How can we solve the problems of gender bias in ai?

Artificial intelligence (AI) has a bias problem. Actually, AI has a lot of well-documented bias problems.

How can we solve the problems of gender bias in ai?

Artificial intelligence (AI) has a bias problem. Actually, AI has a lot of well-documented bias problems. Arguably, the main one is gender bias. Developers and researchers of AI systems have been trying to establish rules on how to avoid biases in AI models.

As a result, many AI systems, which are based on such biased data and which have often been created by mostly male teams, have had significant problems with women, from credit card companies, which seem to offer more generous credit to men, to instruments that can detect all kinds of conditions, from COVID to liver disease. If AI considers that gender is limited to masculine and feminine, this is not in line with modern perspectives on non-binary and transgender expression, which could cause harm to these communities. But what if the system completes “The man is for the software developer” like the woman is for the X with “secretary” or some other word that reflects stereotypical views on gender and careers? AI models, called word embeddings, learn by identifying patterns in huge collections of texts. For example, Digital Democracy, an organization that works with marginalized communities to defend their rights through technology, worked with local community groups, such as the Commission for Women Victims for Victims (KOFAVIV), to create a secure system for collecting data on gender-based violence in Haiti.

For example, translation software, which learns from large amounts of text online, has historically used gender-neutral terms (such as “the doctor” or “the nurse” in English) and has returned gendered translations (such as “the doctor” and “the nurse”, respectively, in Spanish), reinforcing stereotypes about doctors and nurses. AI is a powerful tool that offers us the opportunity to solve problems that were previously unsolvable, from cancer to climate change, but unless the problem of prejudice is addressed, AI risks being unreliable and ultimately irrelevant. Social change leaders, as well as leaders of organizations that develop machine learning systems, have roles to achieve gender-intelligent machine learning and promote gender equity. The benefits of using AI in a responsible manner and of eradicating prejudice wherever it occurs will be considerable, as it will allow business leaders to improve their reputation for trust, fairness and responsibility, while providing real value to their organization, customers and society as a whole.

Workshops such as those organized by the Criterion Institute, which included training on financial investment concepts and gender considerations, helped researchers and professionals with experience in gender to better understand the field of impact investing, as well as to participate in and, ultimately, promote work and investment initiatives with a gender perspective. Gender gaps in AI competencies may worsen gender gaps in terms of participation and economic opportunities in the future, as AI encompasses an increasingly in-demand set of skills. First, it calls for training gender experts in the field of artificial intelligence and participating in the debate by asking the organizers of the conference to organize sessions and workshops on gender and AI. When developing ethical AI governance structures (an AI ethics council and a director), ensure that there is gender diversity.