\displaystyle O(2^{n})} time. The classic Knuth-Plass dynamic programming approach to solving the minimization problem is a worst-case O ( n 2 ) {\displaystyle O(n^{2})} algorithm but usually runs much faster, in close to linear time. Solving for the Knuth-Plass optimum can be shown to be a special case of the convex least-weight subsequence problem, which can be solved in O ( n ) {\displaystyle O(n)} time. Methods to do this include the SMAWK algorithm. == Simple example of minimum raggedness metric == For the input text AAA BB CC DDDDD with line width 6, a greedy algorithm that puts as many words on a line as possible while preserving order before moving to the next line, would produce: ------ Line width: 6 AAA BB Remaining space: 0 CC Remaining space: 4 DDDDD Remaining space: 1 The sum of squared space left over by this method is 0 2 + 4 2 + 1 2 = 17 {\displaystyle 0^{2}+4^{2}+1^{2}=17} . However, the optimal solution achieves the smaller sum 3 2 + 1 2 + 1 2 = 11 {\displaystyle 3^{2}+1^{2}+1^{2}=11} : ------ Line width: 6 AAA Remaining space: 3 BB CC Remaining space: 1 DDDDD Remaining space: 1 The difference here is that the first line is broken before BB instead of after it, yielding a better right margin and a lower cost 11.
Exploratory search
Exploratory search is a specialization of information exploration which represents the activities carried out by searchers who are: unfamiliar with the domain of their goal (i.e. need to learn about the topic in order to understand how to achieve their goal) or unsure about the ways to achieve their goals (either the technology or the process) or unsure about their goals in the first place. Exploratory search is distinguished from known-item search, for which the searcher has a particular target in mind. Consequently, exploratory search covers a broader class of activities than typical information retrieval, such as investigating, evaluating, comparing, and synthesizing, where new information is sought in a defined conceptual area; exploratory data analysis is another example of an information exploration activity. Typically, therefore, such users generally combine querying and browsing strategies to foster learning and investigation. == History == Exploratory search is a topic that has grown from the fields of information retrieval and information seeking but has become more concerned with alternatives to the kind of search that has received the majority of focus (returning the most relevant documents to a Google-like keyword search). The research is motivated by questions like "What if the user doesn't know which keywords to use?" or "What if the user isn't looking for a single answer?" Consequently, research has begun to focus on defining the broader set of information behaviors in order to learn about the situations when a user is, or feels, limited by only having the ability to perform a keyword search. In the last few years, a series of workshops has been held at various related and key events. In 2005, the Exploratory Search Interfaces workshop focused on beginning to define some of the key challenges in the field. Since then a series of other workshops has been held at related conferences: Evaluating Exploratory Search at SIGIR06 and Exploratory Search and HCI at CHI07 (in order to meet with the experts in human–computer interaction). In March 2008, an Information Processing and Management special issue focused particularly on the challenges of evaluating exploratory search, given the reduced assumptions that can be made about scenarios of use. In June 2008, the National Science Foundation sponsored an invitational workshop to identify a research agenda for exploratory search and similar fields for the coming years. == Research challenges == === Important scenarios === With the majority of research in the information retrieval community focusing on typical keyword search scenarios, one challenge for exploratory search is to further understand the scenarios of use for when keyword search is not sufficient. An example scenario, often used to motivate the research by mSpace, states: if a user does not know much about classical music, how should they even begin to find a piece that they might like. Similarly, for patients or their carers, if they don't know the right keywords for their health problems, how can they effectively find useful health information for themselves? === Designing new interfaces === With one of the motivations being to support users when keyword search is not enough, some research has focused on identifying alternative user interfaces and interaction models that support the user in different ways. An example is faceted search which presents diverse category-style options to the users, so that they can choose from a list instead of guess a possible keyword query. Many of the interactive forms of search, including faceted browsers, are being considered for their support of exploratory search conditions. Computational cognitive models of exploratory search have been developed to capture the cognitive complexities involved in exploratory search. Model-based dynamic presentation of information cues are proposed to facilitate exploratory search performance. === Evaluating interfaces === As the tasks and goals involved with exploratory search are largely undefined or unpredictable, it is very hard to evaluate systems with the measures often used in information retrieval. Accuracy was typically used to show that a user had found a correct answer, but when the user is trying to summarize a domain of information, the correct answer is near impossible to identify, if not entirely subjective (for example: possible hotels to stay in Paris). In exploration, it is also arguable that spending more time (where time efficiency is typically desirable) researching a topic shows that a system provides increased support for investigation. Finally, and perhaps most importantly, giving study participants a well specified task could immediately prevent them from exhibiting exploratory behavior. === Models of exploratory search behavior === There have been recent attempts to develop a process model of exploratory search behavior, especially in social information system (e.g., see models of collaborative tagging. The process model assumes that user-generated information cues, such as social tags, can act as navigational cues that facilitate exploration of information that others have found and shared with other users on a social information system (such as social bookmarking system). These models provided extension to existing process model of information search that characterizes information-seeking behavior in traditional fact-retrievals using search engines. Recent development in exploratory search is often concentrated in predicting users' search intents in interaction with the user. Such predictive user modeling, also referred as intent modeling, can help users to get accustomed to a body of domain knowledge and help users to make sense of the potential directions to be explored around their initial, often vague, expression of information needs. == Major figures == Key figures, including experts from both information seeking and human–computer interaction, are: Marcia Bates Nicholas Belkin Gary Marchionini m.c. schraefel Ryen W. White
Sardinas–Patterson algorithm
In coding theory, the Sardinas–Patterson algorithm is a classical algorithm for determining in polynomial time whether a given variable-length code is uniquely decodable, named after August Albert Sardinas and George W. Patterson, who published it in 1953. The algorithm carries out a systematic search for a string which admits two different decompositions into codewords. As Knuth reports, the algorithm was rediscovered about ten years later in 1963 by Floyd, despite the fact that it was at the time already well known in coding theory. == Idea of the algorithm == Consider the code { a ↦ 1 , b ↦ 011 , c ↦ 01110 , d ↦ 1110 , e ↦ 10011 } {\displaystyle \{\,{\texttt {a}}\mapsto {\texttt {1}},{\texttt {b}}\mapsto {\texttt {011}},{\texttt {c}}\mapsto {\texttt {01110}},{\texttt {d}}\mapsto {\texttt {1110}},{\texttt {e}}\mapsto {\texttt {10011}}\,\}} . This code, which is based on an example by Berstel, is an example of a code which is not uniquely decodable, since the string 011101110011 can be interpreted as the sequence of codewords 01110 – 1110 – 011, but also as the sequence of codewords 011 – 1 – 011 – 10011. Two possible decodings of this encoded string are thus given by cdb and babe. In general, a codeword can be found by the following idea: In the first round, we choose two codewords x 1 {\displaystyle x_{1}} and y 1 {\displaystyle y_{1}} such that x 1 {\displaystyle x_{1}} is a prefix of y 1 {\displaystyle y_{1}} , that is, x 1 w = y 1 {\displaystyle x_{1}w=y_{1}} for some "dangling suffix" w {\displaystyle w} . If one tries first x 1 = 011 {\displaystyle x_{1}={\texttt {011}}} and y 1 = 01110 {\displaystyle y_{1}={\texttt {01110}}} , the dangling suffix is w = 10 {\displaystyle {\texttt {w}}={\texttt {10}}} . If we manage to find two sequences x 2 , … , x p {\displaystyle x_{2},\ldots ,x_{p}} and y 2 , … , y q {\displaystyle y_{2},\ldots ,y_{q}} of codewords such that x 2 ⋯ x p = w y 2 ⋯ y q {\displaystyle x_{2}\cdots x_{p}=wy_{2}\cdots y_{q}} , then we are finished: For then the string x = x 1 x 2 ⋯ x p {\displaystyle x=x_{1}x_{2}\cdots x_{p}} can alternatively be decomposed as y 1 y 2 ⋯ y q {\displaystyle y_{1}y_{2}\cdots y_{q}} , and we have found the desired string having at least two different decompositions into codewords. In the second round, we try out two different approaches: the first trial is to look for a codeword that has w as prefix. Then we obtain a new dangling suffix w, with which we can continue our search. If we eventually encounter a dangling suffix that is itself a codeword (or the empty word), then the search will terminate, as we know there exists a string with two decompositions. The second trial is to seek for a codeword that is itself a prefix of w. In our example, we have w = 10 {\displaystyle w={\texttt {10}}} , and the sequence 1 is a codeword. We can thus also continue with w = 0 {\displaystyle w={\texttt {0}}} as the new dangling suffix. == Precise description of the algorithm == The algorithm is described most conveniently using quotients of formal languages. In general, for two sets of strings D and N, the (left) quotient N − 1 D {\displaystyle N^{-1}D} is defined as the residual words obtained from D by removing some prefix in N. Formally, N − 1 D = { y ∣ x y ∈ D and x ∈ N } {\displaystyle N^{-1}D=\{\,y\mid xy\in D~{\textrm {and}}~x\in N\,\}} . Now let C {\displaystyle C} denote the (finite) set of codewords in the given code. The algorithm proceeds in rounds, where we maintain in each round not only one dangling suffix as described above, but the (finite) set of all potential dangling suffixes. Starting with round i = 1 {\displaystyle i=1} , the set of potential dangling suffixes will be denoted by S i {\displaystyle S_{i}} . The sets S i {\displaystyle S_{i}} are defined inductively as follows: S 1 = C − 1 C ∖ { ε } {\displaystyle S_{1}=C^{-1}C\setminus \{\varepsilon \}} . Here, the symbol ε {\displaystyle \varepsilon } denotes the empty word. S i + 1 = C − 1 S i ∪ S i − 1 C {\displaystyle S_{i+1}=C^{-1}S_{i}\cup S_{i}^{-1}C} , for all i ≥ 1 {\displaystyle i\geq 1} . The algorithm computes the sets S i {\displaystyle S_{i}} in increasing order of i {\displaystyle i} . As soon as one of the S i {\displaystyle S_{i}} contains a word from C or the empty word, then the algorithm terminates and answers that the given code is not uniquely decodable. Otherwise, once a set S i {\displaystyle S_{i}} equals a previously encountered set S j {\displaystyle S_{j}} with j < i {\displaystyle j Pyramid, or pyramid representation, is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling. Pyramid representation is a predecessor to scale-space representation and multiresolution analysis. == Pyramid generation == There are two main types of pyramids: lowpass and bandpass. A lowpass pyramid is made by smoothing the image with an appropriate smoothing filter and then subsampling the smoothed image, usually by a factor of 2 along each coordinate direction. The resulting image is then subjected to the same procedure, and the cycle is repeated multiple times. Each cycle of this process results in a smaller image with increased smoothing, but with decreased spatial sampling density (that is, decreased image resolution). If illustrated graphically, the entire multi-scale representation will look like a pyramid, with the original image on the bottom and each cycle's resulting smaller image stacked one atop the other. A bandpass pyramid is made by forming the difference between images at adjacent levels in the pyramid and performing image interpolation between adjacent levels of resolution, to enable computation of pixelwise differences. == Pyramid generation kernels == A variety of different smoothing kernels have been proposed for generating pyramids. Among the suggestions that have been given, the binomial kernels arising from the binomial coefficients stand out as a particularly useful and theoretically well-founded class. Thus, given a two-dimensional image, we may apply the (normalized) binomial filter (1/4, 1/2, 1/4) typically twice or more along each spatial dimension and then subsample the image by a factor of two. This operation may then proceed as many times as desired, leading to a compact and efficient multi-scale representation. If motivated by specific requirements, intermediate scale levels may also be generated where the subsampling stage is sometimes left out, leading to an oversampled or hybrid pyramid. With the increasing computational efficiency of CPUs available today, it is in some situations also feasible to use wider supported Gaussian filters as smoothing kernels in the pyramid generation steps. === Gaussian pyramid === In a Gaussian pyramid, subsequent images are weighted down using a Gaussian average (Gaussian blur) and scaled down. Each pixel containing a local average corresponds to a neighborhood pixel on a lower level of the pyramid. This technique is used especially in texture synthesis. === Laplacian pyramid === A Laplacian pyramid is very similar to a Gaussian pyramid but saves the difference image of the blurred versions between each levels. Only the smallest level is not a difference image to enable reconstruction of the high resolution image using the difference images on higher levels. This technique can be used in image compression. === Steerable pyramid === A steerable pyramid, developed by Simoncelli and others, is an implementation of a multi-scale, multi-orientation band-pass filter bank used for applications including image compression, texture synthesis, and object recognition. It can be thought of as an orientation selective version of a Laplacian pyramid, in which a bank of steerable filters are used at each level of the pyramid instead of a single Laplacian or Gaussian filter. == Applications of pyramids == === Alternative representation === In the early days of computer vision, pyramids were used as the main type of multi-scale representation for computing multi-scale image features from real-world image data. More recent techniques include scale-space representation, which has been popular among some researchers due to its theoretical foundation, the ability to decouple the subsampling stage from the multi-scale representation, the more powerful tools for theoretical analysis as well as the ability to compute a representation at any desired scale, thus avoiding the algorithmic problems of relating image representations at different resolution. Nevertheless, pyramids are still frequently used for expressing computationally efficient approximations to scale-space representation. === Detail manipulation === Levels of a Laplacian pyramid can be added to or removed from the original image to amplify or reduce detail at different scales. However, detail manipulation of this form is known to produce halo artifacts in many cases, leading to the development of alternatives such as the bilateral filter. Some image compression file formats use the Adam7 algorithm or some other interlacing technique. These can be seen as a kind of image pyramid. Because those file format store the "large-scale" features first, and fine-grain details later in the file, a particular viewer displaying a small "thumbnail" or on a small screen can quickly download just enough of the image to display it in the available pixels—so one file can support many viewer resolutions, rather than having to store or generate a different file for each resolution. Algorithmic management is a term used to describe certain labor management practices in the contemporary digital economy. In scholarly uses, the term was initially coined in 2015 by Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish to describe the managerial role played by algorithms on the Uber and Lyft platforms, but has since been taken up by other scholars to describe more generally the managerial and organisational characteristics of platform economies. However, digital direction of labor was present in manufacturing already since the 1970s and algorithmic management is becoming increasingly widespread across a wide range of industries. The concept of algorithmic management can be broadly defined as the delegation of managerial functions to algorithmic and automated systems. Algorithmic management has been enabled by "recent advances in digital technologies" which allow for the real-time and "large-scale collection of data" which is then used to "improve learning algorithms that carry out learning and control functions traditionally performed by managers". The term does not refer to a specific underlying technology, and encompasses the design choices, organisational policies, and governance that surround the managerial use of algorithms in workplaces. In the contemporary workplace, firms employ an ecology of accounting devices, such as "rankings, lists, classifications, stars and other symbols' in order to effectively manage their operations and create value without the need for traditional forms of hierarchical control." Many of these devices fall under the label of what is called algorithmic management, and were first developed by companies operating in the sharing economy or gig economy, functioning as effective labor and cost cutting measures. The Data&Society explainer of the term, for example, describes algorithmic management as 'a diverse set of technological tools and techniques that structure the conditions of work and remotely manage workforces. Data&Society also provides a list of five typical features of algorithmic management: Prolific data collection and surveillance of workers through technology; Real-time responsiveness to data that informs management decisions; Automated or semi-automated decision-making; Transfer of performance evaluations to rating systems or other metrics; and The use of "nudges" and penalties to indirectly incentivize worker behaviors. Proponents of algorithmic management claim that it "creates new employment opportunities, better and cheaper consumer services, transparency and fairness in parts of the labour market that are characterised by inefficiency, opacity and capricious human bosses." On the other hand, critics of algorithmic management claim that the practice leads to several issues, especially as it impacts the employment status of workers managed by its new array of tools and techniques. == History of the term == "Algorithmic management" was first described by Lee, Kusbit, Metsky, and Dabbish in 2015 in their study of the Uber and Lyft platforms. In their study, Lee et al. termed "software algorithms that assume managerial functions and surrounding institutional devices that support algorithms in practice" algorithmic management. Software algorithms, it was said, are increasingly used to "allocate, optimize, and evaluate work" by platforms in managing their vast workforces. In Lee et al.'s paper on Uber and Lyft this included the use of algorithms to assign work to drivers, as mechanisms to optimise pricing for services, and as systems for evaluating driver performance. In 2016, Alex Rosenblat and Luke Stark sought to extend on this understanding of algorithmic management "to elucidate on the automated implementation of company policies on the behaviours and practices of Uber drivers." Rosenblat and Stark found in their study that algorithmic management practices contributed to a system beset by power asymmetries, where drivers had little control over "critical aspects of their work", whereas Uber had far greater control over the labor of its drivers. Since this time, studies of algorithmic management have extended the use of the term to describe the management practices of various firms, where, for example, algorithms "are taking over scheduling work in fast food restaurants and grocery stores, using various forms of performance metrics ad even mood... to assign the fastest employees to work in peak times." Algorithmic management is seen to be especially prevalent in gig work on platforms, such as on Upwork and Deliveroo, and in the sharing economy, such as in the case of Airbnb. Furthermore, recent research has defined sub-constructs that fall under the umbrella term of algorithmic management, for example, "algorithmic nudging". A Harvard Business Review article published in 2021 explains: "Companies are increasingly using algorithms to manage and control individuals not by force, but rather by nudging them into desirable behavior — in other words, learning from their personalized data and altering their choices in some subtle way." While the concept builds on nudging theory popularized by University of Chicago economist Richard Thaler and Harvard Law School professor Cass Sunstein, "due to recent advances in AI and machine learning, algorithmic nudging is much more powerful than its non-algorithmic counterpart. With so much data about workers' behavioral patterns at their fingertips, companies can now develop personalized strategies for changing individuals' decisions and behaviors at large scale. These algorithms can be adjusted in real-time, making the approach even more effective." == Relationships with other labor management practices == Algorithmic management has been compared and contrasted with other forms of management, such as Scientific management approaches, as pioneered by Frederick Taylor in the early 1900s. Henri Schildt has called algorithmic management "Scientific management 2.0", where management "is no longer a human practice, but a process embedded in technology." Similarly, Kathleen Griesbach, Adam Reich, Luke Elliott-Negri, and Ruth Milkman suggest that, while "algorithmic control over labor may be relatively new, it replicates many features of older mechanisms of labor control." On the other hand, some commentators have argued that algorithmic management is not simply a new form of Scientific management or digital Taylorism, but represents a distinct approach to labor control in platform economies. David Stark and Ivana Pais, for example, state that, "In contrast to Scientific Management at the turn of the twentieth century, in the algorithmic management of the twenty-first century there are rules but these are not bureaucratic, there are rankings but not ranks, and there is monitoring but it is not disciplinary. Algorithmic management does not automate bureaucratic structures and practices to create some new form of algorithmic bureaucracy. Whereas the devices and practices of Taylorism were part of a system of hierarchical supervision, the devices and practices of algorithmic management take place within a different economy of attention and a new regime of visibility. Triangular rather than vertical, and not as a panopticon, the lines of vision in algorithmic management are not lines of supervision." Similarly, Data&Society's explainer for algorithmic management claims that the practice represents a marked departure from earlier management structures that more strongly rely on human supervisors to direct workers. In analyzing the difference and the similarities to previous management styles, David Stark and Pieter Vanden Broeck expand the applicability of algorithmic management beyond the workplace. They develop a theory of algorithmic management in terms of broader changes in the shape and structure of organization in the 21st century, attentive to the erosion of organization's boundaries whereby heterogeneous actors, assets, and activities, are coopted regardless of their place in organizational space. Stark and Vanden Broeck propose the following means of differentiating algorithmic management from other historical managerial paradigms: == Issues == Algorithmic management can provide an effective and efficient means of workforce control and value creation in the contemporary digital economy. However, commentators have highlighted several issues that algorithmic management poses, especially for the workers it manages. Criticisms of the practice often highlight several key issues pertaining to algorithmic management practices, such as the imperfection and scope of its surveillance and control measures, which also threaten to lock workers out of key decision-making processes; its lack of transparency for users and information asymmetries; its potential for bias and discrimination; its dehumanizing tendencies; and its potential to create conditions which sidestep traditional employer-employee accountability. This last point has been especiPyramid (image processing)
Algorithmic management