Algorithm management is a system in which the algorithm rather than humans decides how business operations should be carried out. It was presented in an attempt to explain how on-demand economies such as UBER manage their workers. There are different types of algorithms like taxi algorithms, calling algorithms, car rental algorithm, bus algorithm, etc. These four algorithms are supposed to achieve the exact same goal, but any of them will do the job. in a completely different way. Each algorithm has a different cost and a different travel time. Algorithms are usually chosen based on circumstances, for example: taking a taxi is probably the quickest way to reach a particular destination, but it is also the most expensive way and taking the bus is certainly cheaper, but also much slower. In computer programming, there are often algorithms in different ways to accomplish a given task. Each algorithm has its own advantages and disadvantages in different situations. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”? Get an original essay Although managing algorithms in an on-demand economy can often paint a vision of a utopian future, the transition will be a process long and arduous which will perhaps never see the light of day and end up demolishing everything. Those developing algorithmic management say it creates new job opportunities, better and cheaper consumer services, transparency and fairness in parts of the labor market that can be categorized by inefficiency and opacity of human bosses. However, the summer of wildcat strikes in London's gig economy shows that some workers are starting to chafe at the contradictions of being their own bosses, as they may be free to choose when to work , but they can't choose how to work, which not only affects their psychology, but also affects their skills outside of a gig economy, because getting used to not using your own brain in the work you do could might as well contribute significantly to bringing us to dystopia. Human relations is the process of training employees, responding to their needs, promoting a corporate culture, and resolving conflicts between different employees or between employees and management. Different people have different needs and therefore expecting algorithms to take into account everyone's needs may not be the right solution. best idea. Face-to-face communication allows us to know our subordinates better and so it is easy to consider their needs to encourage them to work harder, but algorithms present a more generalized way of considering employee needs that does not encourage not always workers to work hard, but more generally. in many cases, monitoring every step might just end the pressure on them and, in turn, make the work environment worse. Estimates suggest that a fifth of employers in Europe had access to wearable technology in 2015, while in the US up to 72% of CVs are not seen by the naked eye. Amazon, Unilever, Deloitte, Tesco and most other big companies have dipped their toes into the waters of algorithmic management, but not everyone is happy with this trend. One of the main reasons for resilience is the fear that it willleads to a digital king Taylorism, taking the principles of scientific management to another level of intrusion. Academic Phoebe Moore has warned us about the threats to work-life balance from algorithms, as they can lead to a culture of hyperemployment, something scientific principles were designed to avoid by first place. The results of a recent ethnographic study of long-haul truck drivers show that electronic monitoring has caused them to feel pressure not to take mandatory breaks, which might as well be the first step toward a dystopian economy. There are algorithms like powerful video surveillance during interviews or those that identify appropriate content in emails that risk creating a culture of guilt until proven innocent. In 2015, a California worker sued her employer after she was fired for uninstalling a cell phone app that tracked her movements 24 hours a day. The plaintiff claimed her boss used the device to monitor her speed driving outside of working hours. Such algorithms can easily offend a person and cause them to question their sense of privacy. In a work environment where scientific principles allow people to have freedom at work by making decisions and having authority over those decisions, algorithms pose a serious threat to people's autonomy. and a feeling of control. For example, delivery drivers' daily rates and schedules are fully mapped by algorithms that infer their sense of control over their own actions, which might as well amount to turning them into robots that listen to what they are told using their brain. But perhaps the biggest complaint is whether they work. Many of these are untested and often found to be patchy and subject to wild fluctuations. In her book Weapons of Maths Destruction, mathematician and technology polemicist Cathy O'Neil explains how a performance algorithm used in the New York City education system earned the same teacher a grade of 6/100 in one year and 96/100 the following year, without change. their teaching style. For critics like Guy Standing, one man's flexibility is another man's insecurity. The gig economy fuels a class of “precariatized” workers deprived of the protection of traditional jobs, he says. Algorithms offer “fantastic opportunities for rapacious exploitation” of people who are already at the bottom of the job market. “They can monitor and make sure they're only paying for the time they really want to pay for, and have people available at all times, waiting on call.” In my opinion, an important part of why Taylor came up with these principles was to avoid exploiting workers in any way and giving them what they deserve, but companies use algorithms as an excuse to pay their employees only for the work they truly deserve. Workers want to pay, not the work they deserve to be paid for. Technology cannot be described as a uniform mass of tools but rather as a multitude of devices that have different consequences for workers. Much depends on how algorithms are developed, including how data is collected, how the collected data is analyzed, and how the results are interpreted and acted upon. We cannot..