Executive Summary and Main Points:
The latest evidence shows that even though Facebook has announced changes to its ad targetting tools to prevent discrimination, its algorithm still shows bias. This bias in machine learning can have significant impacts on people’s access to jobs, housing, and other opportunities.
This is a known issue with recommendation algorithms, and while there are technical fixes being pursued, policymakers will need to play a greater role in implementing them.
Potential Impact in the Education Sector:
The potential impact of biased algorithms in education could have severe consequences for students, particularly in areas such as admissions, financial aid, and job placement. This could lead to unequal opportunities and further widen the achievement gap.
Potential Applicability in the Education Sector:
The use of AI and digital tools in education could help address this issue by identifying and addressing bias in the learning environment. By incorporating ethical and inclusive principles in the development and implementation of these technologies, education systems can create fairer and more equitable learning environments for all students.
Criticism and Potential Shortfalls:
As seen in the case of Facebook’s ad algorithm, the potential for bias and discrimination in AI and digital tools is a pressing concern. Other examples, such as the use of facial recognition technology in identifying student behavior, also raise ethical and cultural implications. Without proper oversight and consideration for ethical principles, these technologies could do more harm than good.
Actionable Recommendations:
Educational institutions and leaders should prioritize diversity and inclusivity in the use of AI and digital tools in their systems. This includes ensuring a diverse development team, regularly evaluating algorithms for bias, and promoting transparency and ethical principles in their use. Additionally, international partnerships and collaborations can provide a global perspective and help identify areas where bias and discrimination may exist.
Source article: https://mittr-frontend-prod.herokuapp.com/s/613274/facebook-algorithm-discriminates-ai-bias/