A Comparative Study of Gender Development Indexes in the Province of Lorestan in 2006

Introduction Gender inequality is a part of socio-economic inequality in all societies. Overcoming discrimination is most important in the poverty reduction programmes every societies especialy in developing countries and a useful tools for the achievement of the Millennium Development Goals that announced by UNDP. The Gander development index (GDI) is often considered a “gender-sensitive extension of the HDI” (Klasen 245). It addresses gender-gaps in life expectancy, education, and incomes. It uses an “inequality aversion” penalty, which creates a development score penalty for gender gaps in any of the categories of the Human Development Index which include life expectancy, adult literacy, school enrollment, and logarithmic transformations of per-capita income. The GDI cannot be used independently from the Human Development Index (HDI) score and so, it cannot be used on its own as an indicator of gender-gaps. Only the gap between the HDI and the GDI can actually be accurately considered; the GDI on its own is not an independent measure of gender-gaps. In the years since its creation in 1995, much debate has arisen surrounding the reliability, and usefulness of the Gender Development Index (GDI) in making adequate comparisons between different countries and in promoting gender-sensitive development. The GDI is particularly criticized for being often mistakenly interpreted as an independent measure of gender-gaps when it is not, in fact, intended to be interpreted in that way, because it can only be used in combination with the scores from the Human Development Index, but not on its own. Additionally, the data that is needed in order to calculate the GDI is not always readily available in many countries, making the measure very hard to calculate uniformly and internationally (Ibid). So the participation and role of women in various cultural, social, economic and political fields determine levels of development indicators in each country around the world. Existing data’s shows that women's condition in Iran’s during last decades have dual status. In terms of some HDI indicators component such as health and education, women in Iran are in a situation more better than most countries in the region, but their share of participation rate in labor market and income earning is in very low level in compare with other developing countries. In this case Lorestan province is one of the most poor areas. Material & Methods This study’s research method was been second data analysis as a interpretive approach, and critical perspective. While focusing on theoretical and methodological underpinnings of the gender development index be discussed. So this research took about the methods that used here is numerical taxonomy and data matrix - have been used too. Discussion of Results & Conclusions As mentioned in state of problems, here the finding shows that Lorestan gender development indicators (GDI) are in low level. But inter provinces analysis among town and cities shows many different among districts. For examples in field of cultural- educational indicators, cities of Borujerd and Khorramabad occupied high developed rank, and then Doroud and Poldokhtar rank is relatively developed cities, some other cities such as Koohdasht,and Azna been relatively disadvantaged and finally most poor sections of cities are located in Aligoodarz and Delfan. Base on results and this study finding a longtime development plans is needed to try increase and improve of gender development indicators in all cities with focus on poor areas same Delfan and Alighoodarz

A Comparative Study of the Facilitator Application of Artificial Intelligence in Criminal Prosecution; Capacities and Challenges

Nowadays, using the artificial intelligence technologies in law-based criminal sciences has gained a significant place. In the field of substantive criminal law, topics such as the determination of criminal liability due to crimes caused by the performance of robots or self-driving cars are among the most interesting and, of course, the most controversial topics in this field. In the field of procedural criminal law, the use of this technology in the five stages of criminal proceedings has faced many discussions. The main question of this study is whether the technologies related to artificial intelligence can be applied in the process of criminal detection and prosecution or not and what are the challenges facing it in the assumption of application? The results of the current research indicate that the technologies related to artificial intelligence are playing a role in many countries today according to the requirements of different stages of criminal proceedings and taking into account the requirements of each crime. In terms of crime detection and prosecution, a variety of police tools for predicting the time and place of crime and facial recognition technologies (FRT) with the aim of facilitating police actions and moving from "reactive" police to "preventive" police in many parts of Europe and the United States United States have been developed and deployed. What causes the steps to be taken more slowly towards the expansion of the use of this technology in the field of criminal law in general and in the stage of crime detection and prosecution in particular is the existence of challenges such as violation of privacy and freedom of citizens, violation of presumption of innocence and the risk of militarization of criminal justice. The authors believe that using the artificial intelligence technology in the field of detecting and prosecuting crimes is useful and necessary in order to deal with the crime phenomenon as much as possible, but we should not be fascinated in this regard. Using this technology in the important stages of detecting and prosecuting crimes should not conflict with the general principles governing criminal proceedings as well as the rights and freedoms of individuals. In this regard, regulating and establishing special laws can reduce the upcoming concerns to some extent. This is the reason why the need to regulate artificial intelligence is widely discussed, especially in the European region. In this regard, reports and strategic guidelines have been predicted and published