Beyond the Past and Present: Integrating the Potential for Individual and Societal Growth and Progress in Machine Learning
As a software engineer, I have primarily worked on frontend and backend development and integrations. However, my recent experience working with data structures and algorithms has caused me to become more aware of the “black box” nature and the potential for bias within machine learning systems. While past data can help predict future trends, I believe that we should also consider the potential for growth and development to be a weighted consideration in our models to achieve a more humanistic and progressive society, rather than solely relying on past and present information.
For example, in the context of hiring, a model that takes into account an individual’s potential for growth and development, along with their past experiences, can be more effective in predicting success in a role than a model that only relies on past experience. Such a model could evaluate candidates based on their soft skills, personality traits, and demonstrated willingness to learn and adapt to new challenges. Additionally, the model could account for external factors, such as access to training and support, that could help unlock an individual’s potential.
Thankfully, there are many voices pushing for regulation in the realm of machine learning, people like Catherine (“Cathy”) Helen O’Neil with her Weapons of Math Destruction, Joy Buolamwini with her beautiful poet of code, Big Brother Watch in the UK, Timnit Gebru, Kate Crawford, Tim O’Reilly, Andrew Ng and a host of more brave people, which is encouraging. To ensure fairness and equality in these systems, we must take a proactive approach. This includes collecting diverse data, regularly auditing the models for bias, providing transparency, implementing human oversight, testing for fairness and bias, and involving minority groups in the design and testing process.
By committing to these efforts, we can create a better future for all. If you’ve not seen the documentary; Coded Bias on Netflix, check it out.
#MachineLearning #Fairness #Transparency #AIethics #DataBias #DiversityInTech #TechRegulation #HumanisticTechnology #TechProgress #SocialJustice
