The ethics of how a Machine Learning (ML) or an Artificially Intelligent (AI) system is to work is a typical believed that emerges when we read about huge headways in those fields. Will this awareness assume control humankind? Or on the other hand will it enable us to achieve an Utopian period? It’s certainly not a twofold inquiry. Be that as it may, one of the less ordinarily made inquiries (and maybe which is all well and good) is “Was this fabricated and established with the correct temperances?”. What’s more, this inquiry concerns less about the inspiration driving building a ML system than it appears. On the off chance that you have no involvement or no information about what a ML system is, consider it a black box. A black box which when suggested with a conversation starter yields an answer that has a high probability of being right. So as to get this high probability, we have to set up the black box first. By and by, we endeavor to make an arrangement of many black boxes and pick the one with the most astounding exactness. To fabricate these we require loads of data and an algorithm. Think about the data as a not insignificant rundown of inquiries with amend answers. The algorithm gains from this data. Each black box in a set has a somewhat extraordinary form of a similar algorithm. At last, we pick the form that is most exact (in fact called tuning the hyperparameters). In this paper examines different types of ethical issues and where they may appear in different ML application using case scenarios.MethodologyBy using case scenarios to develop mindfulness on specific issues that may relate to Machine Learning and Artificial Intelligence. There will be two distinctive use cases, each covering diverse parts of moral or ethical issues that we may experience in this time of machine learning and AI. Along these use case there will be bits of knowledge on some other expanded issues that we may reveal.Case ScenariosThomas is a 30-year-old air traffic controller who was told by his boss that starting this month he would have to undergo attention training by wearing a new biotechnological tool that provides him with neurofeedback. Thomas does not know exactly what the device does, but he feels that his attention has somewhat improved since he started training. The explanation that Thomas was given about the new tool was that it somehow reads his brain, so he is sometimes afraid the tool can also read his thoughts. Also, last Monday Thomas got a lecture from his boss, who said he could see that Thomas most likely had been drinking alcohol on Sunday night.3A bank using machine learning algorithm to recommend mortgage applications for approval. A rejected applicant brings a lawsuit against the bank, alleging that the algorithm is discriminating racially against mortgage applicants. The bank replies that this is impossible, since the algorithm is deliberately blinded to the race of the applicants. Indeed, that was part of the bank’s rationale for implementing the system. Even so, statistics shows that the bank’s approval rates for black applicants has been steadily dropping. Submitting ten apparently equally qualified genuine applicants shows that the algorithm accepts white applicants and rejects black applicants. 1 DiscussionStarting with the case scenario of the bank, on the off chance that the machine learning algorithm depends on a convoluted neural network, or a hereditary algorithm delivered by directed evolution, at that point it might demonstrate close unimaginable or comprehend why or even how, the algorithm is judging candidates in view of their race. Then again, a machine learner in light of decision trees or Bayesian networks are significantly more straightforward to developer review, which may empower a programmer to find that the AI algorithm utilizes the address data of candidates who were conceived or already dwelled in overwhelmingly neediness stricken zones. “Responsibility, auditability, transparency, predictability, incorruptibility and a tendency to not make innocent victims scream with helpless frustration” are some of the thing that the computing society should be keeping in mind when developing new machine learning algorithms and model. 1The scenario of Thomas (albeit somewhat futuristic) touches upon more philosophical topics such as extended and enacted mind, but most importantly it raises concern for mind reading and privacy. As this case scenario illustrates, employers may be able to gain more information than had been agreed upon when they require an employee to use a BCI system. Furthermore, the subject may be completely unaware of the extent of information that is being obtained from his or her brain.” For this situation, the business could perceive the way that the subject may have been drinking the prior night, however more by and large a BCI system might have the capacity to uncover other mental states, qualities, and psychological wellness vulnerabilities. It may not be in a person’s best enthusiasm to have this individual data accessible to others, particularly a business, and working environment separation could be a worry. It could be viewed as an infringement of a man’s entitlement to security. Besides, the case situation raises worries about social stratification and cerebrum enhancement. If mind upgrade becomes successful and well known, there could be strain to improve one’s cerebrum to stay aware of the opposition. Boundaries, for example, cost could keep a few people from getting to this upgrade. This issue, however entangled, isn’t remarkable to BCI and is additionally talked about somewhere else.