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Okay. Coded Bias is a documentary film that challenges the idea of technology being neutral, arguing that computers are biased in default as it reflects the faulty beliefs of those who design them. The film explores the underlying causes of such issues and acts as both a wake-up call and a call to action by drawing attention to an intrusive occurrence and exposing the blindspots in poor AI systems. Most people are unaware that they have already given algorithms access to a great deal of personal information for the sake of efficiency. Yet, Coded Bias exposes how these predatory algorithms are programmed for failure. I am optimized for efficiency. I have an intimate view of your company and can make predictions for your future now. I can simply use your historical data to make predictions. I will approach things based on what I am programmed for. I can easily define and classify your market—their demands and needs. I can determine if they would want your product or service, then target them with my algorithm. Tell me, how do you want your demand in 2024 to be? If I program your 2024 forecast based on that, there may be biases that will result in inaccurate predictions for your future business operations. Do you still want to proceed? Business predictions are prone to inaccuracy due to biases that cause forecasts to be under or over the actual outcomes. As a result, there is a significant risk of being poorly prepared and the inability to satisfy consumer demands. Inadequate data, human error, and bias are some of the factors that contribute to forecast biases. Aside from historical data, research should be conducted and recent market data should be considered to make a reliable prediction. Human error, on the other hand, can occur when business leaders let their feelings of optimism or pessimism affect their forecasts like being overly confident about how well a particular strategy would work and the number of sales the product would generate. I, too, may target and analyze the wrong people. My efficiency may not always give you accuracy. The volume of fashion surplus and the billions of unsold garments reveal the fatal bias in the retail forecast. Aside from personal preferences and a brand's retail data, there are still other main sources of data. Firstly, the retail data gathered from websites, followed by the data from social media. There is a considerable bias in retail data. According to my source, the demand for products from digital channels has a forecast inaccuracy of more than 50%. From an accuracy standpoint, adapting trends does not necessarily convert into business success. Ensuring that business predictions are accurate requires understanding how to determine forecast bias. Acknowledging biases improves the future planning of business leaders and can aid the business in better serving its target market. Overall, lowering the risk of a forecast bias can help the management set more realistic targets for the company as a whole. The data I analyze will never be 100% accurate. I evaluate data on the various facets of your company, but I am not immune to bias—and that is not good for your forecasts. To ensure that the future of your company is not solely decided by an algorithm operating in a black box, it is necessary to support them with solid, pertinent business perspectives based on various internal and external factors. Remember, algorithms by themselves are perilous.