opacity
See also the connection with explainability
A Data Ethical Consultation on how parliamentary enquire are done - Digital Assistant for Parliamentary Inquiries - highlighted concerns about opacity and the need for transparency as a matter of governance.
- de-centering of the state is a major challenge here, in that the complexity and diffusion of procedures of data storage, data collection, data processing, and data sharing within the public administrative system makes it arguably harder than ever to provide explainability
#author/Anderson
The following is a proof-of-concept, using the "Vault Chat" plugin, applied to this set of notes.
Based on your notes, opacity refers to the inscrutability or lack of transparency in machine decisions made by AI systems. Some ethical concerns related to opacity include:
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Lack of accountability: Opacity makes it difficult to hold AI systems and their developers accountable for the decisions they make. Without understanding the underlying logic and decision-making process, it is challenging to identify and address biases, errors, or discriminatory practices.
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Unfair Treatment: When AI systems are opaque, it can result in unfair treatment of individuals or groups. If decision-making algorithms are biased or discriminatory, it can lead to unjust outcomes such as discrimination in hiring, lending, or criminal justice. fairness
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Lack of trust: Opacity erodes trust in AI systems. When the decision-making process is not transparent and understandable, it becomes challenging for individuals to trust the outcomes. This can lead to skepticism, resistance, and potential rejection of AI systems, hindering their adoption and potential benefits.
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Ethical diligence: Opacity poses challenges to conducting ethical due diligence on AI systems. Organizations and regulators need to ensure that the systems are fair, unbiased, and adhere to ethical principles. Without transparency, it becomes challenging to assess and mitigate any potential ethical risks.
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Autonomy and Agency: Opacity can impact individual autonomy and agency by limiting the ability to challenge or contest decisions made by AI systems. Individuals may feel powerless or unable to understand or question the processes that influence their lives.
Addressing opacity and promoting explainability in AI systems is crucial for mitigating these ethical concerns and ensuring that AI is used in a fair, transparent, and accountable manner.
connects this to explainability & inscrutability