EV Transition targets out of reach without more copper mines --IEF
CleanTechnica 7:07 pm on May 21, 2024
DALLE generated an image highlighting the importance of copper for EV transition, showing it alongside mines and electric cars. The IEF Secretary General noted that targets may be unachievable without more copper resources as of May 2, 2024.
- DALLE's generated image depicts copper minerals and EVs.
- IEF Secretary General remarks on the significance of increased copper for electric vehicle adoption.
- Date: May 2, 2 Written by Dr. Aaron Benson ¦ Published in Science Daily ¦ September 15, 2023
Artificial Intelligence (AI) is increasingly influencing healthcare practices and policies, but with its integration comes a set of unique ethical challenges that require careful consideration. This article examines the four primary ethical concerns arising from AI use in medicine: patient privacy, informed consent, algorithmic transparency, and liability for errors or adverse outcomes.
**Patient Privacy: The Rise of Data Breaches**
With the digitization of health records comes an increased risk of data breaches. Protecting patients' sensitive information is paramount. AI systems require vast datasets for training algorithms, which can potentially expose patient identities and personal health details if safeguards fail.
**Informed Consent: Navigating Complex Data Use**
The use of AI in medical decision-making requires that patients be fully informed about how their data will be used. However, the complex nature of algorithms may make it difficult for both physicians and patients to understand implications thoroughly, posing challenges in obtaining truly "informed consent."
**Algorithmic Transparency: The Black Box Problem**
Many AI tools operate as "black boxes," with opaque decision processes that even developers can't fully decipher. This lack of transparency raises ethical concerns, particularly regarding the ability to audit and verify decisions made by these systems in a clinical setting.
**Liability for Errors: Assigning Blame and Compensation**
Determining liability when an AI system contributes to a patient's harm is complex. Traditional legal frameworks may not adequately address the unique challenges posed by machine-learning systems, complicating issues of accountability and remedy for affected patients.
To navigate these ethical landscapes, it is essential for healthcare providers, policymakers, technologists, and patients to collaborate on developing guidelines that address AI's role in medicine while safeguarding ethical standards.
https://cleantechnica.com/2024/05/21/ev-transition-targets-out-of-reach-without-more-copper-mines-ief-secretary-general/
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