What are the most effective ways to manage risks with artificial intelligence and machine learning?

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Artificial intelligence (AI) and machine learning (ML) are powerful technologies that can create new opportunities, enhance productivity, and solve complex problems. However, they also pose significant risks, such as ethical, legal, security, and social implications, that need to be carefully managed and mitigated. In this article, we will explore some of the most effective ways to manage risks with AI and ML, based on the best practices and standards for technological innovation.

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1 Identify and assess risks

The first step to manage risks with AI and ML is to identify and assess the potential sources, impacts, and likelihood of risks, using a systematic and comprehensive approach. This may involve conducting a risk analysis, a stakeholder analysis, a data quality assessment, a model validation, and a scenario planning. The goal is to understand the nature, scope, and severity of the risks, as well as the trade-offs and uncertainties involved.

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Keyur Anandjiwala

Director at Deloitte Haskins & Sells LLP
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Each century has come up with different challenges and human kind has so far evolved over the period of time with change in rules and regulations. Currently the unique challenge is to control the uncontrollable AI and ML, which can be achieved through setting up a separate regulator for monitoring the use of AI and ML. For setting up the framework, huge data needs to be collated for understanding the threats of each category of tools. Telecom companies can play a pivotal role in restricting the spread of harmful platforms. Public awareness on restricting social platforms to be spread positively.

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Hemang Davé

Strategic Thinker & Innovator | Servant-Leader | Keynote Speaker | American Dream
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Before you can identify and assess risks, you will need to set up a governance structure which creates "rules of engagement." This in turn allows your organization to identify and assess risks. These rules should be thorough but easy to understand, and more importantly, easy to enforce. Rules can address data governance, choice of technology, skillsets, legal and many more.

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2 Implement risk controls

The second step to manage risks with AI and ML is to implement risk controls, which are measures or actions that aim to reduce, avoid, or transfer risks. This may involve applying ethical principles and guidelines, designing and testing robust and secure systems, ensuring data privacy and protection, developing and enforcing policies and regulations, and establishing accountability and transparency mechanisms. The goal is to ensure that the AI and ML systems are aligned with the objectives, values, and norms of the stakeholders and the society.

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3 Monitor and review risks

The third step to manage risks with AI and ML is to monitor and review the risks, which are processes that aim to track, measure, and evaluate the performance and outcomes of the risk controls. This may involve collecting and analyzing data and feedback, conducting audits and inspections, performing quality assurance and improvement, and updating and revising the risk analysis and controls. The goal is to identify and address any gaps, issues, or changes that may affect the risk profile or the risk management strategy.

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4 Learn and adapt

The fourth step to manage risks with AI and ML is to learn and adapt, which are activities that aim to enhance the knowledge and skills of the stakeholders and the system, and to adjust the risk management strategy accordingly. This may involve providing and receiving training and education, sharing and disseminating best practices and lessons learned, fostering a culture of innovation and learning, and incorporating new insights and developments. The goal is to improve the effectiveness and efficiency of the risk management process, and to foster a continuous improvement cycle.

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5 Engage and communicate

The fifth step to manage risks with AI and ML is to engage and communicate, which are interactions that aim to involve and inform the relevant stakeholders and the public about the risks and the risk management strategy. This may involve establishing and maintaining dialogue and collaboration, soliciting and addressing feedback and concerns, disclosing and reporting information and results, and building and maintaining trust and confidence. The goal is to ensure that the risk management strategy is responsive, inclusive, and transparent, and that the stakeholders and the public are aware, informed, and empowered.

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6 Evaluate and improve

The sixth step to manage risks with AI and ML is to evaluate and improve, which are assessments that aim to measure and enhance the impact and value of the AI and ML systems and the risk management strategy. This may involve defining and applying indicators and metrics, conducting and reviewing evaluations and assessments, identifying and implementing improvements and recommendations, and celebrating and rewarding achievements and successes. The goal is to ensure that the AI and ML systems and the risk management strategy are delivering the desired outcomes and benefits, and that they are aligned with the vision and mission of the stakeholders and the society.

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Hemang Davé

Strategic Thinker & Innovator | Servant-Leader | Keynote Speaker | American Dream
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One of the most important aspects of AI/ML is over time these models lose their efficacy. This happens as data sources, data noise, data exhaust and business requirements change over time. You should set up, as a part of governing body, criteria to evaluate efficacy of AI/ML for your organizational needs.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Hemang Davé

Strategic Thinker & Innovator | Servant-Leader | Keynote Speaker | American Dream
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AI/ML, just like any tech, will continue to evolve. There is no such thing as one and done. Organizations will need to be dynamic and proactive in proper use and implementation of AI/ML and associated risks. This requires agile and iterative approach to properly manage technology usage.

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What are the most effective ways to manage risks with artificial intelligence and machine learning?

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3

4

5

6

7

1 Identify and assess risks

2 Implement risk controls

3 Monitor and review risks

4 Learn and adapt

5 Engage and communicate

6 Evaluate and improve

7 Here’s what else to consider

Technological Innovation

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What are the most effective ways to manage risks with artificial intelligence and machine learning?

1

2

3

4

5

6

7

1 Identify and assess risks

2 Implement risk controls

3 Monitor and review risks

4 Learn and adapt

5 Engage and communicate

6 Evaluate and improve

7 Here’s what else to consider

Technological Innovation

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