Revisiting big data optimism: risks of data-driven black box algorithms for society

Area of Science:

• Computer Science
• Sociology
• Public Policy

Background:

• Big data algorithms and AI are increasingly used across science, society, and public policy.
• These technologies aim to improve efficiency but often fall short of ensuring fairness or empowerment.
• Issues like bias, measurement error, and over-reliance on prediction can lead to unfair and opaque outcomes.

Purpose of the Study:

• To critically examine the ethical risks and societal side effects of big data and AI.
• To advocate for a shift from short-term optimization to systemic resilience and participatory oversight.
• To propose pathways for responsible innovation in data-driven technologies.

Main Methods:

• Critical analysis of the application of big data algorithms and AI.
• Examination of ethical considerations, including bias, fairness, and transparency.
• Exploration of socio-economic impacts and power dynamics.

Main Results:

• Big data and AI implementation can exacerbate existing inequalities and introduce new biases.
• Automated decision-making may replace human judgment, leading to reduced fairness and transparency.
• The pursuit of pure optimization overlooks crucial ethical risks and societal consequences.

Conclusions:

• Responsible innovation in big data and AI necessitates a focus on ethical risks and societal side effects.
• A reorientation towards "systemic resilience" and "participatory oversight" is crucial.
• Integrating complexity science with constitutional and cultural values can foster symbiotic human-technology relationships.