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Gender itself and related biases are a constant discussion topic in today’s world. “Keyboard-warriors” equipped only with their wit and internet connection discuss online, tech giants start mentorship programs for underrepresented genders and provide unconscious bias training, only to fail all the same and often causing further deterioration (Wynn, 2019). Theoretically speaking the solution is simple: Be more tolerant towards others, be forgiving, be reflective of your actions and certainly accept that the world is not perfect nor completely equal and will never be. Still, fighting for improvements – whether for instance it is men’s rights in child custody law suits or equal opportunities for women in terms of career is essential. But how does this striving for a better and fairer future in terms of gender biases translate into the digital age, dominated by ever-stronger algorithms and AI?

Algorithms will determine our future and will embed themselves into our ever-day interactions more and more. Hence, it is important to design them in an inclusive and fair manner because even though they are smart, most times they still cannot think on their own. In the end, “machine learning systems are, what they eat” (Maroti, 2019). A prominent example was the Microsoft chatbot Tay which drifted into uttering nazi-language and inappropriateness rather quickly. Or why is it that many virtual personal assistants are female? (UNESCO & EQUALS, 2019). Algorithms and AI restate and copy our reality. What if this for instance influences your ability to get a bank loan (Smith, 2019) or pass the gate of an automated CV checking program?

How do we change the underlying mechanisms of algorithms without leading to discrimination on the other side of the respective spectrum? In the end, equality of opportunity should be the goal, not new inequality. Possible approaches are numerous and should be discussed thoroughly. Below you may find a few collected ideas potentially contributing to fairer AI although no solution will solve the issue at hand alone:
1) Enable more women into the MINT field. Use trainings, governmental and supra-governmental policies such as UN initiatives to equalise the playing field (Deva, 2020). Here it is important to mention that, in my mind, policies should be designed to enable a more common and fair foundation on which to compete and strive for greatness, not quotas
2) Enrich and adjust data sets. Maroti (2019), states that for instance using the same data sets twice with swapped genders can diminish potential bias in natural language processing (NLP). Therefore, optimizing data sets or using additional meta data can be a valid point as long as it does not lead to other biases or discrimination and still reflects the original data accurately
3) Collect relatively unbiased datasets. Instead of changing data at a subsequent step, choosing representative and diverse data sets can significantly improve outcome and therefore improve fairness (Feast, 2019).

As can be seen, creating a less biased, fairer digital world is possible and should therefore be pursued relentlessly! One underlying food for thought is, however, whether group conflict and oversimplification are the appropriate approaches to tackle future problems or whether we should see and reflect on ourselves for what we are – human individuals with biases.

 

 

 

 

 

References:
Deva, S, 2020, Addressing the gender bias in artificial intelligence and automation. Retrieved from: https://www.openglobalrights.org/addressing-gender-bias-in-artificial-intelligence-and-automation/

Feast, J, 2019, 4 Ways to address gender bias in Ai, Havard Business Review. Retrieved from: https://hbr.org/2019/11/4-ways-to-address-gender-bias-in-ai

Maroti, C, 2019, Gender bias in AI: building fairer algorithms, Retrieved from: https://unbabel.com/blog/gender-bias-artificial-intelligence/

Smith, C.S, 2019. Dealing with bias in artificial intelligence. New York Times. Retrieved from: https://www.nytimes.com/2019/11/19/technology/artificial-intelligence-bias.html

UNESCO & EQUALS, 2019, I’d blush if I could – Closing gender divides in digital skills through education. Retrieved from: https://unesdoc.unesco.org/ark:/48223/pf0000367416.page=1

Wynn, A, 2019, Why Tech’s Approach to Fixing its gender equality isn’t working. Havard Business Review. Retrieved from: https://hbr.org/2019/10/why-techs-approach-to-fixing-its-gender-inequality-isnt-working