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| Michael Gallagher |
Embracing transparency in AI
Transparency in AI involves clear communication about how AI systems work, the data they use and the decision-making processes they employ. This openness is essential not only for compliance with privacy regulations but also for fostering trust with customers and stakeholders.
Key aspects of AI transparency:
- Understandable Explanations: Providing user-friendly explanations of AI technologies and their applications, ensuring that information is accessible to non-technical audiences.
- Data usage clarity: Clearly outlining what data the AI systems collect, how this data is used and the basis for any decisions made by AI.
- Engagement and feedback: Creating channels for stakeholders to ask questions, provide feedback and understand more about AI deployments.
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Ensuring accountability in AI deployments
Accountability in AI refers to the mechanisms and practices that ensure businesses are answerable for the design, development and deployment of AI systems.
This includes taking responsibility for the outcomes of AI systems and addressing any issues that arise.
Strategies for AI accountability:
- Governance frameworks: Establishing robust internal governance structures that define roles, responsibilities and processes for AI oversight.
- Audit trails: Keeping detailed records of AI system development and deployment processes, including decision-making criteria, to facilitate audits and assessments.
- Impact assessments: Regularly conducting impact assessments to evaluate the effects of AI systems on privacy, ethics and human rights, and taking corrective actions as needed.
Case study: Financial services AI chatbot
Consider a financial institution that introduces an AI chatbot to provide customer services. Transparency is achieved by informing customers about how the chatbot generates responses and the type of data it collects during interactions. Accountability is maintained by implementing a governance framework that regularly reviews the chatbot's decisions for bias, inaccuracies or privacy concerns, ensuring that any issues are promptly addressed.
Implementing transparency and accountability
- Develop clear policies: Articulate clear policies and procedures for AI transparency and accountability, integrating these principles into the AI system lifecycle.
- Training and awareness: Educate staff and stakeholders on the importance of transparency and accountability in AI, ensuring they understand their roles in upholding these principles.
- Technology solutions: Leverage technology solutions that enhance transparency, such as explainable AI (XAI) tools, and establish mechanisms for monitoring and auditing AI systems.
Conclusion
Transparency and accountability are not just regulatory requirements; they are essential for building trust in AI technologies. By embracing these principles, businesses can navigate the complex landscape of AI development and deployment, ensuring that their innovations are both impactful and responsible. As AI continues to transform industries, the commitment to transparency and accountability will distinguish leaders in the field, fostering a future where AI technologies are trusted and valued by society.
Michael Gallagher is an associate in the business group at Cox & Palmer’s Halifax office. Email Gallagher at MGallagher@coxandpalmer.com.
The opinions expressed are those of the author(s) and do not necessarily reflect the views of the author’s firm, its clients, Law360 Canada, LexisNexis Canada or any of its or their respective affiliates. This article is for general information purposes and is not intended to be and should not be taken as legal advice.
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