The Evolving Landscape of AI Regulations in the US: Challenges, best practices and implementing effective AI Governance strategies

Emerging regulatory framework for AI in the US

Ketaki Joshi

May 12, 2023

As AI and automation technology mature, the need for inherently interpretable, explainable and responsible models has become the critical focus. While this development is being encouraged, there has been an increased emphasis on managing associated risks with these technologies. The AI/ ML regulatory landscape in US is changing rapidly; it has become imperative for organizations to make requisite tweaks in their business processes and explain to regulators how their system works to demonstrate compliance with applicable regulations.

The US government has geared up its ongoing efforts on ‘Responsible AI’ and emphasise the importance of driving responsible, trustworthy, and ethical innovation with safeguards that mitigate risks and potential harms to individuals and society. In May 2023, the Biden-⁠Harris Administration announced new actions that will further promote responsible American innovation in artificial intelligence (AI) and protect people’s rights and safety. 

While there has been palpable excitement around Responsible AI and AI Governance, it is all still in the conceptual phase. Achieving AI governance will help organizations manage AI risk and scale while complying with the growing AI regulations. 

In this whitepaper, we summarize the emerging regulatory framework for AI in the US and propose concrete steps companies can take to comply with such regulations.

Contents:
  • Introduction and components required for ‘AI Governance’
  • Designing an AI Governance framework:

           - Reliability

         -  Fairness and Bias

          - ML Explainability

          - Algorithmic Auditing

          - Data Privacy

         - Responsible AI

         - AI usage risk

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The Evolving Landscape of AI Regulations in the US: Challenges, best practices and implementing effective AI Governance strategies

Whitepaper

By

Ketaki Joshi

May 12, 2023

AI Policies

As AI and automation technology mature, the need for inherently interpretable, explainable and responsible models has become the critical focus. While this development is being encouraged, there has been an increased emphasis on managing associated risks with these technologies. The AI/ ML regulatory landscape in US is changing rapidly; it has become imperative for organizations to make requisite tweaks in their business processes and explain to regulators how their system works to demonstrate compliance with applicable regulations.

The US government has geared up its ongoing efforts on ‘Responsible AI’ and emphasise the importance of driving responsible, trustworthy, and ethical innovation with safeguards that mitigate risks and potential harms to individuals and society. In May 2023, the Biden-⁠Harris Administration announced new actions that will further promote responsible American innovation in artificial intelligence (AI) and protect people’s rights and safety. 

While there has been palpable excitement around Responsible AI and AI Governance, it is all still in the conceptual phase. Achieving AI governance will help organizations manage AI risk and scale while complying with the growing AI regulations. 

In this whitepaper, we summarize the emerging regulatory framework for AI in the US and propose concrete steps companies can take to comply with such regulations.

Contents:
  • Introduction and components required for ‘AI Governance’
  • Designing an AI Governance framework:

           - Reliability

         -  Fairness and Bias

          - ML Explainability

          - Algorithmic Auditing

          - Data Privacy

         - Responsible AI

         - AI usage risk

Complete this form to download

Thank you! Your submission has been received!
Thank you for your interest!

Your Documents is ready to download. please click below to initiate the download.

If you are unable to download the document or have same query, please contact us on

Oops! Something went wrong while submitting the form.

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ML Observability

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