The societal impact of artificial intelligence (AI) is profound, as it reflects human values and biases rather than malfunctioning.

Recognizing that AI mirrors human character encourages more principled decision-making as we integrate these technologies into our lives.

To responsibly shape AI's role in society, organizations must evaluate the data and assumptions behind AI models, rather than solely focusing on economic efficiency.

As AI technology evolves, individual biases will increasingly influence how these systems operate, particularly with adaptive AI-powered robots.

Current societal contradictions regarding AI include valuing its efficiency while fearing job loss, and expressing privacy concerns while willingly sharing personal data.

Research from UC Berkeley indicates that mortgage algorithms often disadvantage Black and Hispanic applicants, perpetuating systemic inequalities in lending.

Predictive policing tools and healthcare algorithms exhibit similar biases, often disproportionately affecting marginalized communities.

Automated grading systems in education have shown favoritism towards wealthier students, reinforcing existing disparities.

Examples such as Amazon's AI hiring tool, which was scrapped due to bias against women, highlight how training on historical data can perpetuate existing inequalities.

Facial recognition technology struggles with identifying darker skin tones due to biases in the training data, illustrating how AI replicates human prejudices.

Healthcare algorithms may misdiagnose patients from certain demographic groups, further illustrating the risks of biased AI systems.

AI's mirroring effect offers an opportunity for self-reflection on the biases embedded in our data and decision-making processes.