Breakthroughs in technological developments in the last decade triggered a digital revolution with palpable consequences in our daily lives. The World Wide Web became a colossal source of data storage that sees and remembers everything. Humans’ dependency on technology devices is so profound that fusion with machines becomes inevitable. A milestone in triggering or speeding up the digital revolution was the disruption of AI’s deep learning, i.e. the possibility to create artificial intelligence capable to learn like a human but much faster and effectively. AI became of widespread use in nearly all of our daily life activities, and by the great majority of government agencies and private companies.
The use of AI systems is even more predominant in scientific research, especially in STEM. Yet, indicators on the risks of the use of AI systems reveal discrimination to detriment of social groups in situations of vulnerability, including women and the LGBTQ+ Community. As experts point out algorithms were initially created to be neutral and fair by avoiding all-too-human biases and faulty logic. However, many of the algorithms used today, from the insurance market to the justice system, have incorporated the very prejudices and misconceptions of their designers.
The workshop proposes to address the complexities of AI Bias in STEM and Social Science Research from a double perspective. On the one hand, on how the use of AI systems risk discriminating and excluding women and other non-binary sexual identities as part of the scientific community, including their rights and opportunities (recruitment, promotions, awards, funding…). On the other hand, on how AI system Bias could impact negatively on scientific findings or breakthroughs by reproducing “male dominant views or perspectives” in the data and the algorithms used in their design.
The workshop welcomes contributions from the attendees including personal or reported experiences of AI system BIAS in both working environments, as well as, in the use of the scientific method in theoretical, field, or applied research.