Introduction Machine learning algorithms are quickly gaining traction in both the private and public sectors for their ability to automate both simple and complex decision-making processes. The vast majority of economic sectors, including transportation, retail, advertisement, and energy, are being disrupted by widespread data digitization and the emerging technologies that leverage it. Computerized systems are being introduced in government operations to improve accuracy and objectivity, and AI is having an impact on democracy and governance [1]. Numerous businesses are using machine learning to analyze massive quantities of data, from calculating credit for loan applications to scanning legal contracts for errors to analyzing employee interactions with customers to detect inappropriate behavior. New tools make it easier than ever for developers to design and deploy machine-learning algorithms [2] [3].

The article states some examples for machine learning bias and ethical dilemmas that occur due to progressive improvement in machine learning based on previous studies by web research. The article evaluates the history of traditional programming to machine learning and explains how ML is implemented and how it lead to efficiency in banking, criminal justice , and medical fields. This also explains the possible bias that can occur by using the algorithmic systems in the society and ethical dilemmas regarding the ML in accordance with previously conducted studies. Finally it explains how to attain a better future with unbiased algorithmic process which will drive the society into a pleasant and fairer offers and decisions.