Analysis of the Ethical dilemma in ICT

Questions: 1. What's going on? 2. What are the facts? 3. What are the issues? 4. Who is affected? 5. What are the ethical issues and their implications? 6. What could have been done about it? 7. What are the options? 8. Which option is the best and why? Answers: 1. John J. Riley center for science, technology and values of University of Notre Dames released an article on 14th December 2016 on predicting criminality ("Predicting Criminality", 2017). A team of researchers from McMaster University Canada and Shanghai Jiao Tong University in China came up with the idea of computer which on the basis of facial features and expression could measure the chances of committing Crime. The machine would analyse the features from the photos of the suspect and predicted the chances on its basis (Glenn, Focquaert Raine, 2015). These pictures can be asserted or taken up from the cameras on streets too. Modern machines will work with help learning algorithms. 2. Advent of science and technology is incorporating Information system in every of it developing machines making the world to rely more and more on machines. These days machines are becoming more of a companion than just a help. The researchers have been on the study to prove that facial expressions are not the right model to detect the criminality or offensive behaviour of a person. Surprisingly, during this the researches rather ended up with a conclusion that facial features may define or detect a persons chance of being criminal ("Predicting Criminality", 2017). Hence the team proceeded further with the notion of developing programs which can read facial features and expression of a photo or person itself. The main discriminating features of criminals and non criminals were attributed as eye corners, philtrum and the mouth area. 3. Every new innovation is always questioned. The issues that this innovation is facing are: Ethical issues Machine could be fed up with predefined data about a person. Loss of dignity if caught being non-guilty. Family of the person held may suffer unnecessarily. Law enforcing body may be held irresponsible. Unethical issues Algorithms and programs could be hacked and misused. No proper code of conduct designed for this technology. Technology issue or inappropriate environment for detection. 4. Bringing this into effect would become a threat for every human being. People who have features that can be similar to a criminal feature may not be right to be treated as criminal. Every human has been designed and have attributes and physical appearance of their own. Judging one by their appearance is quite questionable. The family and related members of the innocent person who has been convicted have to suffer. Not only citizens but also law enforcing team may also suffer with such kind of technology (Tayebi et al., 2014). They may lose the actual criminal and waste much time on someone innocent. A wrong detection may lead to freedom of criminal who may exploit this opportunity to do some greater mischief. 5. It has been well said that one cannot judge a book by its cover. It may be unfortunate for person to look alike a criminal and then be held behind because the wrong analysis done by the machine. Software and algorithms for detection could be altered or hacked. With cameras on every street detecting each face it may be offensive on the persons side to convict someone on streets (Wang, Deng Zhou, 2014). The person held may be innocent but due to that he may lose his dignity in the society. People around may question him of the crime which he knew nothing about. In addition, if someone is held around and is punished which is inhumane. There are far more dangerous outcomes then thought of. Criminals can cheat algorithms by posing being someone who he is not. 6. The area of such concern requires human expertise. One cannot depend on such programming which can be hacked or may be altered on requirement basis. This research should be done with more detailed analysis devising more apt programming for facial recognition (Milliet, Delmont, Margot, 2014). Researches need to take this more seriously. The people responsible can use such technologies but working along with certain proceedings which may prove the suspects guilt completely. It can be authorized to be used under certain circumstances only. 7. The first option being the most appropriate is not using any such technology in the area where possibilities of misconception are too high. Some cases of police using such technologies were heard of which was later dropped off because of its poor efficiency. Government policies can be formed which would allow usage of such technologies but only under certain guidance of ethical code (Glenn Raine, 2014). Modification of algorithm can be done to make the system detection more accurate. 8. Since, the world is heading towards a more digitized era with technologies in every aspect of life this is a good approach but with weaker technicalities. Hence improving the entire system with far more accuracy and ability would be the best option considered (Tayebi et al., 2014). This would at some level or other will help the law enforcing bodies. Improving and working on its flaws will assure criminals get caught and unfortunate innocent may be saved. References Al-Saggaf, Y., Burmeister, O. K. (2012). Improving skill development: an exploratory study comparing a philosophical and an applied ethical analysis technique.Computer Science Education,22(3), 237-255 Glenn, A. L., Raine, A. (2014). Neurocriminology: implications for the punishment, prediction and prevention of criminal behaviour.Nature Reviews Neuroscience,15(1), 54-63. Glenn, A. L., Focquaert, F., Raine, A. (2015). Prediction of antisocial behavior. InHandbook of Neuroethics(pp. 1689-1701). Springer Netherlands. Matheson, S. (2016). DNA Phenotyping: Snapshot of a Criminal.Cell,166(5), 1061-1064. Milliet, Q., Delmont, O., Margot, P. (2014). A forensic science perspective on the role of images in crime investigation and reconstruction.Science Justice,54(6), 470-480. Pollock, J. M. (2014).Ethical dilemmas and decisions in criminal justice. Nelson Education. Predicting Criminality. (2017). Reilly Top 10. Retrieved March 15, 2017, from https://reillytop10.com/2016/12/14/predicting-criminality/ Tayebi, M. A., Ester, M., Glsser, U., Brantingham, P. L. (2014, August). CrimeTracer: activity space based crime location prediction. InAdvances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on(pp. 472-480). IEEE. Wang, G. H., Deng, J. C., Zhou, D. B. (2014). Face Detection Technology Research Based on AdaBoost Algorithm and Haar Features. InUnifying Electrical Engineering and Electronics Engineering(pp. 1223-1231). Springer New York.