ClassX Video Lessons Subjects Artificial Intelligence Can AI predict someone’s breakup? – Thomas Hofweber
Imagine you’re in a loving relationship that’s been strong for years, and you’re thinking about getting engaged. Your partner is excited about the idea, but you can’t ignore the statistics. Many marriages end in divorce, and over 10% of first marriages end within the first five years. This makes you wonder if getting married might be a mistake if it wouldn’t last.
In the near future, a new company has developed an AI-based model that claims to predict the likelihood of divorce. This AI analyzes data from social media activity, online searches, spending habits, and marriage and divorce histories. With this information, it predicts whether a couple will divorce within the first five years of marriage with 95% accuracy. However, the AI doesn’t explain its predictions; it simply states whether you will or won’t divorce.
So, should you base your decision to marry on this AI’s prediction? If the AI predicts that you and your partner will divorce within five years, you have three options: get married anyway and hope the prediction is wrong, break up now without knowing if ending a happy relationship is the right choice, or stay together without marrying, hoping that marriage itself is the issue. Without understanding the reasons behind the prediction, you can’t know if the predicted issues will still affect your relationship.
This uncertainty highlights a common issue with AI: explainability and transparency. Many predictive models, like those used to assess loan repayment likelihood or parole decisions, face similar challenges. Without understanding why AI makes certain predictions, it’s difficult to critically evaluate its advice.
The lack of transparency also affects accountability. If you break up with your partner based on the AI’s prediction, how would you explain your decision? Ending a happy relationship because a machine predicted its end seems unfair. While we don’t always owe explanations for our actions, AI’s opacity can create ethical dilemmas.
Outsourcing decisions to AI involves trade-offs. If you trust the AI’s accuracy, you might not care why it predicts a breakup, only that it does. However, if you value authenticity, you’ll want to understand the reasons behind a potential divorce before deciding.
Authentic decision-making is crucial for accountability and might help you challenge the prediction. However, the AI might have already considered your attempts to defy it, potentially setting you up for failure. While 95% accuracy is impressive, it’s not perfect—1 in 20 couples might receive a false prediction. As more people use the service, self-fulfilling prophecies could artificially maintain or increase the AI’s success rate.
Ultimately, whether to seek the AI’s prediction is your choice. Regardless of what the AI might say, the decision to marry remains a deeply personal one, influenced by more than just data and predictions.
Engage in a structured debate with your classmates on the topic: “Should AI predictions influence personal decisions such as marriage?” Take turns arguing for and against the proposition, considering the ethical, emotional, and practical implications discussed in the article.
Analyze a hypothetical case where a couple receives a prediction from the AI. Discuss in groups how they might respond to the prediction, considering factors like explainability, accountability, and personal values. Present your group’s conclusions to the class.
Participate in a workshop focused on AI transparency. Work with your peers to brainstorm ways to improve the explainability of AI models. Consider how these improvements could affect user trust and decision-making in scenarios like the one described in the article.
Engage in a role-playing exercise where you take on the roles of different stakeholders affected by AI predictions (e.g., the couple, AI developers, ethicists). Discuss the potential impacts of AI predictions on each stakeholder and explore possible solutions to address their concerns.
Conduct a research project exploring the impact of AI predictions on relationships. Investigate real-world examples, gather data, and analyze how AI is currently being used in personal decision-making. Present your findings in a report or presentation to the class.
You and your partner have been in a strong, loving relationship for years, and lately, you’re considering getting engaged. Your partner is enthusiastic about the idea, but you can’t shake off the statistics. Many marriages end in divorce, often not amicably. Over 10% of couples in their first marriage get divorced within the first five years. If your marriage wouldn’t even last five years, you feel like getting married would be a mistake.
In the near future, a new company has released an AI-based model that can predict your likelihood of divorce. This model is trained on data sets containing individuals’ social media activity, online search histories, spending habits, and history of marriage and divorce. Using this information, the AI can predict if a couple will divorce within the first five years of marriage with 95% accuracy. The only catch is that the model doesn’t provide reasons for its results—it simply predicts whether you will or won’t divorce without explanation.
So, should you decide whether or not to get married based on this AI’s prediction? Suppose the model predicts you and your partner would divorce within five years of getting married. At this point, you’d have three options: You could get married anyway and hope the prediction is wrong; you could break up now, though there’s no way to know if ending your currently happy relationship would cause more harm than letting the prediction run its course; or you could stay together and remain unmarried, on the off-chance that marriage itself would be the problem. However, without understanding the reasons for your predicted divorce, you’d never know if those issues would still emerge to affect your relationship.
The uncertainty surrounding these options stems from a well-known issue with AI regarding explainability and transparency. This problem affects many potentially useful predictive models, such as those predicting which bank customers are most likely to repay a loan or which individuals are most likely to reoffend if granted parole. Without knowing why AI systems reach their decisions, many worry we can’t think critically about how to follow their advice.
The transparency problem doesn’t just prevent us from understanding these models; it also impacts user accountability. For example, if the AI’s prediction led you to break up with your partner, what explanation could you reasonably offer them? That you want to end your happy relationship because a machine predicted its demise? That hardly seems fair. While we don’t always owe people an explanation for our actions, when we do, AI’s lack of transparency can create ethically challenging situations.
Accountability is just one of the trade-offs we make by outsourcing important decisions to AI. If you’re comfortable deferring your agency to an AI model, it’s likely because you’re focused on the accuracy of the prediction. In this mindset, it doesn’t really matter why you and your partner might break up—only that you likely will. However, if you prioritize authenticity over accuracy, then you’ll need to understand and appreciate the reasons for your potential divorce before making any decisions.
Authentic decision-making is essential for maintaining accountability, and it might be your best chance to prove the prediction wrong. On the other hand, it’s also possible the model has already accounted for your attempts to defy it, and you could be setting yourself up for failure. While 95% accuracy is high, it’s not perfect—this means 1 in 20 couples will receive a false prediction. As more people use this service, the likelihood increases that someone predicted to divorce will do so simply because the AI predicted it. If this happens to enough newlyweds, the AI’s success rate could be artificially maintained or even increased by these self-fulfilling predictions.
Ultimately, regardless of what the AI might tell you, whether you even ask for its prediction is still up to you.
Critical – Involving careful judgment or evaluation, especially in identifying the strengths and weaknesses of a concept or argument. – In the field of artificial intelligence, critical analysis is essential to assess the ethical implications of deploying autonomous systems.
Thinking – The process of using one’s mind to consider or reason about something, often involving problem-solving or decision-making. – Effective thinking is crucial when developing algorithms that can adapt to new data inputs in machine learning.
Artificial – Made or produced by human beings rather than occurring naturally, often referring to systems or processes that simulate human intelligence. – Artificial neural networks are designed to mimic the way the human brain processes information.
Intelligence – The ability to acquire and apply knowledge and skills, often used in the context of machines that can perform tasks that typically require human intelligence. – The development of artificial intelligence has revolutionized industries by enabling machines to perform complex tasks efficiently.
Explainability – The degree to which a human can understand the cause of a decision made by an AI system. – Explainability is a critical factor in ensuring trust in AI systems, especially in high-stakes environments like healthcare.
Transparency – The quality of being easily seen through or understood, often referring to the openness and clarity of processes or systems. – Transparency in AI algorithms is necessary to ensure that stakeholders can trust the outcomes produced by these systems.
Accountability – The obligation to accept responsibility for one’s actions, particularly in the context of AI systems and their impact on society. – Developers must ensure accountability in AI systems to address any unintended consequences that may arise from their deployment.
Predictions – Forecasts or estimations about future events, often generated by analyzing data patterns using AI models. – Accurate predictions made by AI can significantly enhance decision-making processes in various sectors, including finance and healthcare.
Decision-making – The process of making choices or reaching conclusions, especially when involving complex data analysis and AI systems. – AI-driven decision-making can optimize resource allocation in supply chain management by analyzing real-time data.
Authenticity – The quality of being genuine or real, often discussed in the context of ensuring that AI-generated content is trustworthy and reliable. – Ensuring the authenticity of AI-generated news articles is crucial to prevent the spread of misinformation.
