In recent years, society and technology are becoming more diverse and inclusive concerning human wants and needs. People are finally starting to support one another regardless of who they are and how they identify themselves in society. Many activists have been a major influence in breaking the gender norms that were often classified as taboo or “unacceptable” behavior. Thank you for busting the centuries of abandonment for the millennials. However, in a recent investigation, we found that the majority of facial recognition technology is only 60% effective in identifying one’s gender and race. The machines created by humans are still old school. The accuracy and advancement of technology need to be more efficient to enable greater individualization.
The world is now moving towards AI and automation. Sensor-based systems are going to be located all over the world. Documentations, form filling, and other sorts of data entry work are to be automated almost everywhere. In such cases, it is important for machines to co-evolve with humans.
A survey made by pew researchers says that only facial recognition system in law enforcement for criminal records are used responsibly, while tech companies, advertising agencies among other nationality finding surveys are an indication of major failure in using facial recognition system ref:https://www.pewresearch.org/internet/2019/09/05/more-than-half-of-u-s-adults-trust-law-enforcement-to-use-facial-recognition-responsibly/
A study at Harvard University says the National Institute of Standards and Technology (NIST) has confirmed, finding that face recognition technologies across 189 algorithms are the least accurate on women of color. The study also says that there has been racial discrimination and inequity in facial recognition algorithms which many people find offensive, inaccurate, and rife with their privacy concerns.
A form of machine learning called deep learning is the key factor to these image recognition software. So far Computer driver image recognition software is limited with information to just identify genders of male and female only. Deep learning systems are often “trained” to see which elements of the face are most important to helping the model classify men and women.
Thousands of raw data are collected and are manually processed by humans to identify the gender. A set of commands are coded with various dimensions for the models to operate at scale. It is not feasible for businesses to use labor hours to analyze and classify images based on gender, race, and ethnicity when a simple algorithm can cluster the work within a fraction of a second. But a simple machine learning mistake can affect the business immensely. It’s always best to teach models using proper parameters and attributes removing the unwanted dimensions using dimensionality reduction techniques to get accurate results.
Proper classification and multiple evaluations become mandatory to identify gender in a diverse well trained set of images to achieve accuracy.
Desicrew understands the difficulties of how these systems can be challenging in bringing out the best of performance for your business. Quality testing is done after each process by our trained resources. 1000 plus annotators are constantly involved in data collection and enrichment using high-end data integration tools to facilitate your most complex workflow agile.