ASHOK KUMAR GUPTAManager, Projects, Logistics, (Steel Manufacturing)| Arcelormittal Nippon Steel India LimitedSurat, India
As artificial intelligence systems become increasingly integrated into critical sectors like healthcare and finance, how can we ensure accountability and transparency in AI decision-making, particularly when algorithms exhibit biases or make errors that impact people's lives? Saving Changes...
To ensure accountability and transparency in AI decision-making, especially in critical sectors like healthcare and finance, we must implement robust oversight mechanisms, including regular audits, clear reporting standards, and diverse stakeholder involvement to identify and address biases and errors effectively.
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1 reply by ASHOK KUMAR GUPTA
Oct 17, 2024 3:17 AM
ASHOK KUMAR GUPTA
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Absolutely! Implementing regular audits, clear reporting standards, and involving diverse stakeholders are crucial steps to ensure accountability and transparency in AI, especially in critical sectors like healthcare and finance. This approach helps identify and address biases and errors effectively.
As with any use of automation, we need to ensure SMEs are engaged in validating the inputs and recommendations provided by the AI tools. Without those, we risk trusting something which is inherently untrustworthy.
Consultant| Canarys Automation LtdBangalore, Karnataka, India
Ensuring accountability and transparency in AI decision-making, especially in critical sectors like healthcare and finance, requires a multi-faceted approach.
First, establishing clear audit trails for AI decisions is essential. This means recording the data, algorithms, and rationale behind each decision so that human experts can review and validate outcomes. Second, regular bias assessments and testing should be mandatory, ensuring that the algorithms are fair and don't disproportionately impact specific groups.
Additionally, involving interdisciplinary teams (including ethicists, domain experts, and AI developers) can help mitigate risks. Finally, it's crucial to have a human-in-the-loop framework, where critical decisions can be reviewed by humans, especially when dealing with sensitive cases.
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1 reply by ASHOK KUMAR GUPTA
Oct 17, 2024 3:18 AM
ASHOK KUMAR GUPTA
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Agreed! Ensuring AI accountability and transparency in critical sectors like healthcare and finance requires clear audit trails, regular bias assessments, interdisciplinary teams, and a human-in-the-loop framework. These steps help validate outcomes, ensure fairness, and mitigate risks effectively.
Saving Changes...
Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
Ai is using in this sector from long time ago. I worked in that domain for 20 years. Some people do not understand that AI entities just create a result with probablities asociated to each one. Then, the final decision is ALWAYS in human being hands. "Human in the loop" is the key driver in AI since 40 years ago (and perhaps more than that, but I did not use AI before that). Saving Changes...
ASHOK KUMAR GUPTAManager, Projects, Logistics, (Steel Manufacturing)| Arcelormittal Nippon Steel India LimitedSurat, India
Oct 15, 2024 4:51 AM
Replying to Danny PMP, PgMP
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To ensure accountability and transparency in AI decision-making, especially in critical sectors like healthcare and finance, we must implement robust oversight mechanisms, including regular audits, clear reporting standards, and diverse stakeholder involvement to identify and address biases and errors effectively.
Absolutely! Implementing regular audits, clear reporting standards, and involving diverse stakeholders are crucial steps to ensure accountability and transparency in AI, especially in critical sectors like healthcare and finance. This approach helps identify and address biases and errors effectively. Saving Changes...
ASHOK KUMAR GUPTAManager, Projects, Logistics, (Steel Manufacturing)| Arcelormittal Nippon Steel India LimitedSurat, India
Oct 15, 2024 1:58 PM
Replying to Ashwin Kumar H M
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Ensuring accountability and transparency in AI decision-making, especially in critical sectors like healthcare and finance, requires a multi-faceted approach.
First, establishing clear audit trails for AI decisions is essential. This means recording the data, algorithms, and rationale behind each decision so that human experts can review and validate outcomes. Second, regular bias assessments and testing should be mandatory, ensuring that the algorithms are fair and don't disproportionately impact specific groups.
Additionally, involving interdisciplinary teams (including ethicists, domain experts, and AI developers) can help mitigate risks. Finally, it's crucial to have a human-in-the-loop framework, where critical decisions can be reviewed by humans, especially when dealing with sensitive cases.
Agreed! Ensuring AI accountability and transparency in critical sectors like healthcare and finance requires clear audit trails, regular bias assessments, interdisciplinary teams, and a human-in-the-loop framework. These steps help validate outcomes, ensure fairness, and mitigate risks effectively. Saving Changes...
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