One of the biggest technological advances that we're about now is artificial intelligence,

As we gradually emerge from the pandemic with tech more deeply embedded in our daily lives, issues relating to the scope and use of the vast amounts of data being collected, such as data security, and the use of predictive technologies and artificial intelligence (AI) are becoming increasingly important. This rise in prominence has meant that awareness of the'darker' side of AI, as highlighted by mainstream hits on Netflix such as Coded Bias, and specifically the ways in which machine learning algorithms have led to greater discrimination and inequalities is on the rise. Technology itself, and the data we collect on a daily basis, is not inherently'good' or'bad'. However, as humans, we can program our biases into the technology we create. Existing racial, social, and gender biases can be programmed into the algorithms we develop, often unconsciously, or the data that are available to train algorithms, biased in nature, inevitably affects the outputs.

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. Firstly, it introduces a taxonomy to analyze the field of machine ethics from an ethical, implementational, and technical perspective. Secondly, an exhaustive selection and description of relevant works is presented. Thirdly, applying the new taxonomy to the selected works, dominant research patterns and lessons for the field are identified, and future directions for research are suggested.

The following article is based on work done for my graduate thesis titled: Ethics and Bias in Machine Learning: A Technical Study of What Makes Us "Good," covering the limitations of machine learning algorithms when it comes to inclusivity and fairness. As Cathy O'Neil discusses in her book, Weapons of Math Destruction, the seeming impenetrability and absolute value of machine learning may not be all that we bargained for. Though machine learning appears to indisputably increase business value and efficiency, in some cases, it can sow inequality deeper by hard-coding it into our machines. It is imperative that machine learning experts, creators, and contributors account for "doing the right thing" as much as they do "meeting the bottom line" to balance the enormous power these mechanical decision makers possess. A machine learning algorithm is typically code written by a data scientist in a programming language such as R, Python, or Javascript.

At the Institute of Cognitive Science at Osnabrück University, Germany, virtual reality has become a training ground for machine morality. Leon René Sütfeld, a Ph.D. student in cognitive science, studies human responses to danger and obstacles in traffic scenarios, using this data to train and evaluate decision-making models for algorithms. The need for ethical machines may be one of the defining issues of our time. Algorithms are created to govern critical systems in our society, from banking to medicine, but with no concept of right and wrong, machines cannot understand the repercussions of their actions. A machine has never thrown a punch in a schoolyard fight, cheated on a test or a relationship, or been rapt with the special kind of self-doubt that funds our cosmetic and pharmaceutical industries.

STORRS, Connecticut--The prospect of artificial intelligence (AI) has long been a source of knotty ethical questions. But the focus has often been on how we, the creators, can and should use advanced robots. What is missing from the discussion is the need to develop a set of ethics for the machines themselves, together with a means for machines to resolve ethical dilemmas as they arise. Only then can intelligent machines function autonomously, making ethical choices as they fulfill their tasks, without human intervention. There are many activities that we would like to be able to turn over entirely to autonomously functioning machines.

But the focus has often been on how we, the creators, can and should use advanced robots. What is missing from the discussion is the need to develop a set of ethics for the machines themselves, together with a means for machines to resolve ethical dilemmas as they arise. Only then can intelligent machines function autonomously, making ethical choices as they fulfill their tasks, without human intervention. There are many activities that we would like to be able to turn over entirely to autonomously functioning machines. Robots can do jobs that are highly dangerous or exceedingly unpleasant.

The notion of an ethical machine can be interpreted in more than one way. Perhaps the most important interpretation is a machine that can generalize from existing literature to infer one or more consistent ethical systems and can work out their consequences. An ultra-intelligent machine should be able to do this, and that is one reason for not fearing it.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.