Title: Discussion on explainable AI for Robotic applications

Abstract:

Gender violence is an issue of public health that affects women and children globally. According to the UN, “35 percent of women worldwide have experienced either physical and/or sexual intimate partner violence or non-partner sexual violence” (UN Women, s/f). Indeed, WHO found that “almost one-third of all women who have had a relationship have suffered physical or sexual violence at the hands of their partner” (WHO, 2017). Within the scope of gender violence, femicide is a phenomenon that occurs as a consequence of cycles of violence against a woman, the rates of which continue to grow on a global level. “A total of 87,000 women were intentionally killed in 2017. More than half of them (58 percent) ̶ 50,000 ̶ were killed by intimate partners or family members […] More than a third (30,000) […] were killed by their current or former intimate partner ̶ someone they would normally expect to trust” (UNODC, 2018, pág. 10). Meanwhile, we are seeing great advances in AI and the use of machine learning and deep learning for the creation of algorithms for risk prediction. Tools that aim to determine the level of risk of femicide have been developed in Spain and Canada, for example, Viogen, “The Ontario Domestic Assault Risk Assessment” (ODARA), and “Domestic Violence Risk Appraisal Guide” (DVRAG), etc. When building such tools and considering that risk determination will be carried out by an algorithm, it is pertinent to analyze how the algorithm should be built, how information is collected, how to decide which variables to include or exclude. Also, as the algorithm becomes autonomous thanks to machine learning, the so-called black box plays an important role.  We cannot know the internal workings of the algorithm and how it determines the level of risk. Therefore, the question for an investigation that arises is: Which variables need to be considered when building algorithms to determine risk in the prevention of gender violence? To answer this, an inductive qualitative methodology is used to analyze primary sources, secondary sources, and case studies (algorithms). The results show that there is a need to evaluate situational and trigger factors, as well as factors related to the perpetrator, the victim, and type of relationship (prior violence, threats of homicide)

Biography:

Marcela has completed his Ph.D. at the age of 40 years at Tehran University and postdoctoral studies from Tehran University School of Surveying Geospatial Engineering-Department of Surveying and Geomatics Engineering. He is the director at the Directorate of Engineering and Transportation, a premier service organization. He has published more than 15 papers in reputed journals and has been serving as an editorial board member of repute. He Opened and studied the financial offers and the organization of the fundamental record, supervising the efficiency of electrical generators at the Nseeb border center, and Supervising the efficiency of agricultural machinery at the ministry of agriculture.