Ethical and Technical Implications of Self-Supervised Learning in Autonomous Decision-Making Systems
Keywords:
Self-Supervised Learning, Autonomous Systems, Ethical Implications, Technical Challenges, Machine Learning, Decision-Making, Bias, Interpretability, AccountabilityAbstract
Purpose
This paper investigates the ethical and technical dimensions of applying self-supervised learning (SSL) in autonomous decision-making systems. As SSL becomes foundational in sectors like transportation, healthcare, and security, it is critical to examine the societal implications and technical robustness of these models.
Design/methodology/approach
The study conducts a conceptual analysis of existing literature and emerging use cases of SSL, identifying core ethical concerns (bias, accountability, transparency) and technical challenges (robustness, interpretability, data security) associated with deploying SSL in high-stakes autonomous systems.
Findings
The research highlights that while SSL holds significant potential for reducing dependence on labeled data, its deployment without rigorous oversight could reinforce systemic biases, hinder transparency, and introduce unpredictable model behaviors. Technical vulnerabilities such as lack of interpretability and exposure to adversarial risks further complicate its adoption.
Practical implications
To mitigate risks, the paper calls for the establishment of ethical guidelines, technical standards, and regulatory frameworks tailored to SSL in autonomous systems. These measures are vital for ensuring that the integration of SSL aligns with societal values and safety requirements.
Originality/value
This paper offers a timely and holistic examination of SSL's dual impact—its capacity to transform autonomous systems and the ethical-technical dilemmas it introduces. It contributes to ongoing debates by proposing a structured path forward for responsible innovation in machine learning.
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