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Home → All Courses → Law and Criminal Justice Courses → How to Direct AI for Personalized Advice That Prioritizes Your Best Interests Over General Safety → Flashcard

What strategies should an expert employ to ensure consistent alignment between evolving personal goals and dynamically changing AI advice over time?



Ensuring consistent alignment between evolving personal goals and dynamically changing AI advice over time is a continuous, iterative process that requires proactive engagement from the user. It’s not a set-it-and-forget-it scenario but rather a dynamic partnership where the user actively shapes the AI's understanding of their needs and adapts to the AI's evolving capabilities. Experts must employ a range of strategies to navigate this complex interaction and maintain alignment over time.

1. Regular Goal Re-Evaluation and Articulation:
Strategy: Experts should periodically reassess and clearly articulate their personal goals. This involves revisiting long-term aspirations, short-term objectives, and any changes in priorities. It’s essential to make sure that the goals themselves are clearly defined and not left vague.
Example: Instead of a generic goal like “Be more successful,” a user might re-evaluate and define specific milestones: “Achieve a leadership role within three years, while also publishing research articles in my field.” These newly articulated goals need to be clearly communicated to the AI system. Or, if the user decides to shift their career focus from one field to another, they must update the AI so it can adapt to the new direction.
Actionable Step: Schedule regular check-in points (e.g., monthly or quarterly) to reassess personal goals and update them in the AI system. The goals should be specific, measurable, achievable, relevant, and time-bound.

2. Explicit Communication of Value Shifts:
Strategy: Experts should be proactive in communicating any shifts or nuances in their core values. These values often serve as the foundation for goal-setting, and if they change, the AI system needs to adapt to the shift. The values themselves should be clear and specific.
Example: If a user previously prioritized financial security above all else but now places greater emphasis on social impact, that value shift needs to be communicated to the AI. It may also be a shift from valuing "productivity" to "creativity" or from "independence" to "collaboration". Or, if the user’s views on ethics or the environment have changed, the AI system needs to know so it can update its recommendations.
Actionable Step: Create a log of key values and update them with any changes. When introducing a new value, provide context on why it is important, and how it should be considered in future decision making.

3. Feedback Loop Integration:
Strategy: Establish a feedback loop where the expert actively provides feedback to the AI based on its recommendations, and the AI adjusts future responses accordingly. This is a crucial part of the iterative process, as it allows the user to not only get feedback but to also teach the AI.
Example: If an AI career advisor recommends networking events that don’t align with the user’s schedule, the user should provide feedback like, “The suggested events do not work with my current time availability; please take my calendar into account.” This feedback should teach the AI to adjust future recommendations. Or if the advice is helpful, the AI can be told the advice was "very helpful" and should prioritize similar advice for the future.
Actionable Step: Actively engage with the AI output and provide clear and specific feedback on how useful the advice is, or any aspect that was not helpful. The AI should adapt to each and every response.

4. Prompt Customization and Refinement:
Strategy: Experts should continuously refine their prompts to steer the AI toward more relevant and specific advice. This is an active and iterative approach to prompt engineering. It requires experimenting with different phrasing, keywords, and examples, all with the goal of eliciting more relevant output.
Example: If an AI travel planner suggests itineraries that don't match the user’s travel style, the user may need to modify their prompts to be more explicit about their preferences. For example, instead of saying "Plan a vacation", they may have to be more specific such as “Plan a two week vacation in Europe that prioritizes cultural sites and art museums, and has at least 3 days of hiking in the mountains". The more specific the prompt, the more precise the output.
Actionable Step: Regularly experiment with variations in the phrasing of the prompts, and continue to test which variations deliver the best output. If you see one prompt working better than other prompts, try to understand why that prompt worked better, and adapt that style for future prompts.

5. Parameter Adjustments (Where Applicable):
Strategy: Experts should learn about the various parameters, configurations, or settings that are provided by the AI system. These are often levers that can change the behavior of the AI. The user should learn which setting has what effect on the AI, so that they can be tuned for their specific use case.
Example: Some AI models provide settings for creativity, randomness, or the scope of the answer. Experts should experiment with adjusting those parameters, to understand how the output is being changed by different configurations.
Actionable Step: Investigate what controls are provided for the AI, and spend the time to learn how each setting or parameter changes the output. Keep adjusting them to achieve the desired output.

6. Regular Review and Alignment Checks:
Strategy: Schedule periodic reviews to ensure the AI’s advice continues to align with the user's goals and values. These reviews should assess not only the AI’s effectiveness, but also the direction that the advice is going, and whether that direction is still aligned with the user’s needs.
Example: A user might review all of the AI's recommendations over the past month, assessing if the AI was effective in meeting the needs of the user, and whether the system requires adjustments. If the AI is moving in a direction that is not ideal for the user, it’s vital to course correct.
Actionable Step: Set aside time to do a monthly or quarterly review of all past advice, and then communicate the findings to the AI, so it can align the direction of the output to the user's needs.

7. Active Monitoring for Bias and Drift:
Strategy: Experts should constantly monitor AI recommendations for any signs of bias, drift, or unintended shifts in advice. This requires active participation in the review and analysis of every output from the AI system.
Example: If the AI starts giving advice that promotes some types of business and ignores others, that could be a sign of bias, and it should be addressed. Or if the AI suddenly starts changing its risk recommendations without reason, that is a sign of drift that should be investigated.
Actionable Step: Always keep a critical eye on all the outputs, looking out for any biases, deviations, or unexpected shifts in the direction of the recommendations. As soon as you notice a problem, address it immediately.

8. Prioritization of User Agency:
Strategy: Experts should always remember that they are in charge of their decision-making process, and the AI system is just a tool. The user must always be in control, and the AI is there to support and augment their decision-making capabilities. The user should be capable of overriding any of the AI’s suggestions when needed.
Example: While the AI might recommend one course of action, the user should always have the ability to override the recommendation and make decisions that better align with their personal values.
Actionable Step: Establish clear boundaries for what the AI can do, and always remember that the user is the final authority, not the AI system.

9. Continuous Learning and Adaptation:
Strategy: Experts must also stay informed about the latest advancements in AI technology, as well as the new features and tools that are provided in their chosen AI system. This is vital to maximizing the effectiveness of the AI system and for remaining current with the capabilities of the AI.
Example: If new options, configurations, or features are added to the AI system, it’s important to understand them and to incorporate them into one's workflow. It's also important to stay informed of how the AI is being trained and updated.
Actionable Step: Always remain informed of any new features, and spend the time to learn about how the AI system is constantly improving over time. Experiment with new features and incorporate them when beneficial.

In summary, ensuring the ongoing alignment between evolving goals and dynamic AI advice requires a proactive approach. Experts must not only be explicit about their values and aspirations but also active in their communication with the AI system. It's also essential to establish feedback loops, customizable prompts, parameter settings, regular reviews, bias monitoring, and a user-first approach. By employing these strategies, experts can navigate the complexities of AI interactions, ensuring that AI remains a valuable partner in their personal and professional growth. It should be viewed as a collaboration between the user and the system, with the user firmly in control of the process.