Mastering the Art of Questioning AI: The Super Skill
Discover the importance of asking the right questions to AI and learn actionable tips to maximize your results while ensuring accuracy and reliability.
Artificial intelligence (AI) has become an integral part of our daily lives, revolutionizing the way we work, communicate, and make decisions. It's also going to change how we work in the future, how we search for candidates, match their profiles with our roles, and even how we conduct interviews.
However, as we increasingly rely on AI-generated answers and solutions, it is crucial to recognize the importance of questioning the results we receive.
While AI can be an incredibly powerful tool, it's definitely not perfect. AI-generated answers may sometimes be inaccurate, biased, or even entirely fabricated. As a result, blindly trusting AI output can lead to misguided decisions, missed opportunities, and potential harm.
In the age of AI, the ability to ask the right questions and critically evaluate the answers we receive has become a vital "super skill" that sets individuals apart.
The Power of Questioning
As AI gets more advanced and becomes more prevalent in all parts of our lives, it's really important to ask why we should question what AI tells us.
Firstly, AI is not perfect. While AI algorithms can process vast amounts of data and generate responses quickly, they are not immune to errors, biases, and limitations. AI systems are only as good as the data they are trained on and the algorithms that power them. If the training data are biased or incomplete, the AI-generated answers may reflect those biases or lack important context.
For example, a study by Lambrecht and Tucker (2019) found that an AI system used for job advertising showed a preference for male candidates over female candidates, highlighting the potential for AI to perpetuate societal biases.1
Moreover, AI can sometimes generate answers that are entirely fabricated or nonsensical. This phenomenon, known as "hallucination," occurs when an AI system produces content that appears coherent but is not grounded in reality.
The consequences of blindly trusting AI can be significant. In high-stakes domains such as healthcare, finance, and legal services, relying on inaccurate or misleading AI-generated answers can lead to misdiagnoses, financial losses, and legal complications.
A study by Thakkar and Yadav (2021) found that an AI system used to predict patient outcomes in a hospital setting had a 20% error rate, highlighting the need for human oversight and questioning of AI results. 2
As recruiters and talent acquisition experts, questioning AI-generated answers is particularly crucial! AI tools are increasingly being used to screen resumes, conduct interviews, and make hiring decisions.
However, if left unchecked, these tools can perpetuate biases and overlook qualified candidates. The prime example is Amazon's AI system, which taught itself that male candidates were preferable. It penalized resumes that included the word "women's," as in "women's chess club captain."
By asking the right questions and critically evaluating AI-generated insights, recruiters can ensure that they are making fair and informed decisions that lead to the best outcomes for their organizations.
Crafting the Right Questions
To effectively harness the power of AI and ensure accurate and reliable results, it is essential to understand how to craft the right questions.
One of the key limitations of AI is its lack of common sense and contextual understanding. AI systems operate based on patterns and associations learned from training data, but they may struggle to grasp the nuances and complexities of real-world situations.
For example, an AI language model might generate a grammatically correct sentence that lacks logical coherence or relevance to the given context. To mitigate this limitation, it is crucial to provide AI with sufficient context and frame questions in a way that guides the system towards more accurate and relevant answers.
Research has shown that the way questions are formulated can significantly impact the quality of AI-generated responses. A study by Srinivasan et al. (2019) found that using specific and unambiguous language, providing relevant examples, and breaking down complex questions into smaller, more manageable parts can greatly improve the accuracy and coherence of AI-generated answers.3
Another important aspect of crafting effective questions is understanding the domain-specific requirements and constraints. In the context of recruitment and talent acquisition, this means considering factors such as job requirements, company culture, and diversity and inclusion goals.
By incorporating these factors into the questions asked, recruiters can guide AI systems towards generating more relevant and aligned answers. For example, instead of asking a generic question like "What are the characteristics of a good candidate?", a recruiter could ask, "What are the key skills and experiences required for a successful [job title] in our organization, considering our commitment to diversity and inclusion?"
Moreover, asking follow-up questions and engaging in a dialogue with AI systems can help refine and clarify the answers generated. By probing deeper, recruiters can uncover additional insights, identify potential biases or inconsistencies, and ensure that the AI-generated answers align with their specific needs and objectives. This iterative questioning process can lead to more accurate and actionable results.
Verifying AI-Generated Answers
Blindly trusting AI-generated answers can lead to misinformed decisions and potential adverse consequences. As such, fact-checking and cross-referencing should be an integral part of working with AI.
One effective way to verify AI-generated answers is to consult multiple sources (and AI tools like Gemini, Claude etc.) and compare the information provided. By cross-referencing the answers with reputable databases, industry publications, and expert opinions, recruiters can assess the accuracy and reliability of the AI-generated insights. This process helps identify any inconsistencies, errors, or biases that may have crept into the AI's output.
Collaborating with subject matter experts can also be a valuable strategy for ensuring the accuracy of AI-generated answers. Experts in the relevant domain can provide in-depth knowledge and insights that may not be captured by AI systems. For example, when using AI to analyze candidate profiles, recruiters can consult with experienced hiring managers or industry professionals to validate the AI's recommendations and ensure that they align with the company's specific requirements and culture.
Several tools and resources are available to aid in the verification process. Fact-checking websites, such as Snopes and PolitiFact, can be used to verify specific claims or statements generated by AI. Additionally, specialized AI auditing tools, like AI Fairness 360 and Aequitas, can help identify and mitigate biases in AI-generated results.4
By regularly auditing the AI's outputs and involving human recruiters in the verification process, any company could be able to identify and correct instances of bias, resulting in a more diverse and qualified candidate pool.
Developing a Critical Mindset
Developing a critical mindset is essential for navigating the complexities and challenges posed by AI-generated content. As AI systems become more sophisticated and widely adopted, recruiters and talent acquisition experts must cultivate a healthy skepticism towards AI-generated answers and maintain a proactive approach to staying informed about AI advancements.
Cultivating a critical mindset involves staying up-to-date with the latest developments in AI research and best practices. As AI technologies continue to evolve at a rapid pace, it is important for recruiters to continuously update their knowledge and skills to effectively leverage AI in their work.
By fostering a critical mindset, recruiters can enhance their ability to discern the strengths and weaknesses of AI systems and make more informed judgments when incorporating AI into their hiring processes.
Moreover, developing a critical mindset involves recognizing the importance of human oversight and collaboration in AI-driven decision-making. While AI can provide valuable insights and efficiency gains, it should not be viewed as a replacement for human expertise and judgment.
Top Three Actionable Tips for Asking AI Better Questions
Now that we have explored the importance of questioning AI, let me share with you three actionable tips that recruiters can implement to ask better questions and maximize the value of AI-generated insights:
1. Start with a clear objective in mind
Before engaging with an AI system, it is crucial to have a well-defined objective. Clearly articulating the purpose of your inquiry will help guide the formulation of your questions and ensure that the AI-generated answers are relevant and aligned with your goals.
For example, if your objective is to identify the most suitable candidates for a specific role, your questions should focus on the key skills, experiences, and qualifications required for that position.
2. Break down complex questions into smaller, more manageable parts
When dealing with complex or multi-faceted issues, it can be helpful to break down your questions into smaller, more focused components. By addressing one aspect of the problem at a time, you can guide the AI system towards providing more specific and actionable insights.
For instance, instead of asking a broad question like "What are the best strategies for improving diversity in our hiring process?", you could break it down into smaller questions such as "How can we attract a more diverse candidate pool?" or "What are some effective ways to mitigate bias in our resume screening process?"
3. Use specific and unambiguous language
To elicit accurate and relevant answers from AI systems, it is important to use clear and specific language in your questions. Avoid using vague or ambiguous terms that could be interpreted in multiple ways. Instead, opt for precise and well-defined terminology that leaves little room for misinterpretation.
For example, instead of asking, "What makes a good candidate?" you could ask, "What are the top five skills and experiences that have been shown to predict success in a [specific job role]?"
Want more tips and a peek at the three items I use daily? Check out the content behind the paywall. I'm sharing key insights on crafting your questions, plus the three tips I swear by every day.
Art of Questioning AI
The rise of AI has brought about a new era of opportunities and challenges for recruiters and talent acquisition experts. As AI systems become increasingly sophisticated and integrated into the hiring process, the ability to ask the right questions and critically evaluate AI-generated answers has emerged as a crucial "super skill" that sets successful professionals apart.
By understanding the limitations of AI, providing relevant context, and engaging in a dialogue with AI systems, recruiters can unlock the full potential of AI while ensuring the accuracy and reliability of the insights generated.
As we all move forward in the age of AI, it is essential for recruiters to embrace questioning as an indispensable skill. By developing a critical mindset, staying informed about AI advancements, and actively collaborating with AI systems, professionals can harness the power of AI to make more informed, fair, and successful hiring decisions.
The future of recruitment and talent acquisition lies in the effective partnership between human expertise and AI capabilities. By mastering the art of questioning and cultivating a critical mindset, recruiters can navigate the challenges and opportunities presented by AI, ultimately leading to better outcomes for their organizations and the candidates we all serve.
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The Best Structure of Questions to Ask AI (with Examples)
To maximize the effectiveness of your interactions with AI systems, it is essential to structure your questions in a way that elicits the most accurate, relevant, and actionable responses.
Here's what to keep in mind when crafting your questions, plus three tips I use daily: