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4 key moves for embedding ethical AI in businessÂ
Leaders should make sure they (1) translate, (2) integrate, (3) calibrate and (4) proliferate responsible AI practices in business operations. Â
Technical and non-technical leaders have important roles to play in the implementation of responsible artificial intelligence (AI) practices. After engaging with organizations across various industries at all stages of implementation, here are four key moves we have identified for leaders:
1. Translate
Translate high-level principles, as seen in many templates for ethical charters on the use of AI, into practical guidance that is clear and helpful.
- Facilitate alignment by keeping teams together. Make sure your governance structure supports collaboration between compliance and digital teams. Explain the importance of working closely together on responsible AI goals.
- Facilitate open dialogue where members of many different teams can share their input for problem-solving.
2. Integrate
Integrate ethical considerations into your AI design and development processes.
- Adapt AI tools and frameworks to adhere to global standards while also remaining operationally relevant to your companyâs unique context.
- Provide ongoing support to answer questions as they arise. At the same time, periodic audits and/or other oversight mechanisms can help ensure ongoing adherence to ethical standards.
âTranslate high-level principles, as seen in many templates for ethical charters on the use of AI, into practical guidance that is clear and helpful.â
3. Calibrate
Calibrate AI solutions in response to local conditions and changing technologies to keep them relevant to real-world applications.
- Sustain momentum and align ethical considerations with business objectives for synergy.
- Leverage external partnerships, such as technical experts, academic institutions, and industry groups, for fresh perspectives and insights to optimally adjust AI solutions.
4. Proliferate
Share practices and learnings with the rest of the organization. Upskill your workforce on AI developments.
- Prioritize training for key stakeholders first when resources are limited. Conducting a stakeholder analysis can help you decide who needs to receive AI ethics training before it can be rolled out widely.
- Scale up with practical toolkits and other resources. Consider an AI scaling toolkit that defines a core set of practices for implementation, communication, and more that can be adapted to meet local needs.
Further reading:
All views expressed herein are those of the author and have been specifically developed and published in accordance with the principles of academic freedom. As such, such views are not necessarily held or endorsed by TONOMUS or its affiliates.
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